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Method for recognizing objects in an image pixel plane
Method for recognizing objects in an image pixel plane
Patent Drawings:
(4 images)
Stahl, et al.
Date Issued:
September 13, 2005
November 20, 2000
Fechner; Thomas
(Berlin, DE)
Rockinger; Oliver
(Friedrichshafen, DE)
Stahl; Christoph
(Ottobrunn, DE)
EADS Deutschland GmbH
(Munich, DE)
Primary Examiner:
Dastouri; Mehrdad
Assistant Examiner:
Attorney Or Agent:
Greenblum & Bernstein, P.L.C.
U.S. Class:
Field Of Search:
International Class:
U.S Patent Documents:
Foreign Patent Documents:
4438235; 19639884
Other References:
Practice of Digital Image Processing and Pattern Recognition by P. Haberaecker, published by Carl Hanser Verlag, 1995 ("Praxis der DigitalenBildverarbeitung und Mustererkennung"); pp. 125 to 168..
"Automatic Target Detection and Recognition", T. Dodd, University of Southhampton, Technical Report ISIS TR-4, pp. 1-19..
"Classifier and Shift-invariant Automatic Target Recognition Neural Networks", David P. Casenant and Leonard m. Neiberg, Pergamon, 1995 Special Issue, pp. 1117-1128..
"Machine Learning Research", Thomas G. Dietterich, AI Magazine, Winter 1997, pp. 97-133..
"Vehicle Detection in Infrared Linescan Imagery using Belief Networks", P.G. Ducksbury, D.M. Booth, C.J. Radford, 5.sup.th Int. Conf. Image Proc. and Applications, Heriot-Watt University, Edinburgh, Jul. 3-5, 1995, 5 pages..
"Automatic Target Recognition Using a Feature-Decomposition and Data-Decomposition Modular Neural Network" by L. Wang et al. IEEE Transactions on Image Processing, IEEE, New York, USA, vol. 7, No. 8, Aug. 1998; pp. 1113 to 1121, XP000768985..
"A Tutorial on Multisensor Integration and Fusion" by R. Luo et al., Signal Processing and System Control, Factory Automation, Pacific Grove, Proceedings of the Annual Conference of the Industrial Electronics Society, (IECON), New York, USA, Nov.27-30, 1990, pp. 707-722, IEEE vol. 1, Conf. 16, XO010038257..
"Detection Filters and Algorithm Fusion for ATR", by David Casasent et al., IEEE Transactions on Image Processing, IEEE New York, USA, vol. 6, No. 1, Jan. 1997, pp. 114-125, XP 000642456..
Image data of optically acquired input images (1) are processed for emphasizing at least two object classes. Each pixel is subjected to a rough classification (10) based on first criteria that determine whether or not a pixel is relevant for an object recognition. A reduced image (11) is formed from the relevant pixels and irrelevant pixels are omitted. The reduced image (11) is filtered (20) for forming at least two correlated filtered images (21, 22, 23) based on second criteria. Classified images (31A, 32A, 33A) are formed from the filtered images by classifiers that work in accordance with predetermined rules. Weighting factors are allocated to each object class. The classified images are merged in accordance with an algorithm to make a combined global evaluation for each object class. The global evaluation decides, based on the merged images (41A, 41B, 41C), for each pixel whether the respective pixel belongs to an object class and if so to which object class.
What is claimed is:
1. A method for multiple object recognition on an image pixel plane of received images, said method comprising: (a) roughly classifying all pixel points of said receivedimages according to whether or not a pixel point is relevant for said multiple object recognition, according to a set of first criterion related to a nonhomogeneous vicinity around each particular pixel point, to eliminate irrelevant pixel points fromthe relevant pixel points; (b) forming a reduced image based on said relevant pixel points as roughly classified in step (a); (c) filtering each reduced image for forming at least two filtered images whereby image components relevant for said multipleobject recognition are retained in said at least two filtered images; (d) further classifying each pixel point of said at least two filtered images for providing classified images, wherein said further classifying is performed by a group of differentclassifiers which operate in accordance with learned rules to allocate, with evaluation number, said classified images to different object classes, wherein each classifier of said group of different classifiers operates based on a characterizing vectorof a respective filtered image forming a direct input information for its respective classifier and wherein each different classifier works independently of any other classifier of said group of different classifiers; (e) merging, for each pixel point,said classified images in accordance with an algorithm based on the evaluation numbers to perform a combined global evaluation for each class of said different object classes for providing merged images; and (f) deciding for each pixel point, on thebasis of said merged images, whether a pixel point of all remaining pixel points is still relevant and if so to which of said different object classes each relevant pixel point belongs.
