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A method and system that obscures image features in a region of interest designated by a user. An operator designates a special shaped region of interest and a computer convolves a specially designed kernel with the image data in the region. The kernel is designed to pull features outside the region into the region while combining the information within the region with the information pulled from outside. The kernel can be designed to correspond to the shape of the region for efficient computation. Once the region of interest is obscured the image is printed resulting in a print that does not include undesirable features.
1. A method of obscuring information in an image, comprising:
(a) obtaining one digital representative of the image;
(b) completely enclosing a region of the image having a plurality of pixels for obscuration so that the lines delineate the enclosed region;
(c) merging first image signal information that is from outside the region and that is also in the digital representation with second image signal information that is from inside the region and that is also in the digital representation by applying at least two convolution kernels around the perimeter of the region toward the center in discrete sequential concentric smaller layers, the result of each layer being merged with the next layer; and,
(d) obscuring the bounded region as a result of step (c).
2. A system, comprising:
an image source device providing one digital representative of an image;
an input/output device allowing a user to completely enclose a region of the image having a plurality of pixels for obscuration so that the lines delineate the enclosed region; and
a computer, coupled to said source device and said input/output device, processing the region of the image to obscure information in the region by merging signal information that is outside the region and that is also in the digital representation with signal information that is inside the region and that is also in the digital representation by applying at least two different convolution kernels around the perimeter of the region toward the center in discrete sequential concentric smaller layers, the result of each layer being merged with the next layer.
3. A method as recited in claim 1, wherein the region has a shape and the at least two convolution kernels has a shape corresponding to the shape of the region.
4. A method as recited in claim 1, wherein the at least two convolution kernels comprise an element for an image pixel of interest and elements closer to an outside edge of the region than the pixel of interest.
5. A method as recited in claim 4, wherein the at least two convolution kernels include elements that are a power of two.
6. A method as recited in claim 1, wherein the at least two convolution kernels comprise a first kernel for flat region areas and a second kernel for curved region areas.
7. A method as recited in claim 1, wherein the at least two convolution kernels are symmetric.
8. A method as recited in claim 7, wherein the at least two convolution kernels convolve a pixel of interest and exterior neighboring pixels.
9. A method as recited in claim 8, wherein the at least two convolution kernels convolve exterior neighbor pixels of an adjacent different layer.
10. A method as recited in claim 7, wherein the at least two convolution kernels are rotationally symmetric about a normal to a surface of the region.
11. A method as recited in claim 10, wherein the at least two convolution kernels provide the same response when symmetrically rotated when presented with identical data.
12. A method as recited in claim 9, wherein a weighting of the pixel of interest is 1/16th.
13. A method as recited in claim 9, wherein a weighting of the exterior immediate neighbor pixels is 4/16ths and a weighting of the exterior neighbors is 1/16th.
14. A method as recited in claim 1, wherein the at least two convolution kernels include a number of elements that are a power of two.
15. A method as recited in claim 1, wherein step (b) includes only addition and shift operations.
16. A method as recited in claim 1, wherein step (b) convolves information exterior to the region toward a central point of the region.
17. A method as recited in claim 1, wherein the region is a closed curve shaped to approximate an expected shape of a region of undesirable features.
18. A method as recited in claim 1, further comprising (c) printing the image.
19. A system as recited in claim 2, further comprising a printer printing the image including the obscured region.
20. A system according to claim 2 wherein the image source device is a camera or an image storage device.
This is a continuation of application Ser. No. 08/230,195, filed Apr. 20, 1994, now U.S. Pat. No. 5,710,839.
1. Field of the Invention
The present invention is directed to a system for automatically obscuring undesirable features in an image region which system combines image convolution and erosion to pull features outside the region into the region using a specially designed kernel, a shaped region of interest adapted to reduce artifacts while obscuring undesirable features and local information.
2. Description of the Related Art
Image processing techniques which improve the appearance of images are well known. Methods for the global reduction of image information in an image are also well known. For example, an image can be made less sharp by blurring the entire image with a convolution whose kernel elements are chosen to suit the particular need. Information describing an image can also be produced with the application of algorithms which compute various features within an image. For example, successive applications of erosion and dilation operations can be used to extract information pertaining to the structure of features within an image.
Techniques for the local enhancement of image information are also well known. Such techniques seek to improve the apparent quality of an image in a restricted area or attempt to derive information about an object in an image.
There are also many standard methods for removing image information which rely on operator interaction. Image editing software tools provide air brushing capability, cloning operations and the like to remove image features directly under the operator's artistic control. Such methods are not automated and all require the supervision of the operator.
Automatic operations which endeavor to effectively obscure portions of an image (indicated by an operator) while leaving the rest of the image unaffected are more difficult (and less sophisticated). Such operations must avoid destroying the overall appearance of the image or its image quality. At the same time, the obscured image portion must not be obvious to the eye and, when more closely examined, should appear as a normal part of the image but without the undesirable features.
What is needed is an automatic operation that merges information outside a region with information inside the region to obscure undesirable features while retaining some of the characteristics of the information in the region and being pleasing to the eye.
It is an object of the present invention to provide a method for the local obscuration of objectionable features within an image without degrading the overall quality of the image, creating obtrusive artifacts or creating perceptible discontinuities within the image.
It is another object of the present invention to allow a user to designate regions of an image which include undesirable information and to process the regions to obscure the undesirable features in a way pleasing to the eye.
It is also an object of the present invention to provide a system that uses a shape of a region to be obscured that improves the perceived quality of the final image by reducing processing artifacts.
It is a further object of the present invention to provide a kernel that improves the efficiency of the obscuration process.
It is an additional object of the present invention to merge information outside a region with information inside the region to obscure undesirable features in the information in the region.
The above objects can be attained by a method and system that obscures image features in a region of interest designated by a user. The process includes designation by an operator using a computer of a special shaped region of interest and the automatic convolution by the computer using a specially designed kernel with the image data. The kernel is designed to pull features outside the border of the region into the region making the region blend naturally with the surroundings while not completely eliminating the image details within the region. The kernel is also designed for efficient computation requiring only adds and shifts. The kernel is applied iteratively from the border of the region to the center. Once the region of interest is obscured the image will not include undesirable features and can be made available for viewing or printing.
These together with other objects and advantages which will be subsequently apparent, reside in the details of construction and operation as more fully hereinafter described and claimed, reference being had to the accompanying drawings forming a part hereof, wherein like numerals refer to like parts throughout.
FIG. 1 depicts the typical hardware components of the present invention;
FIG. 2 depicts the steps of the process of the present invention;
FIG. 3 illustrates an image to which the invention is to be applied;
FIG. 4 depicts an area being processed;
FIGS. 5A-5D illustrate kernels applied to the image of FIG. 4;
FIG. 6 depicts a first processing level;
FIG. 7A illustrates a kernel applied to the image;
FIG. 7B depicts processing results;
FIG. 8 illustrates a first level of processed results;
FIG. 9 illustrates a second level;
FIG. 10 depicts a final level;
FIGS. 11-13 depict different final levels;
FIGS. 14A-14D depict reduced kernels;
FIG. 15 depicts an elliptical region of interest;
FIGS. 16A and 16B illustrate the image region digitally;
FIGS. 17A-17D and 18A-18D illustrate kernels applied to the elliptical region;
FIG. 19 depicts pixels to be processed;
FIG. 20 illustrates the results of processing the pixels of FIG. 19;
FIG. 21 depicts the procedure for selecting kernels;
FIG. 22 illustrates the processing results for the entire first level;
FIG. 23 illustrates two levels of processing;
FIG. 24 illustrates three levels of processing; and
FIG. 25 depicts all pixels processed.
