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A method and apparatus is provided to determine affine disparity and affine invariant pattern distance from two image patterns. The method formulates the problem of determining affine disparity and affine invariant distances between two image patterns as one of finding an affine transform for an adjustable hypercolumn (HC) reference frame that maximally compensates the affine difference of the two image patterns. The image HC-coding and the difference vector is computed from two image patterns using specific orientation (SO) simple cell receptive fields employed in an HC-reference system. The Lie derivative of the difference vector is computed from image patterns using Lie germs. In accordance with one aspect of the invention, the affine transform that maximally compensates the affine difference between two image patterns is found by a gradient system wherein the gradient is calculated from the difference vector and its Lie derivatives. A closed loop adaptive HC-reference frame network is provided to extract binocular affine disparity using an energy minimization technique in a manner which permits for coincidental binocular fusion of the images in the HC-coding and determination of affine invariant pattern distance in general.
1. An artificial neural system for determining stereoscopic image affine disparity, and affine invariant distances of first and second images obtained from coupled imaging sensory device comprising:
a) adjustable HC-linear functional means for receiving intensity signals of said first and second images and providing first-view HC-representation signals and second-view HC-representation signals, and Lie derivative signals of at least one of said first- and second-views;
b) state vector increment means coupled to adjustable HC-linear functional means for receiving HC-representation signals and Lie derivative signals and providing state vector increment signals;
c) HC-pattern distance means coupled to adjustable HC-linear functional means for receiving HC-representation signals of first- and second-views and providing HC-pattern distance signal of said two images;
d) feedback circuit coupled to said state vector increment means for receiving state vector increment signals and providing adjusting signal.
2. The system of claim 1, wherein said adjustable HC-linear functional means comprises a plurality of specific orientation (SO) units, each of said SO-units comprising adjustable HC-reference frame means and Lie differentiation means.
3. The system of claim 2, wherein said adjustable HC-reference frame means comprises first-view simple cell receptive field means and second-view simple cell receptive field means for receiving intensity signals of first image and second image and providing first-view and second-view simple cell response signals, respectively.
4. The system of claim 3, wherein said SO-unit further comprises signal subtraction means coupled to said first-view simple cell receptive field means and second-view simple cell receptive field means for receiving first-view and second-view simple cell response signals and providing simple cell difference signal.
5. The system of claim 4, wherein said feedback circuit comprises global adjusting means for globally adjusting said HC-reference frame and global adjusting control means coupled to said state vector increment means for receiving state vector increment signal and providing global adjusting signal to said global adjusting means in accordance with said state vector increment signal.
6. The system of claim 5, wherein said global adjusting means comprises a plurality of intrinsic influencing means, each corresponding to an SO-unit, coupled to global adjusting control means for receiving global adjusting signal and providing intrinsic influential field signals.
7. The system of claim 6, wherein at least one view of said simple cell receptive field means in said adjustable HC-reference frame means further comprises receptive field adjusting means coupled to said intrinsic influencing means for receiving intrinsic influential field signal and adjusting simple cell receptive field.
8. The system of claim 6, wherein said Lie differentiation means comprises a plurality of Lie germ means for receiving intensity signals of images and providing Lie derivative signals.
9. The system of claim 8, wherein said Lie germ means in said Lie differentiation means further comprises Lie germ adjusting means coupled to said intrinsic influence means for receiving influential field signal and adjusting Lie germ.
10. The system of claim 8, wherein said state vector increment means comprises a plurality of component increment means coupled to adjustable HC-linear functional means for receiving simple cell difference signal and Lie derivative signal, and producing component increment signal.
11. In an artificial neural system including an adjustable HC-linear functional means for receiving intensity signals of said first and second images from a coupled image sensory device and providing first-view HC-representation signals and second-view HC-representation signals and Lie derivative signals of at least one of said first- and second-views, state vector increment means coupled to adjustable HC-linear functional means for receiving HC-representation signals and Lie derivative signals and providing state vector increment signals, HC-pattern distance means coupled to adjustable HC-linear functional means for receiving HC-representation signals of first- and second-views and providing HC-pattern distance signal of said two images, feedback circuit coupled to said state vector increment means for receiving state vector increment signals and providing adjusting signal, the method for determining the stereoscopic image affine disparity, described by l parameters of the two dimensional affine Lie group and affine invariant pattern distance of first and second images of visual stimuli comprising the steps of:
generating a dynamical system with an energy function E(ρ)=|δ(ρ)|2, wherein ρ, the state vector, is extracted by state vector increment means and determines the amount of adjustable HC-reference adjustment, and the non-linear function |δ(ρ)|, a signal extracted by HC-pattern distance means, is substantially equivalent to zero when said adjustable HC-reference frame has been adjusted by amount ρ that is substantially equivalent to said affine disparity in binocular vision; and
determining at least one minimum point of said non-linear function |δ(ρ)| which gives affine disparity and affine invariant pattern distance.
12. The method of claim 11, wherein said dynamical system comprises the step of setting up initial state of said dynamical system.
13. The method of claim 11, wherein said dynamical system further comprising the step of extracting first and second HC-reference frame representation γ1 (ρ)=(γ1 1 (ρ), . . . , γ1 n (ρ)) and γ2 =(γ2 1, . . . , γ2 n) from said first image L and second image R, respectively, by using n pairs of simple cells means in said HC-reference frame means to obtain each simple cell response signals.
14. The method of claim 13, wherein said dynamical system further comprising the step of extracting difference vector
δ(ρ)=γ2 -γ1 (ρ)
of said two adjustable HC-reference frame coordinate vectors by using signal subtraction means to obtain each simple cell difference signal.
15. The method of claim 14, further comprising the steps of adjusting said HC-reference frame providing said coordinate vectors in accordance with the state vector ρ of said dynamical system, and making corresponding adjustment to said difference vector resulting therefrom.
16. The method of claim 11, wherein said dynamical system comprises the step of extracting Lie derivatives of difference vector δ(ρ), Ω.sub.ρj =(Ω.sub.ρj1, Ω.sub.ρj2, . . . , Ω.sub.ρjn), j=1, . . . l.
17. The method of claim 16, wherein said dynamical system further comprises steps of using state vector increment means to obtain state vector increment Δρ.
18. The method of claim 16, wherein said dynamical system comprises step of adjusting the state vector by an amount of state vector increment ≢ρ(t):
where t is time, and Δt is time interval for state vector increments.
19. The method of claim 18, further comprising the steps of adjusting said Lie germ providing adjusted Lie derivatives Ω.sub.ρj (ρ)=(Ω.sub.ρj1 (ρ), Ω.sub.ρj2 (ρ), . . . , Ω.sub.ρjn (ρ)), j=1, . . . , l, in accordance with the state vector ρ of said dynamical system.
20. The method of claim 11, wherein said determining step comprises the step of terminating said dynamical system.
1. Field of the Invention
The invention relates generally to a method and apparatus for extracting geometric information from and performing invariant pattern analysis on visual stimuli and; more particularly, to an artificial neural system for determining affine disparity parameters and affine invariant pattern distance from two image patterns. The invention further relates to an image pattern geometric analysis system comprising a plurality of specially constructed geometric computing devices simulating the simple cells in the visual cortex of primates and functioning as a reference frame within the scope of a hypercolumnar (HC) organization of visual cortex for coding intensity image data, a plurality of specially constructed geometric computing devices simulating Lie germ type hypercomplex cells in visual cortex of primates functioning as infinitesimal generators of the two dimensional affine Lie group for computing Lie derivatives of intensity images in the HC-coding, a plurality of specially constructed geometric computing devices simulating intrinsic neurons in visual cortex of primates functioning as affine transformer of the HC-reference frame, and a feedback circuit for determining affine disparity and affine invariant pattern distance from two image patterns.
2. Description of the Related Art
Artificial vision systems are generally modeled after biological vision systems of vertebrates. With reference to FIG. 1, most vertebrates begin the process of generating visual representations by receiving light from a visual scene 2 through lenses in the right and left eyes onto a retina located in their respective orbs (not shown). Each retina comprises a two-dimensional grid of photoreceptors 4 and 6, respectively, for sensing the light and for generating an analog neural potential, which is proportional to the logarithm of the intensity of the light at a corresponding point in the image. The light incident each photoreceptor comes from the receptive field of that photoreceptor, that is, a local region of space in the vicinity of the receptor. The location of a photoreceptor on the retina is useful for encoding the direction of the light source in real space. Multiple, two-dimensional layers of neurons 8 and 10 process and transmit signals corresponding to light source location information through the optic nerve to two-dimensional layers of neurons in the brain in accordance with a conformal, retinotopic mapping, which maintains the relative spatial locations of the signals. Accordingly, receptive fields of adjacent neurons correspond to adjacent regions of the visual field.