2. The method of claim 1, further comprising providing a set of second criteria for performing said filtering step (c).
3. The method of claim 1, further comprising acquiring vicinity image data representing a vicinity of a respective relevant pixel point of a corresponding filtered image, and forming said characterizing vector from said vicinity image date.
4. The method of claim 1, wherein the allocation with evaluation number of said classified images to different object classes comprises allocating certain evaluation numbers of a different weighting factors to each relevant pixel point therebymarking each relevant pixel point with regard to which of said different classes of objects the marked pixel point belongs.
5. The method of claim 3, wherein said step of acquiring said vicinity image date comprises sorting said vicinity image data in a spiral pattern into a vector of coefficients, applying a rapid Fourier transformation to said vector ofcoefficients to form transformation coefficients and forming said characterizing vectors of an absolute value of said transformation coefficients.
6. The method of claim 1, further comprising using, as said group of different classifiers, a neural network capable of learning for performing said step of further classifying.
7. The method of claim 6, further comprising selecting from relevant pixel points of said filtered images characterizing or feature vectors representing features of said relevant pixel points of said rough classifying, and forming rules for saidneural network from said characterizing or feature vectors.
8. The method of claim 1, further comprising performing said merging step in accordance with a statistical process for obtaining said global evaluation, and further comprising using in said statistical process information based at least on oneof a type, position and content of said received images to be evaluated.
9. The method of claim 1, further comprising representing recognized objects by pixel clusters in an image that represents a decision regarding said combined global evaluation while performing said deciding step.
This application is based on and claims the priority under 35 U.S.C. .sctn.119 of German Patent Application 199 55 919.8, filed on Nov. 20, 1999, the entire disclosure of which is incorporated herein by reference.
The invention relates to a method for recognizing in an input image objects belonging to at least one given object class. The images are provided as digital images. The recognition takes place in or on the image pixel plane. The present methodis particularly suitable for the automatic evaluation of image data representing large quantities of image informations.
Large volumes of image data are available in many fields of use. Such large image data volumes must be analyzed in accordance with predetermined criteria. For example, in the area of military reconnaissance, it is frequently the case that largequantities of image data of scenes or terrains are acquired by sensors. These acquired image data must be scrutinized with regard to the presence of installations, vehicles, infrastructure features and so forth in the terrain. These image data aregenerally acquired in large numbers which must be processed and evaluated within given time limitations. The objects to be recognized may have any random dimensions and may have a structure that characterizes any particular object. The structure of theobject may be rather complex or it may be simple. In all these cases it is desirable to perform an automatic image analysis as rapidly as possible.
Other fields of application of this type of image evaluation are, for example, to be found in the area of medical diagnosis, for example when it is necessary to examine a large number of X-ray images, for instance for recognizing anomalies suchas tumors or the like. Another example where an automatic image analysis method is employed is in the area of police work. This area includes the search for missing persons, the monitoring of border crossings or the like. In all these areas a reliableautomatic rapid image analysis method provides great advantages.
General, theoretical approaches for such analysis method for the recognition of objects in images are known from an article in "Technical Report ISIS TR-4" by T. Dodd, University of South Hampton, 1996. This article describes different possibleapproaches to the analysis of digital images for the purpose of recognizing objects in such images.
Individual steps for analyzing images are known from the following publications. Different methods for a rough classification of objects are described in an article "Classifier and Shift-Invariant Automatic Target Recognition Neural Networks",by D. P. Casasent, L. M. Neiberg published in "Neural Networks", Vol. 8, No. 7/8, by Elsevier Science Ltd., 1995. General methods for the dissecting or decomposing of a digital image into image components represented by signals are found, for example ina publication "Practice of Digital Image Processing and Pattern Recognition" by P. Haberaecker, published by Carl Hanser Verlag, 1995. The so-called "Ensemble Theory for Classifiers" has been described in an article "Machine Learning Research" by T. G.Dietterich that appeared in "Al Magazine", Vol. 18, No. 4, 1997, published by AAAI Press. A possible way of merging or fusing individual results of an analysis are described in "Vehicle Detection in Infrared Line Scan Imagery Using Belief Networks" byP. G. Dubksbury, D. M. Booth and C. J. Radford, published at the 5.sup.th International Conference of Image Processing and Application, Edinburgh, 1995.