The present invention as illustrated in FIG. 1 includes a camera 10, such as a the Eastman Kodak Co. DCS200 camera, which captures an image 12 of about 4 megabytes that includes features that are undesirable. Although a camera image source is shown the image source could be other devices such as an image storage device like a compact disk. The image 12 once it is received is stored in a computer 14, such as the SUNSPARK workstation from Sun Microsystems or the Kodak EktronBOSS processor having matrix process accelerators, and used to drive a display 16. An operator 18 through a conventional input process and device 20, such as a mouse, designates the location and size of a region of interest in the image 12 in which details are to be obscured. The region designation is stored in a location 22 of the memory of the computer 14. Once all the regions that need-to be obscured within the image 12 are designated, an obscuration process 24 using the region designation 22 and a kernel 26 automatically performs the obscuration using a conventional convolution technique in which the kernel is iteratively applied from the outside to the center to obscure the details in the image 12 in the region of interest. Once the image processing is complete the image 12 may be viewed on the display 16 or output to an output device 26, such as a printer like the Kodak XL thermal printer.
The iterative method mentioned above provides for the efficient obscuration of image features using a related family of two-dimensional kernels which are applied to the border of the image feature area first and then successively moved to the center of the image feature area. The kernel combines information from the border of the obscured area with information within the obscured area. The construction of the kernel is such that the application of the kernel to the image is extremely efficient computationally. The features of the kernel which improve efficiency and image quality include: making all element values a power of two, so that only shifts (instead of multiplies) and adds are required; making the sum of the kernel elements a power of two; pixels adjacent to the target pixel at the same level not contributing to the obscured pixel element values; requiring a minimum number of shifts; the kernel shape matching the shape of the region being obscured; tracking the position of kernel elements with respect to data is simple; minimizing the number of intermediate copies of data by providing that the original can be overwritten; and supporting extensive parallel processing.
The automatic method for obscuring image features of the present invention does so without degrading the overall image quality, creating image artifacts, or creating perceptible discontinuities within the image. The method, as illustrated in FIG. 2, can start with the capture 40 of an image which includes one or more undesirable features. The only operator 18 input then required is to designate or indicate 42 a region of interest requiring obscuration. A simplified, artificial image 80 is shown in FIG. 3. The feature which will be obscured within this image is the ball 82 in the hand 83 at the end of the left arm 84 of the human FIG. 86 on the right side of the artificial image 80.
The area to be obscured is selected as being within a rectangle 88 by the operator 18. Such a rectangle is shown in FIG. 3, is drawn with a dotted line, and completely surrounds or encloses the area to be obscured. Once the area of interest is selected, the operator's task is complete and the processing proceeds automatically with the application 44 of the kernel to the image.
The area to be obscured can be digitally, rather than pictorially, represented by FIG. 4. In this figure, the background has pixel values in the zero to ten range, the hand 83 has values in the 40 to 70 range, and the ball or circle 82 has values in the 130 to 160 range. The area within the dark bordered box 90 is to be obscured.
Four separate kernels are used to obscure the region of interest each with a rectangular shape matching the rectangular shape of the region. Each kernel is used on one of the four sides of the rectangular region of interest. Alternatively, the kernels can be considered as the same kernel but rotated when applied to each of the four sides. That is, each kernel is symmetric inside the kernel about a normal to the surface and each kernel is rotationally symmetric. That is, if the kernel is rotated and applied to the same data the same result will be produced. The four kernels are shown in FIGS. 5A-5D. Each kernel includes an element for the pixel of interest and for pixels exterior to the pixel of interest and exterior to the current level and which are considered complete or have had their processing, if any completed. The obscuration process proceeds iteratively, from the outside layers, or levels, of the region-of-interest to the inside. The first level, as illustrated in FIG. 6 is composed of the rows and columns touching the inside of the dark border and is the first to be processed. The second level is the rows and columns just inside the first level and so on. Each level is processed before the next, although some computations may be performed concurrently where there is no arithmetic dependency and the image can be divided into tiles for processing by different processors when multiple processors are available. The process applies 44 the kernel to the data which can be a conventional convolution operation or a specialized operation that takes account of the characteristics of the kernels as discussed below. The results are stored 46 in the original matrix and the kernel is moved 48 by incrementing the pointers. If the process is not operating on the sides in parallel as would occur when parallel processors are involved, the process determines whether 50 a corner has been reached and whether 52 the last corner has been reached. If the last corner has not been reached 50 the kernel is rotated 54 and applied 44 again. When the last corner has been reached the corners may need to be processed 56. If the level is not the last level 58, the kernel is moved 60 down a level and level processing starts again. If the last level has been reached special final center processing may need to be performed 62, the image is complete 64 and may be viewed or printed. Because the length of each side is gradually shrinking and the corners are moving inward the process must keep track of the processing position as well as the level. A person of skill in the art can provide the appropriate pointers and pointer adjustments for this.
As noted above, the first level is processed first. Every pixel in the level can be processed independently of every other so that some degree of parallel processing is simple to obtain, even though the process steps discussed above process the sides sequentially. During processing along the top row (excluding the corners), each pixel in the first level is replaced by the convolution of the pixel area with the top kernel shown in FIG. 7A. This operation is shown in FIG. 7A for the pixel whose value is 5 and is located in row 3 column 4 (starting the count from top row and left most column). To clarify the illustration, the interior levels and corners are not shown. The darkly bordered box is shown enclosing the pixels whose convolution coefficients are non-zero. Thus, the resulting pixel value is ((2×1)+(5×1)+(1×4)+(2×4)+(3×4)+(4×1)+(5×1))/16=2.5 or more simply because of the design of the kernel ((2+5+5+4)+(1+2+3)×4)/16=2.5. This rounds (for integer-valued pixels) to 2. The result is shown in row 3, column 4 of FIG. 7B. The results for the rest of the beginning of that row are shown as well as the results for the beginning of the column. All of the new pixel values are shown in a bold font.
The corner pixels can be computed by averaging the results of the convolutions using the two relevant kernels. Thus the upper, left corner pixel value in the first level shown here will average the result of the top kernel for that pixel location (result is 3) and the result of the left kernel for that location (result is 5). The average value is therefore 4. As an alternative and for a faster computation one of the kernel pixels can be arbitrarily chosen, for example always choose the top and bottom kernel results for the corners. However, since the kernels pull information from different areas of the image the final result will be different though generally the difference would not be noticeable. FIG. 8 shows the overall result for the calculation at level 1. The pixels in level 1 (between the dark lines) have been replaced, all others are left alone.
After level 1 has been computed, level 2 pixels can be computed. The level two pixels are shown in FIG. 9 between the dark lines. These pixels are replaced in exactly the same manner as for level 1. This procedure repeats until one level remains. If, as is shown in FIG. 10, there are an even number of pixels in the center of region of interest, a two-by-two area of pixels will remain at the last level. Each of these four pixels can be treated as a corner in exactly the same way as before. The resulting, completely processed region of interest is shown in FIG. 10.