With further reference to FIG. 1, the approximate receptive fields of two individual photoreceptor are illustrated by lines 9 and 11. Only a portion of one of the layers of neurons 8 and 10 associated with the respective photoreceptor grids 4 and 6 in the retina is shown for illustrative purposes. As stated previously, the layers 8 and 10 comprise matrix-like arrangements of individual neurons. Several different types of neurons exist. As shown in FIG. 2, a neuron 12 generally comprises a cell body or soma 14 from which one or more dendrites 16 extend. FIG. 1 depicts several neurons 12 with dendrites 16 receiving input signals from a receptive field through ocular Gaussian windows on their corresponding photoreceptor grids. The windows 20 of neurons associated with a neuron layer can overlap. A dendrite 16 can receive data from a receptive field and can process it, as well as supply it to other neurons through synapses 18 located on the dendrites as shown in FIG. 2. The soma 14 determines a weighted linear summation of the intensity of various points within the window 20.
With further reference to FIG. 2, the soma 14 can include an axon 22 which processes data for local transmission and transmits data over long distances. Not all neurons, however, have axons. In operation, synaptic inputs are collected and are integrated on the capacitance of a corresponding soma until a critical threshold is reached, at which time a somewhat digitized nerve pulse is generated and propagated along the axon. Many types of neurons, including those with axons, have synaptic outputs as well as inputs on their dendrites 16. The dendrites, therefore, serve an important function because much of the lateral communications in the nervous system is local and effectuated by local, graded analog potentials on the dendrites 16, rather than by digital nerve pulses generated by the soma 14 and transmitted along the axon 22.
As neural processing of visual information progresses, representations of the light source become more complex. For example, the retina itself performs transformation from simple intensity to more complex representations such as local averages of intensity with Gaussian weight functions, Laplacians of Gaussian (LOG) operations, and time derivatives of intensity. Thus, signals from the photoreceptor are transformed through several layers of neural processing before transmission through the optic nerve to the brain. Finally, the visual center of the brain, i.e., the visual cortex, construct models of three-dimensional space using the spatiotemporal patterns of output signals received from the retina.
The visual cortex of primates and cats has columnar organization. The pyramid cells of a column orthogonal to the cortical layering respond to visual stimulus from a particular small zone of visual area, and with the same preferred orientation. Successive orientation columns are arranged perpendicularly to the succession of ocular dominance (right and left) columns. A vertical cortical hypercolumn is defined to embrace all preferred orientations for signals from the two eyes. It is a local binocular information processing unit for visual stimulus from a small zone in the view field. The linear simple cells within a cortical hypercolumnar structure provide means for cortical representation of visual stimulus from the zone. The receptive field functions of these simple cells collectively provides a reference frame for cortical representation of the visual stimulus from the zone. The neural receptive fields are not rigidly locked to absolute retinal coordinates. Instead, the receptive fields of the cortical cells are able to dynamically shift and warp to compensate for motion and disparity. Many types of intrinsic neurons are involved in dynamically shaping the receptive fields of the cortical principal neurons. A dynamical receptive field theory is discussed in a paper by D. C. Van Essen, and C. H. Anderson entitled "Reference Frames and Dynamic Remapping Processes in Vision" in Computational Neuroscience. It is believed that the dynamic aspect of simple cells hold the key to the understanding of dynamical and realtime process of binocular stereo, image motion, and invariant pattern recognition.
Binocular vision systems are useful because the fusion of two stereo images allows for the construction of three-dimensional models of visual scenes and, therefore, shape and depth perception. The binocular images perceived by the right and left eyes of most vertebrates, for example, are slightly different views of a three-dimensional scene which is projected onto the right and left retinas, respectively. With further reference to FIG. 1, right and left ocular images 24 and 26 comprise essentially identical intensity values at corresponding pixels. The images, however, are displaced and affine transformed with respect to each other along an x-axis, which corresponds to a line connecting the right and left eyes. The amount that the left and right ocular images of a visual scene are displaced and affine transformed with respect to each other is referred to as binocular affine disparity. Affine disparity maps, i.e., affine disparity values determined at each of an array of points in an image, can be used to calculate the distance between the eyes and an object in the visualized scene and the shape, namely the spatial orientation at each point on the visible surface of the object.
Once affine disparity between right and left ocular images is determined in a biological vision system, binocular images can be fused into a single image which provides three-dimensional, spatial information. The interaction of the left and right ocular views of a scene begin in the striate cortex of a vertebrate's brain. This interaction involves the formulation of binocular data at thin strips of cortex which receive input signals from either the left or right eye. In the primary visual cortex of many primates, these strips or columns of cortex are interlaced such that small patches of the left and right eye view of the scene are located next to one another in the layer IV of the striate cortex. These interlaced columns are referred to as ocular dominance columns.
Artificial vision systems with a binocular vision capability, including binocular fusion, have a number of uses such as stereo mapping and machine vision depth and three dimensional shape perception. For example, remotely controlled robots can be provided with a surface orientation and depth perception capability. Surface orientation and depth is typically determined by a laser range finding system installed on the robot. The robot transmits a laser beam toward a target object using the system. The beam is reflected from the object and sensed by an onboard sensor which measures the time of laser beam travel. The measured time is subsequently used to determine the distance between the robot and the object. The laser range finding system is disadvantageous because it represents an expense in addition to the cost of a vision system in some robotics systems. Further, many robots are employed in environments where the effects of laser energy impinging on objects are undesirable.
Binocular affine disparity computation can further be applied in realtime data processing in photo databases applications. For example, image mosaicking is needed to generate a photoreal perspective scene from a large quantity of raw satellite imagery taken from various locations. Much of the time spent in geo-correcting is spent in finding the affine disparity between overlapped areas of images taken at different locations by satellites. Current methods for finding affine disparity are based on warp and match paradigm, namely, try various combinations of affine transform and find the best match. The trial and error method confronts combinatorial complexity and has difficulty achieving realtime performance.
The mechanism that fuses two affine related image patterns can also perform affine invariant pattern analysis. Affine invariant pattern analysis has various applications in automatic target recognition, robotics object recognition, automatic terrain tracking for lander guidance, and optical character recognition. Due to changeable viewing condition, the sensor image and stored template of object, target area scene, etc., cannot match exactly. Therefore, to perform affine invariant pattern analysis requires a process that compensates the affine disparity of image patterns.
Stereo mapping involves fusing together two images, for example, of terrain taken from two nearby viewpoints in order to obtain a map which provides three-dimensional information such as terrain elevations and three dimensional surface orientation. These images, for example, can be taken at different but relatively close points in time from an aircraft flying over the terrain. In the past, the images have been fused by a tedious process of matching corresponding terrain features in the two images. A system for stereo mapping has been proposed which employs the binocular fusion capability of a human operator's brain to fuse two images of terrain. The operator views the images through a binocular viewing apparatus and adjusts the apparatus until the images are perceived as fused by the operator. The amount of adjustment yields valuable information regarding binocular disparity; however, the actual fusion of the images onto an elevational map of the terrain continues to require matching some benchmark geographic features. A number of methods have been proposed for implementing binocular vision and determining binocular disparity using machine vision. An important aspect of these proposed methods is the computational problem of detecting features from stereo or binocular images at various spatial resolution levels, and finding their correspondence relations. If the stereo correspondence problem, i.e., feature detection and feature matching, is solved, the measurement of the displacements of the features from one ocular representation to another is straightforward. The problem of matching the features, however, is difficult solve. For example, it is difficult to determine whether a generic feature should correspond to another particular generic feature when there are many candidates. When the visible surface is not in the frontal view, the binocular features may vary in scale and be sheared. Moreover, the problem of feature matching itself is not even a well formulated one since it raises the issue of what kinds of features should be considered generic.
The difficulties of using correspondence relations to determine binocular disparity has resulted in other approaches to the binocular disparity problem. As stated previously, the cells in the visual cortex have a receptive field property. When a visual stimulus is spatially shifted within the scope of the sensitive area of the receptive field of a simple cell, the response of the simple cell changes. The binocular affine disparity problem then becomes one of determining the amount of shift, scale, and shear transforms of the visual stimulus from the differences of the responses of the simple cells. The differential response approach to the disparity problem may take different forms depending on the particular forms of receptive fields being employed in a computational model.
One important differential response model is derived from the Fourier theory of visual information processing. The responses of some cortical cells in vertebrates are tuned to narrow bands of spatial frequencies. In general, Fourier models of vision view the receptive field responses of these cortical cells as frequency specific responses. Consequently, the spatial displacement of visual stimuli is represented by the phase shifts of the frequency specific responses. Using Fourier phase shift theorem, a number of analytical methods for computing disparity from phase differences of spatial frequency specific responses have been proposed, some of which are described in the following publications: A. D. Jepson et al., "The Fast Computation of Disparity From Phase Differences", IEEE Proceedings on Computer Vision and Pattern Recognition, pp. 398-403 (1989); and T. D. Sanger, "Stereo Disparity Computation Using Gabor Filters", Biological Cybernetics, vol. 59, pp. 404-418 (1988). The application of the Fourier theorem is problematic, however, because the phase shift derived for the global spatial shift of functions must somehow be made applicable to the non-homogeneous local phenomena of binocular disparities.