German Patent Publication DE 44 38 235 A1 discloses a method for recognizing objects in natural surroundings. The known method uses several classifiers which operate in accordance with a predetermined, simple rule. A disadvantage of the knownmethod is seen in that it can function only, and on principle, under the assumptions that the objects to be recognized are compact and at least partially symmetric. Thus, the method is useful only for the recognition of point-shaped objects. The methodcannot be used for recognizing larger and structurized objects.
German Patent Publication DE 196 39 884 C2 discloses a system for recognizing patterns in images. For classifying an object, the known system requires the input and processing of feature or characterizing parameters of the object in order toproduce a recognition result based on such parameters. The system requires the use of a complex classifier. A disadvantage of the known system is seen in that only a clearly determined object can be recognized.
In view of the above it is the aim of the invention to achieve the following objects singly or in combination: to provide an image analyzing method that operates sufficiently reliable for the intended purposes of simultaneously recognizing amultitude of complex objects of any random size in an image; to provide an image recognition method or process which, by its very systematic features, is suitable for an automatic object recognition from a large number of images under time limitations;to provide a recognition method that yields as the result of its analyzing procedure an output statement regarding the presence of a multitude and complex object in an image; and to provide a rapid and automatic evaluation of large quantities of imagedata by a concrete and robust analysis method for an object recognition.
According to the invention at least one object class or a plurality of different object classes and respective classifiers are used simultaneously. Thus, it is possible to evaluate an input image for a respective number of object classes. In afusing or merging step of reduced images a decision is made for each object class and that decision provides directly the position and type of the recognized object or objects. In order to achieve such a rapid analyzation of a multitude of images theinvention employs rules that are learned by a neural network on the basis of representative examples. A characterizing or feature vector is used for the formation of the classifiers. The feature vector is formed from a previously determined vicinity ofa respective relevant pixel point of a corresponding filter image.
More specifically, the method according to the invention comprises the following steps: (a) roughly classifying (10) pixel points of said received images whether or not a pixel point is relevant for said object recognition to provide relevantpixel points; (b) forming (11) a reduced image based on relevant pixel points as roughly classified in step (a); (c) filtering (20) each reduced image (11) for forming at least two corresponding decomposed or filtered images (21, 22, 23) whereby imagecomponents relevant for said object recognition are retained in said filtered images; (d) further classifying (30) said filtered images for providing classified images, wherein said further classifying is performed by a group of different classifierswhich operate in accordance with learned rules to allocate said classified images to different object classes, wherein each of said classifiers operates based on a characterizing vector forming an input information for its respective classifier; (e)merging or fusing (40) said classified images in accordance with an algorithm to form a combined global evaluation or decision for each class of said object classes, said global evaluation or decision representing merged images (41A, 41B, 41C); (f)deciding (50), on the basis of said merged images, whether a pixel point is relevant and if so to which of said object classes each relevant pixel point belongs.
The method according to the invention is robust and practical and can be used for the recognition of a large number of complex, different object classes, such as vehicles, persons, buildings and so forth. Furthermore, the present method issuitable for use in connection with a large number of different image sources such as video cameras, infrared cameras, X-ray cameras and so forth.
In order that the invention may be clearly understood, it will now be described in connection with example embodiments, with reference to the accompanying drawings, wherein:
FIG. 1 shows a flow diagram of the object recognition method according to the invention for three object classes whereby the method is performed on the image pixel plane of the images to be analyzed;
FIG. 2 shows in its left side an example of a received or input image that has been reduced in a first step of the present method to form a reduced image shown on the right side;
FIG. 3 shows symbolically a possible decision result image achieved by the last step of the present method showing pixel points that are not relevant by a zero and recognition relevant pixel clusters designated by a respective classificationnumber; and
FIG. 4 is a flow diagram similar to that of FIG. 1, however illustrating the present method as applied to a single object class.
Referring to FIG. 1, the method according to the invention is performed for recognizing objects in images. Three object classes are processed in the flow diagram of FIG. 1. First, an input or individual image 1 shown on the left-hand side ofFIG. 2 is separately analyzed in a first rough classification step 10. In this rough classification step 10 the input image 1 is viewed as a unit. Stated differently, the input image 1 is not yet analyzed or dissected. However, in step 10 the inputimage 1 is separated into pixel points that are relevant for an object recognition and pixel points that are irrelevant for the object recognition and thus can be ignored.