The final level shown in FIG. 10 will occur when the region of interest is square and has an even number of pixels in each dimension. If the area is square but has an odd number of pixels in each dimension, the last level will contain a single pixel as shown in FIG. 11. (FIGS. 11 through 14 show only the last few levels of a larger region of interest.) In this case, the central pixel (originally 19) can be replaced by the average of all four kernel operations (top, left, right and bottom). In this case, the value of the top kernel is 19, the value of the left kernel is 22, the value of the right kernel is 21, and the value of the bottom kernel is 23, for an average value of 21. The pixel value of 21 will thus replace the original, central value of 19. For a faster computation as an alternative the pixel for one of the kernels can be arbitrarily selected as previously mentioned.
If the region of interest is not square, but is rectangular the final level will be either a pair of lines or a single line depending on whether an even or odd number of lines or columns are included in the region of interest. The case for a pair of lines is illustrated in FIG. 12. In this case, no special procedures need to be invoked, since normal operation will reduce the final level. In the latter case, FIG. 13, a special procedure, analogous to that of FIG. 11, must be used.
In the case of a remaining line for the last level, the end pixels can be replaced with the average of the top, left or right, and bottom kernels. In the FIG. 13 example, the results are 21, 17 and 19 for the top, left and bottom kernels respectively, so the replacement value would be the average of those three values, or 19. On the right side, the top, right and bottom kernels would be applied, and the average of those three results used. For the pixels not on the end, the top and bottom kernels would be applied and their results averaged. For example, the pixel value 29 which is second from the left in the last level of FIG. 13 has a top kernel value of 23, and a bottom kernel result of 24. The average result is then 23.
FIG. 13 illustrates the case in which the horizontal dimension of the region of interest is greater than the vertical, resulting in a horizontal line for the last level. If the vertical dimension is greater than the horizontal, a vertical line results. This case is treated in a similar way. The resulting end pixels are averaged from the left, right and top or bottom kernels. The remaining pixels are the result of averaging the values derived from applying the left and right kernels.
The procedures described above are very computationally efficient on binary, digital computers. No multiply or divide operations need to be done, only shifts and adds. Tracking the location of the pixel arguments for the kernels is more work, but the total is much less than required for a conventional convolution, for example. In addition, the special cases FIGS. 11 through 12, only pertain to regions of interest with odd dimensions. If the regions of interest are restricted to even row or column sizes, the special cases do not obtain and further simplification can be achieved.
If an image has a lot of high frequency detail, the procedure outlined above can result in a perceptibly visible border around the obscured area. This can be reduced by using the slightly smaller kernels shown in FIGS. 14A-14D. These kernels are effectively lower powered versions of those previously described. By applying them in the same manner at the first few levels of the region of interest, a more gradual, less perceptible edge is obtained. The number of levels for which the smaller kernels are used is image or application dependents Note that the smaller kernels exhibit even greater computational efficiencies than the larger ones, since fewer adds and shifts are needed to implement them.
The iterative use of these kernels in the manner described above effectively obscures image information without creating objectionable artifacts. This occurs because the method as described combines some information from within the obscured area with blurred information from the edge of the area while not pulling information from pixels beside or on the same level. As the iterations proceed, the edge information is pulled into the region of interest. Thus, the effective obscuration increases as the iterations proceed providing a smooth transition from the relatively unaffected border regions to the interior of the obscured area. There are three simultaneous activities occurring. First, the local pixels are blurred as with a typical convolution in which adjacent pixels at the same level are combined; this effectively reduces the image detail. Second, information from within the region of interest is incorporated into the obscured pixels; this reduces the perception of artifacts within the obscured area. Third, information from the edge of the region of interest is moved into the obscured area, effectively providing structural continuity for the image as well as covering the objectionable image information. In addition, information from the outer reaches of the region is dragged into the center or middle.
Examination of FIG. 10 will show that the detail has been effectively removed while some structure remains without creating a clear edge in the processed area. Note that a digital representation of an image area cannot be used to provide a visually accurate representation of the actual image processing.
The present invention can be improved by applying a differently shaped region of interest to the image along with kernels shaped to match the change in the shape region of the region of interest. A second simplified artificial image 100 used to depict improved region of interest is shown in FIG. 15. The feature which will be obscured within this image is again the ball 82 at the end of the human figure's left arm 84 on the right side of the artificial image. The area to be obscured is selected within an ellipse 102 by an operator. The ellipse 102 completely surrounds the area to be obscured.
The elliptical area to be obscured can be digitally, rather than pictorially, represented by FIG. 16A. In this figure, the background has pixel values in the 0 to 10 range, the hand has values in the 40 to 70 range, and the circle or ball 82 has values in the 130 to 160 range. The area within the darkly bordered digitized ellipse 102 is to be obscured. To clarify the illustration pictorially, FIG. 16B adds a light background to each pixel area for the hand, and a darker background shade for each pixel of the circle or ball 82.
Eight separate kernels are used to obscure the region of interest each with a shape corresponding to the shape of the region with which it is used, thereby drawing information into the region from the appropriate direction. The kernels are "V-shaped" recognizing that the kernels are to pull from regions exterior to the curved surface of the ellipse 102. Each kernel is used on one of the eight possible regions of interest for the pixel configurations. Note that these kernels can also be applied to a region of arbitrary shape by merely sharpening the "V" as the associated region curve gets sharper and broadening the "V" as the region curve gets shallower until the flat kernels of the previously discussed embodiment are produced. This will of course require fewer or more kernels for the curve depending on its shape and may increase processing overhead. The eight kernels for this particular ellipse 102 can be divided into two groups, one of which comprises edge kernel reflections as illustrated in FIGS. 17A-17D, and the second of which comprises corner kernel reflections as illustrated in FIGS. 18A-18D. These kernels are all normalized by dividing by 16 as in the previous embodiment.
Every pixel within the area to be obscured must be replaced with a new pixel value. As in the previous embodiment, the obscuration proceeds iteratively, from the outside layers, or levels, of the region-of-interest to the inside. The first level is composed of the pixels contiguous with the inside of the dark border and is the first to be processed. The second level is composed of the pixels just inside the first level and so on. Each level is processed before the next (although some computations may be done at the same time if there is no arithmetic dependency as in the previous embodiment).
Every unknown pixel with three known pixels on the line above, where one pixel is directly above and the other two are on either side of the one above, can be calculated using the kernel of FIG. 17A. Likewise, every unknown pixel with three known pixels on the line to the right, where one pixel is directly to the right and the other two are above and below the pixel on the right, can be calculated using the kernel of FIG. 17C. Every unknown pixel with known pixels above, to the right, and diagonally above and to the right can be calculated using the kernel of FIG. 18B. The corresponding situations for the upper left, and lower right and left quadrants are similar, using the appropriately reflected kernels. It is possible that more than one kernel may be usable. In that case, either kernel may be chosen. (The corner kernel is chosen here.) Alternatively, both convolutions can be calculated and the average used as in the previous embodiment.
Note that not every pixel contiguous with a known pixel can be calculated on a given level of processing. The appropriate neighborhood of known pixels must first exist. However, as the erosive process continues iteratively, every pixel will eventually be filled in.
At any one, level, only the pixels whose kernel elements are combined with new pixels can be modified (except for the pixel of interest at the center of the kernel). Pixels whose kernel elements are zero are ignored. New pixels (those whose new values are already determined) are those which are not in the elliptical region of interest 102 or ones replaced at a previous level calculation.