In the past, Gabor filters and localized Fourier analysis have been taken as the method to resolve the contradiction between the global nature of the Fourier analysis method and the local nature of displacements of visual stimuli. For example, the local phase information extracted by Gabor filters is exploited for the computation of disparity in the Jepson et al. article, and one dimensional Gabor filters are applied to find local phase difference from which the disparity is calculated in the Sanger article. Nevertheless, the localized Fourier analysis approach to binocular stereo, including those methods using Gabor filters, has limitations. The image disparities are different from place to place. For the local computation of disparity value, the spectral analysis must be performed over a very small image region. Constrained by the limit of image resolution, a local Fourier analyzer usually only contains a few cycles. Typically, these Fourier analyzer are linear bandpass filters with a bandwidth of more than one octave. The spectral location of the extracted response will be uncertain around the central frequency of the local Fourier analyzer. Disparity cannot be accurately determined based on uncertain frequency information.
A binocular model for a simple cell has been proposed by M. Nomura et al. and described in "A Binocular Model for the Simple Cell", Biological Cybernetics, vol. 63, pp. 237-242 (1990), in which binocular simple cells respond with identical receptive fields, but different spatial phases to the linear combination of visual stimuli from two eyes. The simple cells tuned with different binocular phase differences respond to binocular input differently when responding to the disparity associated with the visual stimuli. The disparity thus can be coded in the pattern of simple cell responses. In the models proposed by M. Nomura et al., A. D. Jepson et al., and T. D. Sanger, the basic computation is based on the Fourier phase shift formula even though the Gabor responses from both eyes are regarded as the source information for computing binocular disparity. None of the existing binocular vision models addresses the computational problem of the amount of scale and shear transform of visual stimulus existing in binocular vision. While the amount of shift transform only determines the depth structure of a visible surface, the amount of scale and shear transform further determines the three-dimensional orientation of the surface. The usefulness of the results from these overly simplified approaches is therefore limited.
Besides binocular stereo applications, the model based object recognition which depends upon a comparison of two visual patterns also suffers from the lack of effective means to compute and compensate affine disparity between two image patterns. Current art of automatic pattern analysis systems keeps a large amount of template samples to cover affine variations of the same pattern. Such systems require tremendous computing power and memory storage and have difficulty performing realtime pattern analysis.
The present invention is based on a Lie group model of cortical processing of visual pattern geometric analysis of primates, including affine parameter extraction from two image patterns and automatic compensation of affine-disparity while measuring differences between two image patterns. The present invention provides method and apparatus for computing image affine disparity which realizes advantages over and overcomes several shortcomings associated with existing approaches which only calculates binocular displacements. The affine disparity between two images described by affine parameters not only provides the information for the depth structure of the object being viewed. With extra affine parameters, it also provides information for object shape description, i.e. local surface three dimensional orientation. The same mechanism for determining affine disparity also measures the distance between two image patterns after their affine disparity is compensated, thus providing an affine invariant pattern analysis for images.
Accordingly, it is an object of this invention to provide a method and apparatus for determining binocular affine disparity which provides depth and shape information simultaneously to overcome a limitation of the current art which only calculates local displacements of binocular images.
It is another object of this invention to provide method and apparatus for determining binocular affine disparity directly and accurately from binocular images, without feature extraction and feature matching, without trial and error, in much the same manner the neural system would perceive the three dimensional world, and avoiding the combinatorial complexity and uncertainty suffered by the current art.
It is another object of this invention to provide method and apparatus for determining the affine invariant pattern class in a model based pattern recognition system by automatically compensating the affine disparity between image patterns.
It is another object of this invention to provide neuron-like geometric computing devices that serve as realtime computational means for providing a cortical representation of local binocular imagery in an HC-reference frame.
It is another object of this invention to provide neuron-like geometric computing devices that serve as realtime computational means for transforming the HC-reference frame.
It is another object of this invention to provide neuron-like geometric computing devices that serve as realtime computational means for extracting the Lie derivatives of the affine transformation group consistent with the above said cortical representation.
It is yet another object of this invention to provide a neural system like analog geometric computing engine that serves as a realtime computational means for determining binocular affine disparity and affine invariant pattern class by a dynamical process of energy minimization.
These and other features and advantages of the present invention will be more readily apprehended from the following detailed description when read in connection with the appended drawings, in which:
FIG. 1 is a schematic view of binocular images focused on photoreceptor grids associated with the right and left eyes of a vertebrate;
FIG. 2 depicts an exemplary neuron in a vertebrate's brain;
FIG. 3 is a plot of a viewer centered coordinate system; the plane Z=1 is regarded as the image plane, and an oxy coordinate system is taken on it in such a way that the x and y axes are parallel to the X and Y axes, respectively, with origin at (0,0,1). A point X,Y,Z) in the scene is projected onto the intersection of the image plane with the ray connecting the point (X,Y,Z) and the origin O, F, the focal length, is taken to be 1 unit.
FIG. 4 is a schematic diagram of a closed loop, dynamical HC-coordinate system constructed in accordance with the present invention to determine binocular affine disparity and affine invariant pattern distance;
FIG. 5 depicts a right-ocular dominant binocular pair of specific orientation (SO) columns in visual cortex;
FIG. 6 is a schematic diagram of a circuit constructed in accordance with the present invention to compute the increment of the state vector and the energy of the dynamical system from the updated Lie derivatives and difference vector after adaptively affine transforming the first-view frame.
FIG. 7 depicts an influential field adjusting the receptive field of a neuron;
FIG. 8 is a schematic diagram of an analog multiplicative circuit; and
FIG. 9 is a schematic diagram of an analog adder circuit.
This invention is built around an artificial neural system based on a Lie group model of information processing of binocular affine disparity and affine invariant pattern analysis in hypercolumns (HC) of primate's visual cortex. The affine disparity between two image patterns is the two dimensional affine transform that brings one image pattern to the closest distance from the other. Affine invariant pattern analysis is a method to classify image patterns that treats affine related patterns as in the same pattern class.
In said artificial neural system, a dynamical system is set once two image patterns are input. The state vector of said dynamical system represents parameters of affine transformations and the energy function of said dynamical system represents the square of the Euclidean distance of two views of cortical patterns. A novel method and geometric computing devices for computing hypercolumn (HC) coordinates and affine Lie derivatives are disclosed in another U.S. patent application Ser. No. 08/352,140, filed Dec. 1, 1994, and is summarized below.
In accordance with the present invention, the intensity image of a visible surface f(x, y) is a square integrable (L2) function: ∫∫f2 (x, y)dxdy<∞. Here x and y are horizontal and vertical coordinates of pixels.
In accordance with the present invention, the HC-reference frame comprises a plurality of pairs of linear simple cells. Each said pair comprises a first-view simple cell and a second-view simple cell having a common normal receptive field function gi, i=1, . . . , n, where n is the total number of the simple cell pairs in the HC-reference frame. In the preferred embodiment, the receptive field functions of first-view simple cells can be affine transformed and are functions of affine parameters gi (ρ)=A*(ρ)∘gi, where A*(ρ) is a two dimensional (2D) affine transform, ρ=(ρ1, . . . , ρl) is the parameter vector of the 2D affine transform, and l (≦6) is the number of affine transform parameters employed in the process. The receptive field functions of simple cells are vectors in the dual space of the L2 space of the images: Each gi is a reference vector (functional) on L2. Collectively, in the preferred embodiment of current invention, the receptive field functions gi, i=1, . . . , n, constitute a static HC-reference frame for second-view images and gi (ρ), i=1, . . . , n, constitutes a dynamical HC-reference frame for first-view images.
The set of values
Y2 i =(gi, f), i=1, . . . , n. (1)
provides a second-view HC-representation for the image pattern f, where (gi, f) is the Hilbert space inner product of f and gi. The first-view HC-representation is provided in a similar manner via inner product with gi (ρ):
γ1 i (ρ)=(gi (ρ), f), i=1, . . . n.(2)
With reference to Equations 1 and 2, γ1 (ρ)=(γ1 1 (ρ), . . . , γ1 n (ρ)) and γ2 =(γ2 1, . . . , γ2 n) are the first-view and second-view HC-coordinate vectors of a retinal intensity image (briefly, retinal image, or simply image) f(x, y), respectively. In the preferred embodiment, the j-th Lie derivative of the i-th HC-coordinate component γ1 i (ρ) of f is
Ωj i =<A*(ρ)∘gij,f>
where gij =Xj *∘gi, is a Lie germ and Xj * is the Hilbert space conjugate of the infinitesimal generator of the j-th 1-parameter Lie subgroup of the 2D affine Lie group A(2, R).