The right side of FIG. 2 shows the rough classification. Dark areas show relevant pixels, light areas show irrelevant pixels. A suitably selected rough classifier determines the relevance or irrelevance of each pixel of the input image 1 forthe following method steps. The individual image points of the input image 1 are roughly classified in accordance with a given first criterion that determines the weightiness or significance of the individual pixel point for the object classificationand recognition. This given first criterion takes into account, for example whether the image has interesting areas, which may be recognizable objects or at least portions thereof.
Another example of such a first criterion is the relative homogeneity of the vicinity of a pixel point that is being viewed. The assumption is made that homogeneous image areas do not contain any recognizable objects. The vicinity of a pixelpoint is defined by a given raster pattern in the image. Such vicinity is made up of a number of pixels. In order to ascertain the homogeneity of a pixel point vicinity that is being viewed, pixel points that have similar characteristics areconsidered. For example, the pixels of an area or vicinity may have similar color values. If these characteristics of the area correspond to the given homogeneity criteria, then the respective pixel point is classified as not relevant for theperformance of the following method steps and is thus not processed. If the given criterion, such as homogeneity is not satisfied, in other words, if a nonhomogeneous vicinity is recognized around a particular pixel point, the respective pixel point isclassified as being relevant for the further processing.
FIG. 2 shows an example of an input image 1 that was taken by an infrared camera in a low flying aircraft. The input image 1 shows a scene including a terrain with vehicles. These vehicles are automatically recognizable by means of the methodaccording to the invention. In the first step each pixel point of the image is checked whether the particular pixel point has a vicinity with similar color values. Thus, for example homogeneous image regions or areas are recognized if they are made upof pixel points having rather similar color values. On the other hand, the vehicles show locally distinctly different color values and the respective pixel points do not satisfy the predetermined homogeneity criteria. Thus, all pixel points which, forexample belong to fields are classified in the rough classification step 10 as being irrelevant for the further processing while all pixel points that relate, for example to the vehicles are classified for further processing in the following methodsteps.
Referring further to FIG. 2, the right-hand side of FIG. 2 shows the reduced image 11 resulting from the rough classification. The left-hand side of FIG. 2 showing the input image 1 is the result of an image acquisition by an infrared sensor orcamera showing a terrain with a road and dark spots on the road in the lower one half of the left side of FIG. 2. These dark spots represent vehicles. As a result of the rough classification the irrelevant image points have been identified and theright-hand part of FIG. 2 shows these irrelevant pixel points as white areas. These white areas are ignored in the following method steps 20, 30, 40 and 50 in which steps exclusively only the image areas are further processed which are shown as darkpixel points.
The rough classification step 10 shown in FIG. 1 reduces the areas of the input image 1 that need to be processed to form a reduced image 11. The following method steps are concentrated exclusively to the processing of image pixel pointsremaining in the reduced image 11 following the rough classification step 10. Thus, in the following method steps 20, 30, 40 and 50 the relevant pixel points are exclusively processed and pixel points that were classified as not relevant in step 10 arecompletely ignored.
The next step 20 involves a dissecting or filtering of the reduced image 11 to form signal presentations by way of preferably multiple filtering substeps in accordance with known section criteria to form several filter images 21, 22 and 23, eachof which contains the scene that remained in the reduced image 11 as far as that scene is relevant for the object recognition. In other words, the filter images 21, 22 and 23 correspond to each other as far as the shown image content is concerned. Generally, the reduced image 11 is divided at least into two corresponding filter images. However, in accordance with the embodiment of FIG. 1, three corresponding filter images 21, 22 and 23 are formed in the filtering step 20. This filtering ispreferably performed in a two-dimensional fashion. However, the filtering can also be performed in a one-dimensional fashion, whereby the filtering takes place along the rows or columns of the reduced image 11. The filter images 21, 22, 23 correspondpreferably to complementary informations taken from the reduced images 11. These complementary informations contained in the filter images 21, 22, 23 taken together, permit forming or obtaining the complete information gathered from the reduced image11. In other words, all image components that remained in the image 11 after reduction are subjected to the filtering step 20.