The various configurations for kernel application are illustrated in FIG. 19 for pixels in the upper right quadrant of the elliptical region of interest. The pixels and their positions are indicated with square boxes as in the previous figures. Boxes filled with an 0 represent known or previously modified pixels. Boxes filled with an x represent pixels that have not yet been modified. Boxes filled with a bold r indicate pixels that have not yet been modified but that can be modified given the existing known and modified pixels.
Empty boxes are at the edge of the area and are ignored. As with a convolution, a neighborhood of pixels is required for this calculation; those pixels without a defined neighborhood must be treated separately. Those pixels are not considered here and may be filled according to some other scheme or simply left with their existing value. Generally, however, this problem does not arise because the area to be obscured is not at the very edge of the image data. Given this, every pixel in the area to be obscured will have a neighborhood.
FIG. 20 completes the FIG. 19 example using pixel values from FIG. 16A. The r boxes have been filled using the convolution values from the appropriate kernels. The original pixel in the upper left r box location 104 is 2 (from FIG. 16A). Thus, the upper left r values in FIG. 19 is computed using the top kernel, so that the resulting pixel value is ((6×1)+(2×1)+(3×1)+(4×4)+(5×4)+(1×4)+(2×1))/16=3.3125 or more simply because of the design of the kernel ((6+2+3+2)+(4+5+1)×4)/16=3.3125. This rounds (for integer-valued pixels) to 3. Where either a corner or side kernel could be used, an arbitrary choice is preferably made to apply the corner kernels.
Because the digitized ellipses are quadrant symmetric, the same kernels are applied at the same relative points for each quadrant. This reduces the computation required to select the appropriate kernel. (Thus each computation to select a kernel supports the calculation of four new pixel values.) To apply the appropriate kernels to the current level pixels, the following procedure of FIG. 21 can be used for a single quadrant. FIG. 19 illustrates the quadrant in which the procedure is applied. The processing starts 110 with positioning the kernel at the top center or at pixel 104 of FIG. 19. The top kernel is applied 112 and a test 114 is performed to determine whether the pixel to the right is complete, that is, has the pixel to the right already been processed. If not, the kernel is moved 116 to the right and the right pixel is again tested 118. If the right pixel is not complete then the edge is horizontal and the top kernel is again applied 112. If the right pixel is complete the upper right kernel is applied 120 because an inside corner has been detected. Next, the lower right pixel is tested 122 and if it is not complete, the pixel 124 is moved to the lower right. The system then tests 126 the right pixel attempting to identify another corner or a horizontal edge. When the lower right pixel is complete (122), the system tests 128 the pixel below and if it is complete the level processing is finished 130. If the below pixel is not complete, the kernel is moved 132 down. Next, the lower right pixel is tested 134 to look for a vertical edge. If the lower right pixel is complete a vertical edge has been detected and the right kernel is applied 136. See FIG. 20 for the results. To start the next level the kernel is moved back to the top center. Note that this procedure can be applied to the remaining quadrants by simple rotation of the pixels tested. For example, if the lower left quadrant is to be processed step 112 would apply the right kernel and step 114 would test the pixel below. Note that the actual convolution must be done individually for each pixel, since, even though the kernel is the same (although rotated) for each of the four pixels, the pixel values are not the same. FIG. 22 shows the result of applying the appropriate kernel to one entire level of the selected area of FIG. 16A.
After each level is calculated, the next level can proceed. Every pixel in a level can be calculated at the same time or in parallel as in the previous embodiment. If the pixels are processed or calculated in parallel, for each pixel a test is performed to see if all pixels corresponding to the non-zero kernel elements are complete. If so, the pixel of interest is on the current level and may be calculated. If not, the pixel must be replaced at a later date. If they are calculated sequentially, as was illustrated for the first embodiment in FIG. 2 and in FIG. 21, the pixels on the next level may be calculated if the required complete pixels exist or have been calculated. FIGS. 23 and 24 show the second and third level results of the method applied to the selected area of FIG. 16A. Note that the shape of the ellipse is converting into a rectangle which reduces the number of kernels which are used finally reducing to just top, bottom and side kernels.
At some stages, especially the final ones, more than one kernel will be valid for the convolution. When this occurs, either an arbitrary choice between kernels may be made or the average result of all of the valid kernels may be used as in the previous embodiment. This will also happen if a single pixel, row of pixels, or column of pixels remain. For example, if a horizontal row of unknown pixels remains, both the top and bottom kernels can be used and the results of the kernel convolutions may be averaged. FIG. 25 shows the final result of the method as applied to the selected area of FIG. 16A. To improve the clarity of the example, averaging is not done and the corner kernels are chosen in preference to the side kernels. The upper right kernel is attempted first.
All of the convolutions described above with respect to the second embodiment are also very computationally efficient on binary, digital computers. Once again no multiply or divide operations need to be done, only shifts and adds. Finding out which kernel to apply does require more work, as well as tracking which pixels need to be calculated. This can readily be done by maintaining a template of the region of interest which contains values indicating the status of each pixel. Additional work is necessary to support averaging. All of which is within the skill of the art. The elliptical approach does require more overhead than the rectangular approach, but the visual results are superior.
The iterative use of the kernels of FIGS. 17 and 18 in the manner described above effectively obscures image information without creating objectionable artifacts. Using an elliptical region of interest and associated appropriately shaped kernels, while requiring slightly more work computationally than using rectangular regions of interest, provides superior results. The elliptical approach maintains all other advantages of the rectangular region of interest approach.
This invention provides an automatic method for the efficient obscuration of image information without creating image artifacts that are readily perceptible to the viewer. The procedure does not require any user interaction once the region to be obscured is defined. The resulting image information lacks the objectionable image detail, but incorporates image structure from within and without the obscured area. The obscuration at the edge of the region is very small, while the effective level of obscuration is larger within the interior. Thus, the area is effectively obscured but does not create objectionable discontinuities within the image. The approach is very computationally efficient, requiring only additions and arithmetic shift operations. The use of an elliptical region of interest provides superior results to an approach using rectangular regions of interest while maintaining all of its advantages with only a slight increase in computational cost. The elliptical region of interest approach described herein is superior in that the borders created are not linear (either vertically or horizontally) and thus are less perceptible to the human visual system. The elliptical approach is also superior in that the regions described tend to be more natural and less artificial. The elliptical approach is also superior in that there is a reduced tendency to create diagonal artifacts within the obscured area.
The shape of the region used for processing can be arbitrary as long as it is a closed curve and the kernels of FIGS. 17 and 18 modified according to curve sharpness can be used to process the region. Thus, it is possible for the user to outline the area to be obscured using conventional drawing techniques and have only that limited region obscured.
The present invention has been described with respect to a particular process step order of sequentially marching around the region of interest using appropriate kernels, detecting when all pixels of a level have been processed and moving in a level until all pixels have been processed. It is of course possible that other approaches can be taken, such as processing in parallel all at once by testing non-zero kernel element pixels for completeness.
The many features and advantages of the invention are apparent from the detailed specification and, thus, it is intended by the appended claims to cover all such features and advantages of the invention which fall within the true spirit and scope of the invention. Further, since numerous modifications and changes will readily occur to those skilled in the art, it is not desired to limit the invention to the exact construction and operation illustrated and described, and accordingly all suitable modifications and equivalents may be resorted to, falling within the scope of the invention.