Briefly, a novel method for determining affine disparity and affine invariant distance between two image patterns in accordance with the present invention which uses a dynamical system is provided. In addition, a closed loop adaptive HC-reference frame is provided as an analog implementation of said dynamical system.
The novel method of current invention uses HC-coordinate vectors to represent light intensity information from an image pair comprising a first image and a second image in two separated views: first-view and second-view. In the preferred embodiment, said image pair can be the binocular images taken by two cameras being positioned parallel to each other and having a common image plane.
For illustrative purposes, it is convenient to introduce a viewer centered coordinate system for a binocular imaging system and denote the one associated with the first camera as OXYZ and such that the image plane is the plane Z=1, and the focus of the perspective projection is the origin O. The x axis and the y axis of the coordinate system oxy on the image plane are parallel to the X axis and Y axis, respectively. The origin of the image plane is at (0,0,1). The viewer centered coordinate system associated with the second camera O'X'Y'Z' and its image plane coordinate system o'x'y' are defined similarly. System O'X'Y'Z' is related to system OXYZ by a spatial shift of amount t along the X axis. Using the above introduced coordinate systems, the binocular differences of the image taken by said first camera and the image taken by said second camera is the following affine transformation A(α, β, τ): ##EQU1##
In APPENDIX A, it is shown that the normal vector of the visible surface, (ω31, ω32, ω33), which gives three dimensional orientation of the surface, is related to affine parameters α, β, τ:
(ω31, ω32, ω33) =(α/l, β/l, τ/l) (4)
where l=√α2 +β2 +τ2 is the Euclidean norm of vector (α, β, τ), and the depth Z0 of the surface patch with image coordinates around (x0, y0) is
Z0 t/(ω31 x0 lω32 yO l+ω33 l). (5)
Thus, three dimensional shape and depth of a visible surface are determined once the binocular affine disparity, the values of α, β, and τ, are determined.
A general mathematical scheme for determining affine disparity and affine invariant distance between two visual patterns is shown in APPENDIX B. The novel method of the current invention uses a dynamical HC-reference frame to represent light intensity information from two images, and results in two views of HC-coordinate vectors, one for the first image and one for the second image. In general, these two HC-coordinate vectors are different. In the preferred embodiment, the method measures affine disparity by affine transforming the first-view HC-reference system to achieve identical or the closest possible HC-representations of two images. The affine invariant pattern distance is the Euclidean length of the difference vector of the closest possible HC-representations of two image patterns. It will be zero when one image pattern is an affine variation of the other, such as in binocular vision, or in pattern analysis where one is the template of an input image pattern. The distance will be greater than zero if two patterns are not in the same affine invariant class.
For illustrative purposes, the embodiment of an artificial neural system for determining affine disparity and affine invariant distance between two image patterns being described in the following is a binocular vision system, in which only three affine parameters, denoted as α, β, and τ, are involved. In general, all the six affine parameters may be involved in the process of determining affine disparity and affine invariant distance between two image patterns. The generalization and modification from a system involving three affine parameters to a system involving more affine parameters are within reach of the ordinary skill of art.
With further reference to FIG. 2, right and left ocular images R(x, y) 24 and L(x, y) 26 are illustrated as seen, for example, from the corresponding right eye and left eye of a vertebrate. At position (x0, y0) in the right and left images, the binocular disparity is represented as A(α0, β0, τ0). The right and left images are substantially the same, i.e., the intensity of corresponding pixels is practically identical. The left and right images, however, are affine transformed with respect to each other. Thus at a small neighborhood centered around (x0, y0), the right image R(x, y) and left image L(x, y) have the following relation:
R(x,y)=A(α0, β0, τ0)∘L(x,y)(6)
In HC-representation, Equation 6 leads to the following system of equations:
<gi, R>=<gi,A(α0, β0, τ0)∘L>, i=1, . . . , n.
The novel method of current invention determines binocular affine disparity A(α0, β0, τ0) by finding an affine transform A*(α0, β0, τ0) acting on the HC-reference frame that is conjugate to A(α0, β0, τ0):
(gi,R)=A*(α0, β0, τ0)∘gi,L), i=1, . . . , n. (7)
The method of current invention is different from the method suggested by Anderson and Van Essen ("Shifter circuits: a computational strategy for dynamic aspect of visual processing, Proceedings of National Academy of Sciences, U.S.A. 84, 6297-6301, 1987). The method of this invention does not transform one of the two images to match the other. Instead, the method of current invention transforms the receptive fields of simple cells in the first-view HC-reference frame to maximally compensate the affine difference and minimize the distances of HC-vectors of the two images, and thereby determine affine disparity and affine invariant pattern distance between the two images.
Binocular fusion can be a left-ocular dominated process in which the resulting fused view will be the same as left-ocular view, or a right-ocular dominated process in which the resulting fused view will be the same as right-ocular view, or a balanced process in which the resulted fused view is in the between of two ocular views. As will be described in further detail below in connection with FIG. 5, in the preferred embodiment, the system is a right-ocular dominated system. In this system, the HC-reference frame of second-view, which represents the right-ocular image, is static and the HC-reference frame of first-view, which represents the left-ocular image, is adaptive. According to the present invention, and with reference to Equation 7, the binocular disparity problem is solved by finding a small affine transformation A*(α, β, τ) for the first-view HC-reference frame such that the HC-coordinate vectors of first-view and second-view representations are equal, namely:
δi (α,β, τ)=γ2 i -γ1 i (α, β, τ)=0, i=1, . . . , n.(8)
γ1 i (α, β, τ)=<A*(α, β, τ)∘gi, L>, i=1, . . . , n
are components of the HC-coordinate vector of the left-ocular image in an affine transformed HC-reference frame, and
γ2 i =<gi, R>, i=1, . . . , n
are components of the HC-coordinate vector of the right-ocular image. System of Nonlinear Equations 8 is preferably solved using a dynamical system having an energy function. The dynamical system reaches an equilibrium state where the energy function has a minimum value. In accordance with current invention, the energy function is chosen to be the square of the length of the difference vector of first-view and second-view HC-coordinate vectors: ##EQU2## Such a dynamical system can be properly constructed provided the Lie derivatives of δi (α, β, τ) can be calculated. The use of HC-representation is advantageous because, among other reasons, the Lie derivatives of δi (α, β, τ) can be calculated using Lie germs described in the above-referenced U.S. patent application Ser. No. 08/352,140, Filed Dec. 1, 1994. In the preferred embodiment, affine disparity A*(α0, β0, τ0) of binocular images is determined at the minimum energy point (α0, β0, τ0) of the gradient system:
(dα/dt, dβ/dt, dτ/dt)=-∇E(α, β, τ),
∇E=(∂E/∂α, ∂E/∂β, ∂E/∂τ),
and ##EQU3## In accordance with current invention, the Lie derivative Ω.sub.αi of δi is calculated using expression:
Ω.sub.αi =<A*(α, β, τ)∘gi,α,L>,
where gi,α is the i-th Lie germ of the infinitesimal generator of the one parameter Lie subgroup of parameter α. Similarly Ω.sub.βi and Ω.sub.τi are calculated. Also the affine invariant distance between two image patterns L and R are determined at the same minimum energy point (α0, β0, τ0):
dH (L,R)=|δ(α0, β0, τ0)|H
where δ is an n-dimensional vector (δ1, . . . , δn).
It is understood that other numerical schemes for solving system of non-linear equations also can be used to find approximate solution of System of Equations 8. For example, the multi-dimensional Newton method using Lie derivative information also can be applied to find approximate solution for System of Equations 8. The use of gradient system is advantageous because it finds the minimum distance between two image patterns by compensating affine disparities, even when two images R and L are not related by System of Equations 8, as in general affine invariant pattern analysis.
As will be described in further detail below in connection with FIG. 4, Lie derivatives Ω.sub.αi, Ω.sub.βi, Ω.sub.τi and difference value δi can be obtained from response signals of Lie germs and specific orientation (SO) simple cells. The circuit of adaptive HC-reference frame is described with reference to FIGS. 4 and 5. The circuit depicted in FIG. 4 is configured using a gradient scheme to determine the affine disparity A(α0, β0, τ0) and affine invariant pattern distance |δ0 |H, where δ0 is the difference vector (δ0 1, . . . , δ0 n) when the dynamical system converged to the minimum energy. The blocks shown in FIG. 4, which perform mathematical operations using binocular image data, can be implemented as one or more analog circuits or as a programmed sequence of operations performed by a digital computer.