For example, a reduced image 11 could be divided in the filtering step 20 into a first corresponding filter image 21 containing small image elements, a second corresponding filter image 22 with somewhat large image elements, and a thirdcorresponding filter image 23 with remaining still larger image elements. For example, the small image elements could be compact cars, the second elements could be vans, and the third elements could be large trucks. The scene represented in the reducedimage 11 is not affected by such filtering. It is retained, which means that the relevant image components and their correlation relative to one another is retained. Irrelevant image components such as the nature of the terrain are no longer present inthe filter images, but the rough scene of the reduced image 11 is fully retained in the filter images 21, 22 and 23. Viewing, for example, the above mentioned image of a road with vehicles on the road and fields next to the road, the filter image 21,for example, could show the object details of the vehicles, such as the tires and the headlights. The filter image 22 could emphasize the central vehicle structural component such as the windshield, the hood, and so forth. The filter image 23 couldthen emphasize the rough contours of the vehicle. However, in spite of such emphasis of detail each of the filter images 21, 22, 23 keeps containing the entire scene of the road and the vehicles.
According to another example embodiment of the invention the filtering step 20 may divide the reduced image 11 in accordance with color areas or in accordance with gray values. Another criterion for the division or filtering of the reduced image11 could be to form an image pyramid that contains the individual corresponding filter images or representations. Each step of the image pyramid corresponds to another generally lower resolution of the input image 1. Thus, the informations contained inthe reduced image 11 are divided in accordance with different resolutions. More specifically, the object or objects contained in the reduced image 11 may be shown in the filter images with different resolutions, whereby each corresponding filter imageshows the respective image information or image object in a different size dimension. For example, passenger cars may be enlarged while trucks are relatively reduced in size.
The next step 30 according to the invention tests each pixel point of each of the filter images 21, 22, 23 with reference to a respective classifier. At least one classifier is provided for each filter image. The total number of classifiersused forms an ensemble or group of classifiers. Such ensemble or group comprises at least two classifiers. According to the invention each classifier operates independently of any other classifier and the classifiers are generally differentiated fromone another.
The result of the step 30 or the result of the testing of the pixel points by the ensemble of classifiers provides for each filter image 21, 22, 23 a respective group of classification images 31A, 31B, 31C and 32A, 32B, 32C and 33A, 33B, 33C asshown in FIG. 1. In the illustration of FIG. 1 the classification images 31A, 32A and 33A represent the result of the testing with an ensemble of classifiers for a certain object class C1, for example representing a defined vehicle type. Theclassification images 31B, 32B and 33B represent an object class C2 while the classification images 31C, 32C and 33C show a third object class C3, for example a further different vehicle type. However, the present method is not limited to these threeexample object classes.
According to the invention different weighting factors or significance factors are allocated to each pixel point of each of the individual classification images. These weighting factors represent a measure or evaluation of the respectiveclassifier that determines to which object class the pixel point belongs which pixel point is being considered. These weighting factors or evaluation numbers may, for example represent probabilities or a predetermined rank order or hierarchy.
Each classifier operates as follows. Each classifiers ranks the input values, that is each pixel point of the respective pixel images 21, 22, 23 with regard to the pixel point vicinity. More specifically, each classifier allocates to each pixelpoint of the respective filter image in accordance with a predetermined rule, output values in the form of evaluation numbers or criteria. The output values of the classifier indicate approximately how certain the classifier is with regard to thequestion: To which class does this pixel point under consideration belong? The following examples of classifiers may be used in accordance to the invention, such as polynomial classifiers, support vector machines, neural networks or the like.
If, for example, neural networks are used as classifiers, FIG. 1 requires an ensemble of three different neural networks. Each of the filter images 21, 22, 23 is allocated to one of these three neural networks. Thus, each neural networkprocesses one of the filter images. Each neural network allocates to each pixel point of the respective filter image 21, 22, 23 an evaluation measurer or number in accordance with given or predetermined rules for each object class. The evaluationnumber or measure may, for example be a probability value in the respective classified image 31A, 31B, 31C or 32A, 32B, 32C or 33A, 33B, 33C. Stated differently, the neural network stores the allocated evaluation number or measure and then visualizesthat value, for example by a color coding in the classification or classified images 31A, 31B, 31C or 32A, 32B, 32C or 33A, 33B, 33C.
The rule by which a classifier works is preferably obtained from available and analyzed examples of representative input values which have been analyzed prior to the application of the method according to the invention as shown in FIG. 1. Thisformation of the classifier in accordance with the just mentioned rule generally involves an iterative adaptation of the classifier to the given examples which contain the applicable rule or which define the applicable rule. If neural networks are usedthis iterative adaption phase is referred to in the respective literature as "training phase" or as "learning of a rule". If other classifiers are employed according to the invention they may, for example, involve a statistical method.