______________________________________Parts List______________________________________ 10 Camera 12 Image 14 Computer 16 Display 18 Operator 20 Input Device 22 Region 24 Obscuration Process 26 Printer 40-64 Process Steps 80 Image 82 Ball 83 Hand 84 Arm 86 Person 88 Region 90 Pixel 100 Image 102 Region 104 Pixel 110-136 Process Steps______________________________________
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Cameras set up in the bathroom, I let him go at it. Cy is one of those guys who's turned on by being watched a little more than the average guy, so he spent a some time eyeballing the lenses pointed at him, clearly enjoying himself.
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The Rays are in the postseason and they are battling for home-field advantage in the AL playoffs, but third baseman Evan Longoria and ace left-hander David Price wonder if the team's fans realize it.
Monday's game at Tropicana Field drew only 12,446 fans -- the fourth-smallest crowd of the season, according to the St. Petersburg Times. After the game, Longoria called the attendance "embarrassing" and "disheartening."
"We go out there and play hard for 162 games," Longoria told the newspaper, "and for the fans to show the kind of support they're showing right now, you kind of wonder what else you have to do as a player."
Price echoed those comments on his Twitter page: "Had a chance to clinch a postseason spot tonight with about 10,000 fans in the stands ... embarrassing."
Rays management responded Tuesday by announcing that it will give away the approximately 20,000 tickets it hasn't sold for Wednesday night's home finale against the Baltimore Orioles.
"It was something we had discussed, but I don't think we would have (done this without their comments)," Rays president Matt Silverman told the Times, "and it's not about the two players, it's about the sentiment expressed by the team throughout the year, the energy that they get from the fans when this place is full.''
Silverman also said he was hopeful fans would come out to celebrate another trip to the postseason if the Rays clinched Tuesday. Tampa Bay did nail down a playoff berth, beating Baltimore 5-0 behind Price's pitching. The attendance was announced at 17,891, less than half the stadium's listed capacity of 36,973.
The Rays have had just five sellouts at the Trop this year -- opening night; a three-game series against the Yankees when Alex Rodriguez was one home run away from 600 for his career, and Aug. 28 against the Red Sox. When the Times asked Silverman if the giveaway will produce sellout No. 6, he replied: "The fact that you're asking that question speaks to the challenges that we face. We don't know, and we'll see tomorrow. I'm hopeful that we'll have a packed house.''
The team will make the tickets available on a first-come, first-served basis at Tropicana Field beginning at 4:45 p.m., about 2 1/2 hours before the scheduled 7:10 p.m. first pitch, the Times reports.
With one home game remaining, the Rays are averaging 22,850 fans per opening. That ranks 22nd in the majors and is a reason why principal owner principal owner Stuart Sternberg has announced that the team's payroll will drop in 2011.
"I don't think there's any more time for rationalizations. We figured if we have a chance at the beginning of September then maybe the fans will come, and now it's the end of September and it's almost October and we're still kind of looking up in the seats going, 'Where is everybody?' " Longoria told the Times. "Obviously we want to bring a championship to Tampa. And we'd like more than 12,000 to 15,000 to know about it."
MLB Network analyst Mitch Williams, a former major league closer, was even harsher. Said Williams: "This team, these players, they absolutely deserve everything they're getting. That fan base does not deserve a World Series. It does not deserve to have a World Series in their town. ... It is embarrassing. ...
"As fast as Carl Crawford is, is as fast as he's going to run out of Tampa (as a free agent in the offseason). He wants to go somewhere where they care about their team, the game, and in Tampa they don't. ... I feel so bad for these guys. These guys are doing it naked, alone, by themselves. They don't have adrenaline that you draw from a crowd."


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(Mathew Rakola)
I hadn't spent nearly as much time on a bike as I could or probably should have since that halcyon day in 1987 when the state of Arizona awarded me my first driver's license. But I was intrigued by something Cat has been claiming for years: that her running (and more recently cycling) not only expanded her appetite (that seemed obvious) but also enhanced it, so that, post-exertion, food tastes demonstrably better, as though all that pumped oxygen inflated one's taste buds or some such physiological voodoo. With this trip, I figured to do some semi-scientific verifying.
Within minutes of our afternoon arrival at the MacPherson's Foster Harris House B&B, located roughly an hour southwest of Washington, DC, in the much tinier Washington, Virginia (populate 135), we were donning our spandex peels for what was billed as a 22-mile shakedown ride. (I confess this was a new usage for me, my previous definition having had something to do with blackmail.) Foster Harris is a friendly old Victorian farmhouse, and, unlike most B&Bs of my experience, it isn't overstuffed with saccharine antiques and festooned with crazed blooms of doilies. Another distinction: Most B&Bs lack the charming feature of a garage devoted exclusively to bikes.
There we found John outfitting the four other couples in our group (the tour maxes out at 10). Three of the guests were in the medical profession and cast wary glances at the plastic boot cast on Cat's leg. ("No worries!" she exclaimed. "I'll take it off to ride.") Another member of the group worked for the CIA, and therefore saw right through my spandex ruse, instinctively profiling me as a gluttonous interloper. "Stick with me," he said, an instant mentor.
The Tour d'Epicure's course is comprised of a varying array of loops tailored to each group's ambitions and abilities. Our first afternoon's ride would be about 18 miles to the Copper Fox Distillery, for a tour of the facility, then 4 miles back to the Foster Harris House to suit up for dinner. Cat and I each rode a Giant OCR 2 from John and Diane's fleet, and I can lodge no complaints about the road bikes save for the lack of a handy accessory for toting whiskey barrels. (I should note that Diane rescued me from that dilemma by tossing the little barrel into the tour's sweeper van.) In a churlish mode, I could snivel about the hills there in the foothills of the Blue Ridge, but then it's always unwise to pick fights with geology (as with horses).
Rappahannock County is rife with ridges and valleys formed by a few million years of sedimentary rock folding and faulting in a concentrated ploy to wreak lactic-acid havoc on the hams of future cyclists. After the first hour or so, however, I was surprised to find myself almost enjoying the hills, or at least developing a weird affection for them. Given my long interludes away from the bicycle, I considered each hill crest to be a small but significant goalpost, and the reward for achieving the goal was a sweet, air-conditioned downhill glide that felt luxurious and childlike, the 35-mph downhill yang to the grueling uphill yin—an agreeable strain of bipolar disorder, I decided. There's a metaphor for life in there but I don't wish to overdo things.
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Member Since: 07/27/09Send yuhannna07 a Message Give yuhannna07 a Gift Add yuhannna07 as a Friend
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yuhannna07: Friends
- Chris_Cosgriff
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- Fireman_Dave
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yuhannna07: Quiz Results
What is your Firefighter IQ? taken almost 6 years ago
76-100% correct
yuhannna07: Gifts
Yuhanna Muhammad
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- Columbia, SC
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Making a civilians arrest will probaly cause more unwanted paper work and SOP
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Oh Ok dats wuzup So is this site like the myspace for fire fighters lol
about 6 years ago
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whats up man, thanks for the add, nah ive never played for them, but im a big fan....
im with 3 department
Thanks for the add
i here that
thank you.1 day off working a round the house.the next 2 day i will be at work
I love it............. over 22+ years worth & a million miles all over North America. No tickets or accidents either-Thank God, but saw plenty. I love skating & teaching as well have over 30+ years in that to both in Canada & the U.S. I wish you the best in your many adventures. Take Care & Have a Safe & Great Week. Keep in touch. Teresa ^.,.^ ^.,.^
Thanks for the friendship. Please stay safe take care & hope you have a great week. Welcome to Fire Link, it's an awesome site
I am currently i a Fire fighter with the Columbia fire department i enjoy my job, and i am working on my associates degree in Fire Science. I will be finished in October of 2010. I am looking for some part time work to utilize my new degree if you have any information please contact me (678-853-3038) I am really interested in becoming a fire marshall please any information contact me or email me at firstname.lastname@example.com


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Event Details
07/27/2013Location: Floyd Bennett Field, Brooklyn, Aviator Sports Complex Time: 11:00 AM to 12:30 PM Fee Information: free Contact Name: Ryan Visitor Center Contact Phone Number: (718)338-3799
Families can try out a mini-epic bicycle ride with a Park Ranger on a loop across Floyd Bennett Field and back along the Jamaica Bay Greenway. Reservations are required, call (718)338-3799 for more information and to make your reservation. (2 miles) Bus: Q35.