With continued reference to FIG. 4, the adaptive HC-reference frame circuit 30 receives data from right-ocular and left-ocular images 24 and 26, respectively. The adaptive HC-reference frame circuit 30 comprises a system of n SO-units 40 for computing δi, the difference of projections of the right image data and the left image data on said SO-unit, and Ω.sub.αi, Ω.sub.βi, Ω.sub.τi, the Lie derivatives of δi with respect to affine parameters α, β, and τ. With reference to FIG. 5, a right-ocular dominant SO-unit 40 further comprises a second-view simple cell 61, a first-view simple cell 62, three first-view Lie germs 63, 64, 65, an intrinsic neuron 58 accompanying the first-view simple cells and Lie germs, and a subtractor 60. The first-view simple cell 62 and Lie germs 63, 64, 65 are configured to be affine transformable upon receiving influential field signal from the accompanying intrinsic neuron 58, to accommodate the binocular affine disparity.
With further reference to FIG. 4, the output signals 41 of n SO-units, Ω.sub.αi, Ω.sub.βi, Ω.sub.τi, δi, i=1, . . . , n, are supplied to DOT-Product circuit 42. With reference to FIG. 6, the DOT-Product circuit receives the output signals 41 (not shown in figure) from n SO-units and provides state vector increment signal 43 Δρ and energy value 43a |δ|2 by dot products of δ with Ω.sub.α, Ω.sub.β, Ω.sub.τ, and δ, respectively. The output signal of DOT-Product circuit, |δ|2 43a and Δρ=(Δα, Δβ, Δτ) 43, modifies energy function E(ρ) and the state vector (α, β, τ) of the system. The state vector ρ in turn controls further adjustments of the adaptive first-view simple cells in the HC-reference frame via a feedback circuit in a manner described below in connection with FIG. 7.
The SO-units preferably process data from two input image patterns in accordance with the feedback data Δρ=(Δα, Δβ, Δτ) until the minimum energy is reached. At the minimum energy state, the difference between two views is minimum, and the receptive fields of first-view simple cells are transformed an amount A*(α, β, τ) that is substantially conjugate to the binocular disparity: A(α, β, τ)≈A(α0, β0, τ0). Upon convergence, and with reference to Equation 6,
γ1 i (α0, β0, τ0)=γ2 i,
δi =γ2 i -γ1 i (α0, β0, τ0)=0, i=1, . . . , n.
Thus, the closed loop adaptive HC-reference frame is configured to model the manner in which cortical cells from a binocular pair represents the image with identical cortical responses upon fusion.
An advantage of the present invention is that it attempts fusion of two images along with the determination of binocular disparity. When substantially all of the adaptive receptive fields of the first-view simple cells in a right-ocular dominant system have converged, not only are the binocular affine disparities determined, but also the difference of the HC-representations of the two image patterns is minimized. The Euclidean norm of the minimum difference vector of the first- and second-view HC-vectors is affine invariant. And the cortical representations of binocular images are substantially identical. Accordingly, binocular fusion is accompanied by the process of computation of the binocular disparity.
In accordance with another aspect of present invention, the adaptive receptive fields of the first-view simple cells 62 as well as the first-view Lie germs 63, 64, 65 comprises a neural network implementation of an affine transform mechanism for adjusting the simple cells 62 and the Lie germs 63, 64, 65, that is, the weights of the synapses of their receptive fields in accordance with the values of α, β, τ. The affine transform mechanism is a neural function which transforms a two-dimensional cortical cell receptive field using neural computational primitives.
The affine transform mechanism of intrinsic neuron 58 is schematically illustrated in FIG. 7. In order to change the linear processing property of the receptive field, i.e., a response of one of the HC-cells 62i, 63i, 64i, 65i, i=1, . . . , n, the synaptic weights in the receptive field are preferably changed in a global and coordinated manner. Accordingly, the affine transforming circuit 58 is realized by an intrinsic neuron having an influential field, the neural function of which is to translate the input signal ρ=(α, β, τ) from affine transforming control circuit 44 into an output spatial function w(x,y; ρ). The spatial function is applied to the synapses 59 on the receptive field 68 associated with each of the simple cells to influence the linear processing property of its receptive field in a basically multiplicative manner. The output signals applied to each of the HC-cells 62i, 63i, 64i, 65i are represented schematically in FIG. 5 by arrows 66. With reference to FIG. 4, the application of function w(x,y; ρ) to the receptive field is controlled by an affine transforming control circuit 44, the output signal ρ 56 of which is applied to the affine transforming circuit 58. When Δρ=0, the affine transforming control circuit 44 reflects that there is no more change as result of affine transforming the first-view HC-cells receptive fields, i.e., disparity is determined, and the intrinsic neuron ceases adjusting the inputs 66 to the receptive fields of the HC-cells 62i, 63i, 64i, 65i.
The output signal of the affine transform mechanism 58 is an affine transform operator A*(α, β, τ). For pure translation τ, the affine transform operator A*(0,0,τ) is preferably carried by a simple function w(x,y; ρ)=w(x,y; 0,0,τ) which can be implemented by neural primitive computations, as can be the computations performed by the Gabor receptive fields functions of cortical simple cells
gi (x,y)=exp {-π x2 a2 +y2 b2 !}�exp{-2πj νx i x+νy i y!}.
With the Gabor function, ##EQU4## where w(x,y; 0,0,τ)=exp πa2 (2xτ-τ2)+2πjνx i τ! is the influential field function. The influential field can be implemented by primitive neural computations using circuits described below, for example, negative feedback loops and the analog components depicted in FIGS. 8 and 9. For general affine transforms, local exponential mapping and logarithm mapping, which also are part of neural primitive computations performed by analog microcircuits of neural system, will be involved besides the multiplicative connections. Thus, with continued reference to FIG. 7, the computational structure of the adjustable simple cell receptive fields A*(α, β, τ)∘gi is determined by the interaction of a fixed receptive field gi (x, y) 59 and the output analog signal of the influential field w(x,y; ρ). For a pure shift, the interaction is basically a multiplication, i.e. A*(0,0,τ)∘gi (x,y)=gi (x,y)w(x,y; 0,0,τ). The adaptive HC-reference frame circuit 30 and the neural implementation of the affine transforming mechanism 58 is advantageous over existing affine disparity computing systems that shift or affine transform the images themselves as opposed to that shift or affine transform the receptive field function. Receptive field function affine transforming is simpler to implement than image affine transforming because receptive field functions have known analytical form. Further, most disparity computing methods involving image shifting allow for only integer valued shifts, i.e., shifts based on a number of pixels. In contrast, the present invention is not constrained to integer valued shifts.
In addition to the feedback circuitry, the simple cells and Lie germs 61, 62, 63, 64, and 65, and the arithmetic circuit 60 depicted in FIG. 5 can also be implemented by primitive neural computations and are therefore realizable as analog circuits. For example, the simple cells and Lie germs may each comprise a massive array of resistors with multiple inputs. The controller 44 and the DOT-Product circuit 42 in FIG. 4 can each be implemented as a Gilbert transconductance multiplier 72 which is depicted in FIG. 8. The subtractor 60 can be implemented as a basic Kirchoff adder circuit. As shown in FIG. 9, the Kirchoff adder circuit 74 comprises p-channel and n-channel transistors, the drain currents of which are added and subtracted in accordance with Kirchoff's laws.
The implementation of adaptive HC-reference frame network depicted in FIG. 4 using real-time, analog circuits is advantageous in such applications as robotics because of their low energy consumption. The adaptive HC-reference frame network can also be implemented at real-time frame rates using high speed digital hardware such as a processor-controlled connection system designed for use in neural network systems.
The present invention has a number of advantages. For example, the gradient system method of pattern distance minimization and root finding is significantly more efficient than schemes employing small image patch cross-correlation. Most of the efficiency is provided by the Lie derivative information associated with the Lie germs 63, 64, and 65, depicted in FIG. 5. According to the above-referenced method proposed by Anderson and Van Essen, the spatial shift of the receptive field was performed by the shift registers working on shifting the image pixels. The amount of spatial shift from the derivative computation associated with the gradient system method is usually not an integer step, whereas the shift operation on the images is constrained to integer steps. In accordance with the present invention, spatial shift can be applied on the adaptive simple cells instead of on the images themselves. Since the HC-cell receptive field functions are analytically defined, practically all real number spatial shifts are permitted. In addition, the concept of an adjustable HC-reference frame has far reaching consequences beyond simply eliminating difficulties associated with spatial shift in an image. Adjustable HC-reference frame also suggests a closed loop adaptation process which is employed in accordance with the present invention.
Due to the simple analytical form of simple cell receptive field functions, the structure of the spatial shift operation, as well as other affine transformations, on the cortical simple cells has a simple mathematical form. By formulating affine disparity computation as a process of minimization of |δ|H, the neural dynamical system determines the affine invariant pattern distance of two images as a byproduct of the process of extracting affine disparity information. Binocular convergence is possible once binocular disparity is known and completely compensated by the affine transform of the adaptive HC-reference frame. Thus, the HC-representation of the binocular images are fused.
Although the present invention has been described with reference to a preferred embodiment, the invention is not limited to the details thereof. Various modifications and substitutions will occur to those of ordinary skill in the art, and all such modifications and substitutions are intended to fall within the spirit and scope of the invention as defined in the appended claims.