If, for example it is required that different vehicle types must be classified from available input images 1, the classification step 30 may be based on predetermined classes such as "compact cars", "limousines", and "vans or pick ups". Different features of vehicles are extracted from the reduced images 11 by the filtering step 20. A feature vector or characterizing vector is formed from these features for each pixel point. The components of such a characterizing vector are suppliedto the classifiers as input values. The classifier such as a neural network uses these input values as a basis for the classification of each pixel point. More specifically, the classifier allocates, in accordance with a previously determined rule, theabove mentioned evaluation number, which may also be referred to as weighting factor or significance number, to each feature vector for each of the classes "compact cars", "limousines", "vans" or "pick ups".
As mentioned, the characterizing or feature vectors required for the training of the neural network classifiers prior to applying the method according to the invention can, for example, be obtained on the basis of available representative exampleinput images. In the forming of these feature or characterizing vectors that are required for the training of the neural network for learning the rules, only those pixel points are used from the filter images 21, 22, 23 which passed the roughclassification step 10 of example input images and were recognized or classified as relevant pixel points. The example input images are to be distinguished from the input images 1 that are processed according to the present method.
The input values to each classifier are assembled on the basis of the vicinity of the image or pixel point under consideration in the respective filter image 21, 22 or 23. More specifically, for each pixel point a vicinity around the pixel pointis selected. The vicinity includes left, right, upper and lower neighboring pixel points of the pixel point that is being evaluated. The selection of the vicinity including the number of pixel points forming the vicinity can be made dependent on theobjects to be recognized or the selection may be fixed. The selection criteria are based on the characteristics or features of the objects to be recognized. For example, large objects require the examination of large vicinities because the recognitionof large objects requires generally more features than are necessary for the recognition of smaller objects. The pixel points making up the vicinity are sorted into a feature or characterizing vector in accordance with given rules. Based on thischaracterizing or feature vector, the respective classifier of the ensemble provides output values for each pixel point and these output values are interpreted with regard to the evaluation number or weighting factor that allocates a relevant pixel pointto a specific class.
An example of a rule for the formation of a feature vector from the vicinity of a pixel point will now be described. When forming the characterizing or feature vector, the values representing the vicinity of the pixel point in the respectivefilter images 21, 22 and 23 are sorted in spiral fashion into a coefficient vector. A rapid Fourier transformation is then applied to the coefficient vector. Thereafter, the feature vector is formed from the absolute values of the Fouriertransformation coefficients.
A classification step 30 is performed following the filtering step 20. Step 20 provides signal representations in the form of filter images 21, 22 and 23 which contain features of small, medium sized and large objects, then step 30 is performedin the following sequence. A feature vector is formed for each pixel point of the respective filter image 21, 22, 23. This feature vector is formed from the pixel points forming the vicinity of the pixel point under consideration. For example, avicinity may be formed by 5.times.5 pixel points. The pixel point under consideration, which is to be evaluated is in the center of this cluster of pixel points forming the vicinity. This feature vector comprises 25 components in accordance with theselected 5.times.5 vicinity. The components of the feature vector are assembled from encoded values of the respective filter image 21, 22 or 23. A further possible rule for the production of a feature vector based on the 5.times.5 pixel vicinityprovides, for example, that the columns of the 5.times.5 pixel vicinity are sequentially sorted into the feature vector. Based on the formed feature vector, the classifier that is allocated to the respective filter image provides output values whichconstitute an evaluation of the pixel point under consideration and the evaluation determines to which object class the respective pixel point belongs, for example compact cars, limousines, or pick ups or vans. The feature vectors, for example from thefilter image 21 comprise, for example vehicle details which are specific to a particular vehicle class, whereby it becomes possible for the classifier to allocate the respective pixel point to the relevant class. Similarly, or analogously, the featurevectors of the filter image 22 have reference to vehicle structures of midsized vehicles and the feature vectors of the filter image 23 have relevance to large vehicle structures. In all instances these vectors form the basis for the decision made bythe respective classifiers. The classifier provides for each object class an output value. Such an output value provides, for example information regarding the probability with which a pixel point under consideration can be allocated to a specificobject class. This output value is stored in the respective classification image 31A, 32A, 33A, or 31B, 32B, 33B or 31C, 32C, 33C. The output value may be visualized by a color coding of a respective value range. For example, if the output value ismade black, the probability is zero percent, a white color for the output value represents, for example a probability of 100%. Assuming that the evaluation numbers are to be expressed in probabilities, it would be possible, for example, that the firstclassifier which is allocated to the filter image 21 provides, based on the respective feature vector of a certain pixel point the following probability allocations: 13% for the object class compact cars, 17% for the object class limousine, and 70% forthe object class pick up or van. These three probabilities are stored in the classification images 31A, 31B, 31C and visualized. The classification images 32A, 32B, 33C and 33A, 33B, 33C are formed analogously as just described.