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Goats Performing Star Wars Imperial March Theme For the Win
Anytime you hear the 'Imperial March', you automatically think Darth Vader is about to enter a room, right? Well, 'Star Wars' fans, prepare yourself for a different twist on the theme which is performed by a bunch of screaming goats. Yes, these are the goats you are looking for. And they are hilarious.


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News Releases by Email
April 01, 2014
Once released, mallards pose a serious threat to Florida's native wildlife.
Many of Florida's recreational sport fishes move into shallow waters to spawn, making them more available for anglers to catch.
FWC Facts:Adult bottlenose dolphins are typically 6-12 ft. long and live both inshore and offshore along temperate and tropical coasts worldwide.
Learn More at AskFWC
Get FWC News Feeds
Florida Fish and Wildlife Conservation Commission • Farris Bryant Building
620 S. Meridian St. • Tallahassee, FL
32399-1600 • (850) 488-4676
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FMU faculty interested in arranging a Travel Course--that is, a travel component for a course they will be teaching--should visit the link to the right to review a few do's and don'ts, and then fill out a Travel Study Course Proposal form and return it to the Coordinator of International Studies for approval by the International Studies Committee.
Faculty advisors with students who have expressed an interest in travelling abroad will want to read the following and encourage the student to visit the Coordinator of International Studies or the particular liaison/faculty sponsor.
Overview of the Exchange Programs
FMU's Exchange Programs are an easy way for students to spend a semester abroad and earn credit toward graduation without necessarily spending more had they stayed at FMU. If a student receives Financial Aid, most of that aid package will apply to an Exchange experience. Note that only students who have completed at least 30 semester hours with a gpa of at least 2.7 may apply for the Exchange Program.
Some of our Exchange Programs are intended for students with particular majors or needs:
CANADA (University of Northern British Columbia): Open to most majors except those in the Professional Programs (e.g., Nursing, Education or Business).
ENGLAND (DeMontfort University): Students in English and Mass Communications can take more advanced courses. Others can take courses in the Journalism, Film Studies or an array of Humanities courses. Per English traditon, students typically (though not always) write papers rather than sit for an exam.
ESCEM: JUNIORS AND SENIORS ONLY! Students must have taken a full year of Business courses before applying and be accepted by School of Business Programs as majors (or minors). French language skills are not necessary, but helpful.
CAEN: French Language School. Students should be taking classes in the French, though need not be French majors.
France requires a visa for all students. Once students have received acceptance letters from the Host Institution they must apply to Campus France (http://usa.campusfrance.org/en/1.html). When they receive their letter from Campus France they must make an appointment with the Embassy in Atlanta to procure a visa.
IRELAND: a wide array of courses in liberal arts and sciences, open to most majors.
GERMANY: Because of the German academic calendar, we typically send students only during the spring or summer. Students wishing to study in Germany during the fall should consider applying for a full year.
TRIER: German language skills highly recommended as almost all classes are taught in German--although there is a Canadian Studies program offered through Trier's English Department that offers some courses in English. Such a course may count toward a student's literature elective or English major at FMU.
SCHMALKALDEN: Courses in the Business School are taught in English. The Spring semester starts about March 21 and exams begin in July.
MEXICO: intended for students studying Spanish language and Mexican culture. Note that owing to US Department of State travel advisories, FMU's exchange program in Mexico has been temporarily suspended.
There are several reputable and established private organizations offering students foreign exchange opportunities. Some of these are listed under "Non-FMU Programs" in the column to your left. Note that a faculty advisor can neither approve nor disapprove such an exchange, but may counsel the student about ensuring transfer credit, safety and health insurance. The Coordinator of International Studies (Jon Tuttle, FH 146) will be happy to speak with both you and your advisee about any of these issues.


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Musculoskeletal modeling by multibody biomechanics
This Ph.D. course introduces techniques for modeling of musculoskeletal systems based on multibody dynamics. Unlike most courses in the field, this one takes a bottom-up approach beginning with kinematics of open and closed chains and ending with analysis of complex and anatomically realistic models. The course uses the AnyBody Modeling System throughout and also contains an introduction to this system.
- This course is open to all Ph.D. students from all universities and free of charge.
- The course is also open to industrial participants at the charge of 4000 DKK.
- University afffiliates who are not PhD students, i.e. post docs and such, are welcome to sign up and can participate for free, but in case the course is overbooked, priority will be given to PhD students.
News flash!
The signup for this course has been an overwhelming success, and we have received more than twice as many requests to particpate as we can accommodate. We have therefore succeeded to find a sponsor to repeat the originally planned course. This means that the plan now is:
- Course #1: April 23 - 27, 2012 (signup now closed)
- Course #2: May 7 - 11, 2012 (signup is open)
Please notice that the two courses are identical. Course #2 is simply a repetition of #1.
Most of those who have already signed up for for course #1 will be accommodated. You will receive a confirmation soon. A few of the last signups for course #1 cannot be accommodated, but you have the option to sign up for course #2 and you will be given priority if you do so.
Venue and organization
The course is hosted by the Department of Mechanical and Manufacturing Engineering, Aalborg University.
The course is organized by:
- Professor John Rasmussen
- Associate professor Mark de Zee
- Assistant professor Michael Skipper Andersen
Signup for course #1 is now closed. To sign up for course #2 please use this link and select the appropriate course from the list.
Please direct questions to John Rasmussen: jr(at)m-tech(dot)aau(dot)dk
- Please download and familiarize yourself with the material listed for each topic in the list below.
- Please bring a Laptop computer capable of running Windows applications. It is most preferable if the laptop also has Matlab installed. We can unfortunately not provide Matlab licenses to the course participants.
- If you come from a country outside mainland Europe, please bring the necessary conversion plugs. Danish sockets are generally compatible with German plugs. Our voltage is 230 V.
- Please download and install the newest version of the AnyBody Modeling System before the course. The software can be downloaded from www.anybodytech.com. However, please do not request a license for AnyBody. A license will be provided at the course.
Course contents
|Day and time
9am - 4pm
|Fibigerstræde 16,
room 1-101
Kinematics (Michael Skipper Andersen)
9am - 4pm
|Fibigerstræde 16,
room 1-101
Kinetics (John Rasmussen)
Erdemir et al. (password-protected)
Damsgaard (chapter 3)
9am - 4pm
|Fibigerstræde 16,
room 1-101
Muscle systems (John Rasmussen)
Rasmussen et al. (password-protected)
Holmberg & Klarbring (password-protected)
9am - 4pm
|Fibigerstræde 16,
room 1-101
Development of real models (Mark de Zee)
Advanced kinematics (Michael Skipper Andersen)
9am - 4pm
|Fibigerstræde 16,
room 1-101
Development of real models (Mark de Zee, continued)
Introduction of assignment (Michael Skipper Andersen)
Course assignment. Has to be completed and sent to email@example.com for graduation of the course. Deadline: May 11th (course #1) and May 25th (course #2).