For illustrative purpose, it is convenient to use a viewer centered coordinate system, and denote the one associated with the first camera as OXYZ such that the image plane is the plane Z=1, and the focus of the projection is the origin, O. The x axis and the y axis of the coordinate system oxy on the image plane are parallel to the X-axis and Y-axis, respectively. The origin o of the oxy coordinate system is at (0, 0, 1) (FIG. 1). Similarly, denote the one associated with the second camera O'X'Y'Z' and its image plane coordinate system o'x'y'. System O'X'Y'Z' by a spatial shift of amount t along X axis.
In this configuration, a physical point P at position (X,Y,Z) has image coordinate ##EQU5## Its coordinate in the O'X'Y'Z'-system is ##EQU6## And the corresponding image coordinate in the o'x'y'-system is ##EQU7##
Let ω1, ω2, ω3 be three vectors, where
ω1 =(ω11, ω12, ω13),
ω2 =(ω21, ω22, ω23),
ω3 =(ω31, ω32, ω33),
and let following P0 -ω1 ω2 ω3 -coordinate system be attached to a point P0 on a smooth physical surface: The first two of the reference vectors, ω1, ω2, are a pair of orthogonal vectors on the tangent plane of the visible surface, and the third one, ω3, is the normal vector of the visible surface. The center of the coordinate system is at P0 =(X0, Y0, Z0).
Let (ξ, η, ζ) be the P0 -ω1 ω2 ω3 -coordinate of a physical point P on the visible surface nearby the physical point P0. Its OXYZ-coordinates will be: ##EQU8## And its O'X'Y'Z'-coordinates will be: ##EQU9## Now, suppose a point p on the image plane has oxy-coordinate (x, y), and is in the neighborhood of point p0, which is the image point of P0 and has coordinate (x0, y0). The Z-coordinate of the physical point P on the visible surface that generates image point p will be:
Z=(ω31 X0 +ω32 Y0 +ω33 Z0)/(ω31 x+ω32 y+ω33)
And the o'x'y'-coordinates of the image point of the physical point P will be: ##EQU10## where α=tω31 /(ω31 X0 +ω32 Y0 +ω33 Z0), β=tω32 /(ω31 X0 +ω32 Y0 +ω33 Z0), and τ=tω33 /(ω31 X0 +ω32 Y0 +ω33 Z0).
Thus, we have the affine transform between the coordinates of oxy-system and o'x'y'-system: ##EQU11## and the normal vector of the visible surface (ω31, ω32, ω33), which gives three dimensional orientation of the surface, is
(ω31, ω32, ω33)=(α/l, βl, τ/l)
where l=√α2 +β2 +τ2 is the Euclidean norm of the vector (α, β, τ), and the depth Z0 of the surface patch with image coordinate (x0, y0) is
Z0 =t/(ω31 x0 l+ω32 y0 l+ω33 l).
Let A(ρ) be a two dimensional (2D) affine transform, where ρ=(ρ1, ρ2, . . . , ρ6) is the parameter vector. Let A(R, 2)={A(ρ)|ρεR6 } be the Lie group of 2D affine transforms. Given a 2D pattern f(x,y), (A(ρ)∘f)(x, y) is the image of the 2D pattern f under the affine transform A(ρ). The set
Traj(f,A(R,2))={A(ρ)∘f|ρεR6 }
is called the orbit (or trajectory) of the pattern f under the 2D affine transformation group A(R,2). Considering f as a vector in Hilbert space L2, and introducing a topology with the L2 metric, the orbit off under A(R,2) will be a six dimensional manifold.
Let f0 be another 2D pattern, say, a template. The distance between f and f0, d(f, f0), can be defined as the Hilbert space distance of the vector f0 to the orbit Traj(f, A(R,2)):
d(f, f0)=min.sub.ρεR.spsb. |A(ρ)∘f-f0 |L.spsb.2
The distance thus defined is an affine invariant. Pattern f is in the same class as the template f0 if d(f, f0)=0.
In the HC-representation of image patterns, the Euclidean length of the difference vector δ=γ2 -γ1 of two HC-coordinate vectors γ2 =(γ2 1, γ2 2, . . . , γ2 n), where γ2 i =<gi, f> and γ1 =(γ1 1, γ1 2, . . . , γ1 n), where γ1 i =<gi, f0 >, provides another metric for the distances between image patterns: ##EQU12## With HC-metric, a pattern distance between f and f0, dH (f, f0), can be defined as the distance from vector f0 to orbit Traj(f, A(R,2)):
dH (f, f0)=min.sub.ρεR.spsb.6 |A(ρ)∘f-f0 |H
The distance thus defined is an affine invariant. If pattern f is in the same class as the template f0, then dH (f, f0)=0.
Let the energy function E(f, f0, ρ) be defined as
E(f, f0, ρ)-|A(ρ)∘f-f0 |H 2
the computation of affine invariant pattern distance in HC-representation will be the same process of minimizing the energy function. When the two images are binocular pair, they are in same orbit. The minimum energy will be zero and the value ρ which minimizes the energy function will be the binocular affine disparity. In this case, the same process also solves for the system of equations:
<gi, A(ρ)∘f>-<gi, f0 >=0, i=1, . . . , n.
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Calling all members to call on all non-members: ADAA needs your help to build association membership.
We all have friends and colleagues in the dental assisting profession who are not members of the ADAA for one reason or another. Which raises the question, "Why not? Why aren't they members of the ADAA?" What are some of the reasons preventing them from holding membership in our professional association? Let's take a look together, shall we?
I can't afford it ... it is too expensive. My question is, compared to what? Being a member of any professional organization has its expenses and in the economic times of today, we are all very sensitized to finances. However, take my dues for example. I live in California, where everything is more expensive, and yet my dues come out to 45 cents a day. That works out to be $13.75 a month, which now seems more manageable. The great thing about the ADAA membership is that you have the option to pay in a variety of ways:
* Pay in full-with either a credit card or by check
* Pay in half-with either a credit card or check.
* Pay monthly. If you pay with a credit card, debit card or through your bank account, there is the option of monthly payments.
Regardless, there are options to help make your ADAA membership payments more affordable and more convenient. Many other associations do not offer any payment options for dues.
There are no activities or meetings in my area. Were you ever a Brownie, Girl Scout, Cub Scout, Boy Scout? Do you remember how it just took you and one friend to get excited and before you knew it, someone's mom or dad was committing to be a scout leader? That is the key. It just takes two of you to get excited, develop momentum and start something wonderful. As a member, the ADAA has a wealth of resources for meeting ideas, "how to get started" guides and financial support available to recruit members. Don't forget your ADAA district trustees who are trained and at the ready to be of assistance as well.
The truth is these obstacles can easily be overcome if one sees the value in being a member of their professional association, by being a member of the ADAA. Does it require an investment of money and maybe some time? Yes, it requires money to be a member, but the time investment is dependant on what you personally expect to get out of your membership. Is it worth it? It has been said about many things that "You only get out what you are willing to put in" With membership in the ADAA, you receive much in return. Here are just a few of the benefits our members enjoy:
Professional Liability Insurance Add to your security as a dental assistant with this member benefit.
Broaden your knowledge with education--Free unlimited online continuing education for ADAA members. Take advantage of the extensive ADAA library of courses, which contain current and up to date information on a multitude of subjects.
Networking--Opportunities online through Facebook, LinkedIN, Twitter, Foursquare and through state, local and national meetings, conferences and events. There is nothing more helpful than personal and professional connections. Networking can introduce you to other influential individuals by widening your personal and professional network, which is a large part of participating in a professional association.
Best Practices--Our online newsletter, 24/7, provides opportunities to learn about breaking news in your field, learn new ideas, hear about key achievers in the profession, and meet to brainstorm with others who are also looking to share and learn new information.
Resume Boosting--Membership in your professional association indicates that you take your career seriously. It shows dedication of your personal time to your professional growth. "Professional Associations" as a section on your resume is both eye-catching and important in today's business world.
Online job and resume postings--Take charge of your career. This is a great tool to find specific job postings. Additionally, there are career resources available such as tips on effective resumes or cover letters, and job searching strategies to members. ADAA's online Career Center has it all.
Conferences and meetings--Opportunities are available for members to participate in national, state and local courses, conferences and workshops. Members are often given priority registration for their organization's convention and may receive discounts on conference fees or special rates.
Publications--Include online publications, newsletters and the Dental Assistant Journal.
Politically savvy--Stay abreast of federal and state legislative developments that impact the dental assisting professional.
Leadership--From a committee to a council to a chair to the executive board, opportunities are available for you as a member. Whether it is national, state or local involvement in civic and philanthropic ventures, you can become involved in many worthwhile projects. The ADAA contributes to communities in a variety of ways, from providing scholarships and hosting career days for students to supporting important charitable causes.