The next step 40 in the method according to the invention performs a fusion or merging of the above classification images 31A to 33C for each object class. This merging step combines the obtained individual evaluation numbers of the classifiersfor each image or pixel point and for each classification image 31A to 33C to form a global evaluation or decision which is presented in the form of the merged images 41A, 41B and 41C as shown in FIG. 1. In this way one obtains for each image point ofthe reduced image 11 and for each object class a global evaluation or decision number which represents the combined evaluation of the classifiers forming a group or classifier ensemble.
The individual evaluation numbers of the classifiers of an ensemble are combined in accordance with predetermined known mathematical methods. For example a mean value may be calculated on the basis of the individual evaluation numbers and thismean value may be used as the global evaluation result. If one considers the decision of a classifier as a probability statement, statistical methods may be used in order to obtain the final or global decision. Such statistical methods are, for examplethe Bayes-Fusion or the Dempster-Shafer-Fusion. For this purpose the output values of the classifiers are approximated in accordance with probabilities and are then merged with the aid of the probability theory and on the basis of known aprioriprobabilities. The apriori probabilities may, for example, be obtained from context information regarding the type, position and content of the input image 1 to be evaluated. The probability values of the classifiers or the ensemble of classifiers aregathered pixel by pixel for each object class in the merging or fusing step 40. The merging or fusing result of each object class thereby corresponds to a final probability statement for the pixel point under consideration or to be evaluated and withreference to the predetermined object class.
The merging or fusion step 40 for the individual evaluation numbers of the ensemble for any random pixel point of the reduced image 11 may, for example take place in the following sequence. The evaluation numbers of the different classifiers ofthe corresponding pixel points of the classification images 31A to 33C are combined with an algorithm for each object class. Assuming, for example that the evaluation numbers are probabilities then the classifiers of the ensemble provide for a certainobject class and pixel by pixel a respective probability percentage, for example the following probabilities: 87%, 83% and 95%. The merging or fusing result for this pixel point and for this object class could now be determined, for example as the meanvalue of the three probabilities, thereby obtaining 88.3%. The individual numbers of probabilities in this example have the following significance or meaning: a first classifier of the ensemble which is allocated to the filter image 21 provides for adefined object class and for the pixel point of the reduced image 11 under consideration the probability value of 87%. More specifically, the classifier allocates to the pixel point under consideration and on the basis of the features of the filterimage 21 the probability of 87% that this pixel point belongs to a defined object class, for example object class A represented by the cluster A in FIG. 3. The probability values of the remaining classifiers and pixel points are to be interpretedcorrespondingly. The fusion or merged overall or global probability per pixel point means that the ensemble of image classifiers has allocated to the pixel point under consideration a probability of, for example 88.3% that it belongs to a determinedobject class such as object class A. The merging or fusing for the other object classes takes place in an analogous manner.
The last step 50 according to the invention establishes a decision result image 51. In this step it is determined on the basis of the merging or fusion images 41A, 41B, 41C for each pixel point of the reduced image 11 whether that point belongsto an object class and, if so, to which of these object classes the pixel point belongs. For this allocation in step 50 the total evaluation numbers of the corresponding pixel points of the fusion images 41A, 41B, 41C are combined with each other, forexample by forming the maximum of the total evaluation numbers for the different object classes. Then that object class is selected which has the highest value in the global evaluation number. The thus determined maximum global evaluation number forone object class is then compared with a given threshold value. If the global evaluation member or value exceeds the threshold value a corresponding class, suitably encoded, is allocated to the respective pixel point in the decision result image 51. The previously selected threshold value determines for each pixel point whether or not that pixel point is part of an object of a determined object class.