Participants are expected to organize their own accommodation. We recommend the following options:
- Danhostel Aalborg. This is located at the opposite end of the city from the University, but there are convenient bus connections.
- Cabinn Hotel Aalborg. New and centrally located hotel with inexpensive rooms. The rooms are rather small.
- Prinsen Hotel. This is an older but very convenient and inexpensive hotel close to the center of the city.
- Scandic Hotel Aalborg. This is a nice, mid-range hotel within walking distance from the university campus but about 4 km from the city center. It is very conveneintly located for drivers.
The exact location of each lecture is listed in the table above. Please find a map of the campus here. All lectures are in the the building Fibigerstræde 16 as shown on the map. If you have trouble finding the place, please call John Rasmussen at 45 2089 8350 as you approach the university.
Lunch can be bought in the university canteen next to the course venue. Inexpensive and very large pizzas are available from Bella Italia just across the small pond from the course venue and made-to-order sandwiches can be bought in the sandwich shop next to the pizzaria.


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European Law Reports
The European Law Reports, compiled and edited by a team of specialist European lawyers headed by David Vaughan CBE, QC, are unique. The European Law Reports report the most important judgments involving issues of European Union law decided by national courts and tribunals in the UK and Ireland. These cases represent a crucial body of law in a wide range of commercial and public law matters, which no one practising in any field where EU law arises can afford to be without. It is particularly strong in competition cases.
The majority of cases reported in the European Law Reports are not reported elsewhere and are regularly cited in judgments of the higher courts.
In a fully developed EU legal system, many of the most important cases will be decided by the national courts, with only exceptional cases being referred to the Court of Justice for a preliminary ruling. Already the extensive case law of the Court of Justice provides sufficient information to enable national courts to decide for themselves with a high degree of confidence many cases without the need for a reference. This tendency, which grows as the confidence and experience of the judiciary increases, will be further enhanced as the reporting of EU law cases in the national courts improves.
Hence the importance of these law reports.
The European Law Reports fill a vital gap, bringing together the most significant national decisions on issues of EU law decided by the Courts and tribunals of England and Wales, Scotland, Northern Ireland, and Ireland.
Publisher: Hart Publishing
The complete issue is available for purchase
Purchasing this issue will give you access to all of the articles listed above for 2 days. You may also purchase articles individually by visiting the abstract page of the article. The exact price (including tax) will be displayed in your shopping cart before you check out. You will be able to remove this item from your shopping cart at any time before you have completed check-out.


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Charles A. Towns, 71 of Cisne, IL died at 1:27PM Thursday, April 11, 2013 at Flora Gardens in Flora, IL. He was a Communication Lineman for Wabash Telephone CO-OP and was a farmer.
Charles is survived by his wife Janet Towns of Cisne, IL; four daughters Marlyn (Butch) Altom of Centralia, IL; Carolyn (Russell) Mitchell of Fairfield, IL; Sherry Towns of Chatham, IL and Terry (Aric) Mills of Cisne, IL; one sister Carolyn (Sterling) Johnson of Springfield, IL; three grandchildren Lucretia Mills, Cassidy Mitchell, and River Altom.
Funeral services for Charles Towns will be 11 AM Monday, April 15 at the Hosselton Funeral Home with burial in Cisne Cemetery with military rites at the cemetery.
Visitation will be Sunday 6-8PM at the Hosselton Funeral Home in Cisne.
Memorials may be given to Alzheimer's Association and will be accepted at the funeral home.
Online condolences may be left for the family at www.hosseltonfh.com.


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Below is the poem entitled The drunkards story-bullying which was written by poet
rams. Please feel free to comment on this poem. However, please remember, PoetrySoup is a place of encouragement and growth.
Read Poems by
Louis Rams
There was three of them in an old pickup truck
After drinking all night at the GOLDEN DUCK.
As the sun was coming up they was screamin and cussin
As to what to do next - they was fussin.
When seeing this sixteen year old riding her bike to school
They started acting like the drunken fools.
They started screaming and howling and giving wolf cries
As the girl rode on by.
They wasn't just satisfied with their howls and cries
They wanted her by their sides.
They turned the truck around and cut her off
They was going to show her " who was boss".
They tried to grab her from the back
But she was prepared for any sneak attacks.
She had her guardian angel with her from heaven
And she had been learning KUNG -FU since the age of seven.
When the first one reached out to grab her
She grabbed his wrist and pulled his hand straight up and back
Till she heard his wrist crack.
As the second one came running up
A round house kick in his face
Sent him flying into space.
The third one came at her with a tire iron in his hand
As she crotched down and took her stand
When he was about two feet away and open wide
In his crotch her kick did fly.
All three men lay on the ground
as she picked up her bike without a sound
Put her bike on their pick up truck
With this girl they was s--- out of luck.
Then drove the truck into town
and told the sheriff where they could be found.
They was charged with "drunk and disorderly manner"
And on the high school there flies a banner.
"Protect yourself and don't give in
Being a bully you will never win"


text: No recent wiki edits to this page. OriginJohnny's origins are largely mysterious. His parents may have been horribly murdered. He is implied to have been an artist of exceptional talent at some point in the past, but he started to lose grip on reality and descended into paranoid schizophrenia. He also lost the ability to create art, presumably around the time that he became a waste-lock and descended into homicidal insanity. CreationJohnny C. was created by Jhonen Vasquez, and he appears in the comic Johnny the Homicidal Maniac.Character EvolutionJohnny was initially created when Vasquez was in elementary school as a surrogate for him to project his revenge fantasies onto. Single strips of the character were published in Carpe Noctem magazine, and eventually the character was picked up by Slave Labor for a seven-part series that ran between 1995 and 1997. Major Story ArcsThe Homicidal Maniac As the name of the series suggests, Johnny is a paranoid schizophrenic with severe psychotic tendencies who murders people in increasingly bizarre ways for increasingly bizarre reasons. He appears to be cognizant both of the fact that he is insane and that he is committing excessively violent acts, yet he seems mainly to be uninterested in stopping. He occasionally experiences lucid moments in which he expounds at length on the nature of life and the state of society, assessing the flaws in both people around him and himself. These monologues frequently end either with non-sequiturs or acts of violence. He spends much of his time torturing and murdering people, painting the wall in his house that holds back the Wall Monster with fresh blood, attempting suicide, and conversing with the various representations of his inner voices and delusions, notably Nailbunny, Mr Eff and Psycho-Doughboy.Though there does not appear to be any rational reason for Johnny to commit his crimes, he often refers to the need to re-paint a wall in his house with fresh blood in order to prevent the dried blood from changing colour and allowing whatever is behind it to emerge. He also frequently directs his violence against himself, resulting in a number of suicide attempts that inevitably fail because he is actually incapable of dying. He is also apparently unknown to the police, or at least they are incapable of capturing him; his crimes are frequently very public and he makes no effort to cover them up or hide himself, yet he has never been arrested or even questioned. This unwanted immortality and apparent invisibility in the eyes of the law is tied to Johnny's status as a "waste-lock". Basically, he unknowingly protects and contains a "space" that holds within it the residues left behind by the negativity and aggression of humanity. When his suicide attempts eventually succeed, he "flushes" this residue out into oblivion and is restored to life. It is revealed that he is the sadistic murderer that he is because of the pressures of being a waste-lock, and that he will stay so forever.