These are just a few of the benefits our members enjoy. Ask any member what he or she values in ADAA membership and the answer will include the importance of relationships that are established and nurtured.
Please don't get me wrong, the information, the education, the publications and the insurance programs are all important. However, the mortar that sticks us all together is the relationships, the friendships, the networking, the support that is developed through our memberships. Consider this: For pennies a day, you can make a difference to yourself personally and professionally, and you have an opportunity to make a difference to the profession of dental assisting. Are you worth it? I think so. Still not sure? Ask a member. Members will tell you, "Join today! You'll be glad you did!" Go to www.dentalassistant.org for your member application. Joining has never been easier--just complete your online application and start the )Tear with the choice that will change your life forever. Congratulations and welcome to the ADAA!
Here are a just a few testimonials from new and long term ADAA members:
"I really enjoy being a part of the ADAA, to be a part of the community and know what is going on across the world and within the dental society. Being an ADAA member has allowed me to be involved in changing and making new decisions for our future."
Cametra C., member since 2011
"I am an ADAA member to support our association, to keep up ort all current laws passed and how it benefits me as an RDA, and to keep current on all news."
Liz S., member since 1986
"I am excited to be an ADAA member. My career is very important and the ADAA keeps me up to date on all the changes and positive information."
Mary N., member since 2008
"I enjoy being an ADAA member because it allows me to congregate with others in the dental field. I gain a lot of knowledge and wisdom from my fellow members."
LaToya J., member since 2011
"Being an ADAA member has given me the information, updates and community needed to stay updated in my profession. The world of dentistry is constantly changing and being a member keeps me informed."
Valerie D., member since 1986
"I am a member of the ADAA because it gives me a way to be connected with other dental assistants in my area."
Janice B., member since 2005
Shari L. Becker, CDA, RDA, FADAA, is Twelfth District Trustee and the ADAA Council on Membership Chair. She is a resident of Concord, Calif., and has been an ADAA member since 1986.
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We are always accepting new course proposals, as well as suggestions from our audience regarding the courses they're interested in taking. If you would be able to teach a course on any of the following topics, please send us your course proposal! And be sure to spread the word if you know someone who could teach. We look forward to hearing from you!
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FOX Sports Exclusive
Boeheim courageous to take stand
After Hall of Famer Jim Boeheim notched his 900th win as Syracuse's head basketball coach on Monday night, joining Mike Krzyzewski and Bobby Knight as the only coaches in that elite club, he stood at a podium for one of his most hated rituals: the postgame press conference.
Boeheim's petulant in-game manner often comes out after games, as well, when he is known to occasionally snap at reporters' questions or call out writers who have ripped on his team. But at the laudatory press conference Monday, where the topic at hand was Boeheim's remarkable 37-year run at Syracuse and his legacy in college hoops history, he ended the press conference in a most unusual way.
"This will probably offend some people," the 68-year-old Boeheim began. "If we in this country as Americans cannot get the people that represent us to do something about firearms, we are a sad, sad society. . . . I'm a hunter. I've hunted. I'm not talking about rifles — that's fine."
He was talking about the Newtown, Conn., elementary school shootings from last week and the semi-automatic weapon used by the killer.
"If one person in this world — NRA, president, anybody — can tell me why we need assault weapons with 30 shots in the [magazine] . . . we need to give teachers guns, so they can shoot people? Yeah, that's really good thinking, to do that."
Twitter, perhaps the best barometer of our reactionary society's penchant to explode over famous people's words, immediately exploded. Huzzah to Boeheim, for using his sports pulpit to make a common-sense stand for gun control! Or shame on Boeheim, for using his sports pulpit to encourage politicians to infringe upon our constitutional rights!
Social media hubs such as Twitter quickly devolve into lowest-common-denominator sniping on the most sensitive of American topics: race and religion, sexuality and abortion, immigration and gun control. And that sniping looks past the fact Boeheim was following a grand, though increasingly rare tradition, in American sports by using his sporting celebrity to speak out on things that actually matter.
Say what you will about the content of Boeheim's remarks. He's heroic; he's idiotic. What can't be denied are the guts it takes for a high-profile sports figure to step out of a comfort zone that typically focuses on the finer points of the 2-3 zone defense. It would have been much easier to soak in the adulation of his moment, honor the victims with a word and a prayer and move on, avoiding the more highly charged subject of gun control.
Instead, in this moment Boeheim followed in the footsteps of American sprinters Tommie Smith and John Carlos, raising their fists in defiant black power salutes on the medal stand at the 1968 Summer Olympics. Or of Muhammad Ali using his heavyweight pulpit to speak out against the Vietnam War. No, Boeheim wasn't risking being ostracized in the same fashion, but he certainly didn't play it safe to protect his popularity.
It's one thing for a coach or an athlete to make a statement against something like cancer (another topic Boeheim has frequently lent his voice and influence to over the years). But what coach is in favor of cancer? It's one thing for NFL teams to honor the victims of the Connecticut shootings during their weekend games, or for Tennessee Titans running back Chris Johnson to write the names of each of the shooting victims on his shoes for "Monday Night Football." Admirable to recognize that sports aren't everything? Of course. Important for sports figures to recognize the tragedies of the real world? Absolutely.
But courageous? No.
What Boeheim did was courageous, whether you're on his side or not.
I have a rather strong opinion about the Second Amendment, about gun control, and about how our nation's freedoms are balanced with our nation's responsibilities. I also realize that you don't care about my opinion on any of these things. I'll have these conversations with friends, but I'm not going to throw them about on Twitter, or here, where they'll only add to the already-deafening noise and awful vitriol from both sides of this heated topic. My pulpit doesn't bully so well.
Boeheim's did. Short of an on-court interview in the moments after a national championship victory, this might have been the biggest bully pulpit he'd ever have. He knew it. He knew he'd be stepping into dangerous waters. He did it anyway, knowing that, after 37 successful years as a head basketball coach, it's not like his job was on the line.
WHAT A RIDE!A lot can happen on the way to 900 wins. See Jim Boeheim's career highlights in pictures.
"This is our fault," Boeheim told the group of reporters after win No. 900, as his wife and young children stood nearby. "This is my fault, and your fault, all your faults. . . . If we can't get this thing done, I don't know what kind of country we have. Because this is about us."
Some will say, hey, you're a basketball coach, stick to what you're good at, don't tell me what to think about gun control. Entertainment is escapism, and you're not allowing me to escape. The left said that about Clint Eastwood when he spoke against Obama at this year's Republican National Convention. The right said that about Sean Penn when he spoke out against the war in Iraq during the Bush years. One side will always make the same dumb argument — celebrities need to shut up and entertain us — when the other side has a celebrity who makes its case. It's a lowest-common-denominator argument, plugging up our ears and saying we don't need to listen to some celebrity's views that are different than our own.
We complain that athletes and coaches are coddled millionaires even as we happily consume what they're selling. We complain about the media's obsession with Mark Sanchez's girlfriend or Lolo Jones' virginity even as we gobble up the tabloid fodder. Then when a prominent sports figure like Boeheim wades willingly into one of the most important, controversial topics of our day and makes us think about the real world when we're trying to escape with entertainment, we complain that he's stepping outside his role.
How about we think this way instead: In an image-conscious culture where actors are trying to sell their next movie and athletes are staying as controversy-free as possible so they can get that next shoe contract, we should praise someone like Jim Boeheim for standing up for something that matters.
Whether we agree with him or not.
Follow Reid Forgrave on Twitter @ReidForgrave or email him at email@example.com.
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Imran Aziz
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Manager Marketing Operations This is a fantastic career development opportunity for a Senior Account Manager or Senior Project Manager with digital experience. You will be accountable for the delivery of assigned multi-channel tactics, with a strong emphasis on digital (web/mobile). Manage day-to-day multi-channel tactics coordinate internal and external (vendors, agency partners) from concept to delivery. Accountable for the management of the Development, testing and launch phases of all projects. Oversee financial costings through to launch dates. Identify and implement improvement and innovation opportunities within the Marketing Operations functional area. Essential skills included solid digital implementation background, project/program management skills. Strong communication / presentation skills. Creative Recruitment is an equal opportunity employer and do not discriminate based on race, religion, gender, age, sexuality, gender identification, or physical ability.
Fixed Term Contract - 24 months This is an exciting opportunity to assume a key role in a cutting-edge organisation that will sit at the forefront of change across the humanitarian landscape. As our creative, tech savvy Digital Project Manager you will expertly progress the development and implementation of the Humanitarian Leadership Academy's digital presence. The mission of the HLA is to empower people around the world to prepare for and respond to crises in their own countries. The Academy will work with the humanitarian sector and new partners from the technology industry, private sector and universities to help communities become more resilient in the face of disaster and give them the training and skills to respond to crises in their own countries. In this pivotal position your core purpose will be to increase the HLA's reach and access through innovative and user-oriented digital platforms and solutions. You will be a crucial contributor to the Academy's digital strategy, gathering information, completing scoping work, generating ingenious solutions and building and managing partnerships. The post will afford an inspiring individual considerable scope to make their mark, effect change and impress. Key Responsibilities: ��Overseeing the successful implementation of specific digital projects. ��Providing central co-ordination of digital projects in the areas of Learning, Marketing and Communications, Knowledge and Evidence, and Academy Centres. ��Leading on the tendering and negotiating of agreements with suppliers, agencies and partners. ��Maintaining a resourcing plan and arranging the procurement of resources in a timely manner. ��Establishing and enacting a risk management plan. To succeed in the role of Digital Project Manager you'll need: ��Substantial project management experience, involving the development and implementation of digital functions on a comparable scale. ��Significant experience of commercial off-the-shelf business application services and products, and expert knowledge of software development lifecycles. ��A background solving complex digital function issues. ��The ability to develop successful relationships with corporate partners. ��Robust understanding of information security principles. We're looking for an innovative, confident and resilient individual, an outstanding decision-maker who's able to challenge conventional thinking / ways of working, and who can present complex digital issues in a simple manner. To thrive in this role you'll be an established communicator, impulsive networker and a natural collaborator. With your help we'll create excellence in the humanitarian sector - and prevent the suffering of millions of people who face crises worldwide. The Humanitarian Leadership Academy is the future of humanitarian response. Be part of the solution. Join us in preventing the greatest humanitarian crisis of our time. For further information about the Humanitarian Leadership Academy's aims please visit our website: www.humanitarianleadershipacademy.org To apply please visit the Save the Children website Closing Date: 3rd May 2015
Consumer Insight Manager (Qual and Quant) Propel are currently working with a huge publisher, who employ more than 10,000 people worldwide and generate annual revenues of around €3bn (£2.5bn). They are a true success story in the modern publishing field. They have taken their traditional roots into the modern market, giving their publishing assets the scale they need to compete with the growing challenges to their business in the digital era. They are now looking to grow their UK digital team and are recruiting across a number of positions. This is a great opportunity to join a huge publisher who have an outstanding creative and commercial track record, with a passion for publishing the best books both online and in print. The company has a great culture and you will have the opportunity to be supported by some of the best publishing minds in the world and given the resources to take on the digital industry. The role of Consumer Insight Manager: As Consumer Insight Manager you will report to the Head of Consumer Insight and work as part of the wider Consumer and Digital Department including Marketing and Product development. You will play an important role in supporting traditional and digital publishing and consumer-facing brands and activities. Leading and managing research into children's and youth brands, authors and digital products, you will also have responsibility for research into non-fiction genres, brands and titles. This is a newly-created role in a small but highly ambitious in-house team and they're looking for someone who can bring fresh thinking, ideas and enthusiasm and deliver impactful and inspiring insights. Key responsibilities: -Manage a range of qualitative and quantitative research projects - including panel surveys, commissioning and managing research agencies, running focus groups in-house, and using data from syndicated and industry surveys -Lead a programme of children's and youth research -Build relationships with and influence key stakeholders to use insights to drive publishing strategy Experience required of the Consumer Insight Manager: -Strong ad hoc quantitative and qualitative research experience from an agency or clientside role -Experience of liaising with and influencing stakeholders both internally and externally -Confidence in managing research projects from start to finish and advising on research methods -Great questionnaire design skills and an interest in engaging survey design -Ability to interpret research findings into commercial insights -Excellent PowerPoint and presentation skills Propel are the UK's largest independent provider of permanent and contract recruitment services to the global digital economy, specialising in commercial, marketing, technical and creative talent. Services advertised by Propel are those of an Agency and/or an Employment Business. Please be aware that we receive a high volume of applications for our roles, many from people who meet or exceed the requirements. Whilst we try to respond to as many applicants as possible, you can assume that you have not been selected for interview if you do not hear from us within 14 days.
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FOOD; Hearth Strings
By Jason Epstein
Published: January 18, 2004
I have two kitchens, one in Manhattan, the other on Long Island. Each has a table that seats six. The one in the city is made of stainless steel and was built by a shop on Grand Street that does sheet-metal work for Chinese restaurants. Normally the shop doesn't bother with individuals, but one of the owners had a young son named Jason and made an exception in my case. The table in the country is an old, somewhat battered reproduction of a round-topped table that might have been in use when the house was built in 1790 and was left behind by the previous owner when I bought the place in the 1960's. Until I was asked some years ago by a food magazine to write something about my kitchen I hadn't noticed what has since become obvious to me: these two kitchens bear a strong family resemblance, despite the different tabletops. It was as if I had designed them with a common ancestor in mind.
For years, between marriages, I seldom visited the other rooms in my Long Island house, spending most of my time in the kitchen reading in a comfortable blue chair beside an old brick fireplace. I had what, in retrospect, seems to have been an aversion to the bedrooms upstairs to which, I imagined, foolishly, my grown children might someday return. I slept in a room off the kitchen. Only when more than six people came to dinner did I use the dining room, reluctantly, and still do. The same is true in the city. I prefer to read and write in the kitchen and almost never sit in the living room. This part of the apartment has always seemed off limits to me, like the front parlors of Victorian houses with their rubber plants, stuffed birds and antimacassars. Only on the grandest occasions do we use the dining room with its gleaming Louis XV table surrounded by crammed bookshelves and old silver candlesticks made into pink-shaded lamps.
When I sat down in my city kitchen to write the article and looked toward the ceiling for inspiration, I noticed for the first time that the varnish that had been applied 10 years previously to the wainscoted walls and ceiling had by now turned the color of honey. I wondered how long it would be before these walls acquired the look of the ancient pumpkin pine of which the cabinets and countertops in my country kitchen were made. It was then, in a flash of memory, that I saw with sudden clarity the ancestor from which these two kitchens with their paneled walls and utilitarian Garland stoves derived: it was my grandmother Ida's kitchen in Maine, where I spent my most vivid, most pleasant and perhaps most formative childhood years.
My grandparents lived atop a steep hill in what seems to me in memory to have been a very large house whose rooms ran one into the other. There was very little central heating, so in winter, when frost formed sharp peaks on the heavy storm windows, everyone sat in the kitchen, warmed by the big wood stove with nickel trim and the words Model Home Fireside above a temperature gauge on the oven door. My own perch was to the left of the stove, atop a wood box painted robin's-egg blue, from which I could observe the activity around me: my grandmother in a faded apron and oxford glasses removing a towering apple pie from the oven; an aunt at the sewing machine between the windows, beyond which I could see the wind-whipped snow swirling against the blackness of the open barn doors and my friend Raymond, who would grow up to be a priest in Canada, trudging uphill on his wooden skis. Pete, the old English bulldog who once rescued me unnecessarily from a snowdrift by tugging at my sleeve, growling softly, was asleep on a blanket beneath the oven door. The walls were wainscoted from floor to ceiling like the walls of my present kitchen, but the varnish was older and mellower. The copper plumbing at the black slate sink must have been added later, since it was bracketed to the walls and groaned and rattled whenever the faucet was opened.
On summer days when the kitchen became uncomfortably warm and the fumes of melting tar on the road clutched at our throats, I would retreat to read in a large, cool pantry -- called the shed -- off the passageway that led to the barn. It was in this shed, with its remnant of winter cold, that my grandmother kept her preserves: shelves of peaches and green tomatoes, figs in syrup, pears, rhubarb and purple plums, eggplant, applesauce, cucumbers and peppers in shiny jars, which in memory I have conflated with my grandmother's gleaming oxford glasses and their gold chain. Old patchwork quilts were piled in a corner where Pete spent the night.
Whether my grandmother was a good cook I can't say, but she was said to be, and mealtimes, when the family gathered around the large oak table, were always a joy. I remember in summertime bowls of coleslaw, pickled cucumbers and green tomatoes, beet soup, platters of fried or roasted chicken, apple pies and, in winter, double brisket and braised root vegetables, breast of veal stuffed with Swiss chard, lamb braised slowly for hours.


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2012 Can-Am Spyder RS and RT — First Look
Rider Magazine
July 15, 2011
2012 Can-Am Spyder RT Limited in Lava Bronze Metallic Our soon-to-be-delivered September issue features a full test of the 2012 Can-Am Spyder RS-S, the sportiest of BRP's three-wheeled roadsters. Equipped with a 106-horsepower (claimed) 998cc Rotax V-twin, a state-of-the-art Vehicle Stability System... Read more »
2008 Can-Am Spyder First Ride
Rider Contributor
April 11, 2008
[This 2008 Can-Am Spyder First Ride was originally published in the April 2007 issue of Rider magazine] by Troy Siahaan The wild vehicle you see before you is the 2008 Can-Am Spyder. The wild vehicle you see before you is the 2008 Can-Am Spyder, which parent company Bombardier is using to bring the... Read more »