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Harvard College Fund President's Associates Dinner
November 16, 2012
Cambridge, Mass.
Scholarship and the Role of the University: Remarks at the Boston College Sesquicentennial
October 10, 2012
Boston College, Boston, Mass.
Remarks on Behalf of the Academic Community
September 21, 2012
Killian Court, MIT, Cambridge, Mass.
President Faust's 2012 Welcome Letter to the Community
September 13, 2012
Cambridge, Mass.
Convocation Address to the Class of 2016
September 4, 2012
Sanders Theatre, Cambridge, Mass.
2012 Remarks at Morning Prayers
September 4, 2012
Memorial Church, Cambridge, Mass.
2012 Commencement speech
May 24, 2012
Tercentenary Theatre, Cambridge, Mass.
2012 Remarks at ROTC Commissioning Ceremony
May 23, 2012
Tercentenary Theatre, Cambridge, Mass.
2012 Baccalaureate Service: "The Updraft of Inexplicable Luck"
May 22, 2012
Memorial Church, Cambridge, Mass.
Remarks by Drew Gilpin Faust at announcement of edX
May 2, 2012
Cambridge, Mass.
Remarks by Drew Gilpin Faust at signing ceremony with President of Brazil Dilma Rousseff
April 10, 2012
Loeb House, Cambridge, Mass.
Remarks by President Faust: Army ROTC ribbon-cutting ceremony and reception
March 28, 2012
Hilles Hall, Cambridge, Mass.
Challenges of Higher Education
January 20, 2012
University of Mumbai
Universities in a Changing World
January 19, 2012
Asia Society at the Taj Mahal Palace Hotel, Mumbai, India


text:
Panthers CEO Michael Yormark works out with pro boxer
NOV 26, 2013 6:00p ET
He's in the gym well before most of South Florida is awake, and tweets it is his best "meeting of the day."
Yormark has even thrown out the occasional challenge to Panthers players to beat his calorie burn on the stationary bike. So it comes as little surprise Yormark recently accepted an invitation to train with bantamweight Jessy Cruz.
"Training with a professional boxer is an incredible experience," Yormark said. "It was awesome. I really enjoyed it."
Yormark, 47, was a boxer in graduate school and impressed Cruz during a lengthy workout session. Cruz will be fighting Randy Caballero on a card Tuesday night at the BB&T Center in Sunrise.
"He came in with a lot of energy," Cruz said. "Surprised me. I got to give him props."
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text:
Print, Perspective Rendering of a Town House, September 1933
Photostat on paper.
Gift of William E. Lescaze. 1937-37-3
Some of the people involved with this object include
- William E. Lescaze, Swiss-American (Architect)
- William E. Lescaze, Swiss-American (Architect)
What is this?
Perspective rendering of four-story house of William E. Lescaze. Office with glass block on ground floor. Rounded-edge canopy over stairwell with curved banister at left, leading to residence above. Second floor with horizontal row of windows that curve at left edge. Third floor with glass block and two casement windows. Fourth floor all glass block. Building projects in front of existing, flanking architecture. Rendering includes two figures at lower right.


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I'm going to this local blogger meetup thing tonight hosted by The Busy Budgeting Mama. I guess it's kinda like speed dating for bloggers. I'm not really sure what to expect, but I think it will be nice to chat with some women who "get" this blogging thing that I do. They are all local, which means if I any of us do hit it off, maybe we could become … [Read more...]
The Best of Sew Fearless from 2012
So that is that then - the end of 2012, a year full of unexpected twists and turns and lots of readjusting and re-prioritizing. A year that brought with it a new baby... ... and a new sewing room. ...and, consequently, a lot less sewing than I would have liked. Be that as it may, I am going to choose to remind myself of the things I did … [Read more...]


text:
On Tuesday, April 16, 2013, Senator John Bonacic talks with his student shadow, Joe Puglisi of Ulster County, during the Students in Albany Program Day.
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License. Permissions beyond the scope of this license are available at http://www.nysenate.gov/copyright-policy. NYSenate.gov is powered by Drupal.


text:
SAN DIEGO—Cam Newton took a two-mile run along the Pacific coastline early Thursday morning, then took the first tangible steps on a likely climb up the draft board.
Exactly one month removed from leading Auburn to the national title, Newton arrived about 9:30 a.m. at the San Diego high school where he has been training for the better part of three weeks. A little less than an hour later, following a workout in front of about 50 media members and other observers, the Heisman Trophy winner certainly had not hurt his stock.
"I was surprised how refined he is for being such a puppy," said former NFL quarterback and ESPN analyst Trent Dilfer, who intently watched the 40-minute workout. "I would have forgiven a lot of little errors; I would have forgiven a lot of sloppiness. But you didn't have to because you didn't see a lot of it. … If this is his starting point, the ceiling is so high I don't even know where to begin."
Though NFL personnel people could not attend and there are still two weeks before the Scouting Combine and 2½ months until the draft, Thursday was a strong start for a player just beginning his road to becoming an NFL quarterback.
Newton, coming from a spread offense at Auburn that doesn't have much in common with any NFL system, has much to prove in his ability to read defenses. Thursday's event was to provide "a snapshot of his training," said George Whitfield Jr., a quarterback coach heading up Newton's draft preparation.
Certainly, there was a theme being pushed in the Newton camp about how hard he has been working, his temperament and his rapid improvement. That said, for what he did, Newton had an excellent morning:
Improved mechanics. Newton threw most routes, including some while evading "pressure." He improved as the workout progressed, correcting mistakes such as throwing too far inside or throwing too late.
Taking snaps from center, Newton was not perfect, but he had long stretches of virtually flawless mechanics in terms of his feet and follow-through. At 6-6 and pushing 260 pounds, his ability to move and make strong throws, even off-balance, was most impressive.
His velocity is a well-known strength, but it was still remarkable to actually hear the ball slice through the air on many of his passes. Toward the end of the workout, Newton threw three successive strikes into a strong headwind on deep routes.
Impressive accuracy. Whitfield attempted to show Newton's ability to read passing windows against underneath coverage in one drill where five "defenders" stood 10 yards from the line of scrimmage.
As Newton dropped, the defenders would hold their arms wide and shift—and Newton would try to thread the ball to the open passing lane. He was slightly off at times, but his accuracy in the drill was perhaps the most impressive part of the workout.
Winning decision. This might be the last time Newton throws in any sort of public setting until Auburn's pro day. Newton and Whitfield said they haven't made a decision on whether he'll throw at the Combine.
Newton is projected to be a mid-first round pick. And what he does over the next two months will determine how far he climbs. To go public with a transformation project so early was a bold step that appears to have been a shrewd move.
"A lot of guys want to wait," Whitfield said, referring to the usual practice of top-rated quarterbacks waiting to throw until late in the draft process. "Everything isn't done. The construction of what they're trying to work on isn't quite completed. … (Newton) has been relentless in his approach. His whole feeling was there have been other big-time quarterbacks that have come out … and there is so much mystery around the offense they played in, the mechanics. Is this stuff going to translate? Cam just said to himself, 'Not me. I'm going to come right out front and center.'"
Kevin Acee covers the NFL for the San Diego Union-Tribune and Sporting News.