A possible formation of the decision result image 51 will now be described. Assuming that three recognizable object classes are used, for example compact cars, limousines, pick ups or vans. Under this assumption a certain pixel point, forexample of the object class "vans" has assigned thereto the highest value of the respective global evaluation number. If this highest value is larger than a given threshold value, then the pixel point under consideration is allocated to the object class"vans". If the global evaluation number is below the threshold value, it is assumed that the respective pixel point does not belong to any of the object classes. Zero (0) have been allocated to the pixel points that have been eliminated in the roughclassification 10, as shown in FIG. 3. Identification numbers 1, 2, 3 have been allocated to the pixel points that fall into the three example object classes. These pixel points are part of the decision result image 51. For example, "1" refers to theobject class of compact cars, while "2" refers to the class of limousines and "3" refers to vans and pick ups as described above and shown in FIG. 3. The letters A, B, C designate respective pixel clusters. Referring further to FIG. 3, the decisionresult image 51 illustrates recognized objects in the three object classes represented by respective three pixel clusters A, B and C. A pixel cluster is generally formed by a larger number of neighboring pixels with identical characteristics, for exampleidentical color values. In the decision result image 51 the pixel coding numbers 0, 1, 2 and 3 are given merely as an example. An object class is characterized in that the neighboring pixel points are predominantly allocated to a certain object classso that these pixel points predominantly have the same encoding. For example, clusters A and B in FIG. 3 are uniformly formed by code numbers 1 and 2, respectively. However, cluster C has within a plurality of pixels that are encoded with number 3, onepixel point encoded with number 1. These clusters correspond to defined vehicle types as described. The size of the respective cluster corresponds approximately to the dimensions of the vehicle or vehicles in the initial input image 1. The fact thatthe cluster C in FIG. 3 includes one pixel that does not belong into the respective object class does not make the cluster C ambiguous.
The decision result image 51 may be used for further processing in the object plane. However, the method according to the invention relates only to the image pixel plane. In the following an example is provided how the transition from theiconic or image pixel plane to the symbolic object plane can be performed. In the symbolic object plane the image content is described by symbols. The method steps for such transition from the iconic image pixel plane to the symbolic object plane arewell known in the art. Based on, for example the cluster size that is the number of pixels having the same encoding in a common cluster, it is possible to decide in the decision image 51 whether or not any particular cluster represents a real objectfalling into a predetermined object class in the input image 1, on the symbolic object plane. If, for example, the pixel points form a cluster in the decision result image 51, the size of which exceeds a certain value, for example more than 100 pixels,the cluster is considered to be equal to a recognized object of the respective object class. The positions of the recognized objects in the input image 1 may, for example be approximated from the positions of cluster concentration in the decision resultimage 51.
The reduced image 11, the filter images 21, 22, 23, the classification images 31A, 32A, 33A or 31B, 32B, 33B or 31C, 32C, 33C, the fusion or merging images 41A, 41B, and 41C, as well as the decision result image 51 need not be imaged in a visualsense. Rather, these images may be formulated by means of mathematical methods, for example by means of matrices.
FIG. 4 shows the method according to the invention as performed with reference to only one relevant object class for which a search is made. FIG. 4 illustrates the simplest application of the present invention. However, the present method maybe used for recognizing objects in a plurality of object classes.
Although the invention has been described with reference to specific example embodiments, it will be appreciated that it is intended to cover all modifications and equivalents within the scope of the appended claims. It should also be understoodthat the present disclosure includes all possible combinations of any individual features recited in any of the appended claims.
* * * * *
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Things Not To Say To A Man When Dating. Date Hookup!
LYNN from Bakersfield, age 30
Ang Hookup Daan Music Ministry Songs 1980s
Ten Things You Should Never Say to a Guy
Dating tips: 10 things not to say or do if you ever want to see them again | Daily Mail Online
18 Sep 1. "You're not that strong." Unless you're talking about a Superman-esque feat of strength like lifting a building, you've just made him feel inadequate. 2. "I'm so much taller than you!" It doesn't matter how tall a guy actually is, calling him short, even if it's just because of the shoes he's wearing or something. 14 Dec Nothing makes a person who's genuinely interested in you feel worse than hearing, "My ex-boyfriend used to take me to this place all the time," or "I was so in love with my old girlfriend. This holds true for women, too, who should tell their date that they look handsome and that their outfit is a great choice. Many women expect men to pick up the check, especially on a first date. Many men probably expect that they'll have to pick up the tab too. Indeed, a survey revealed that over three-quarters of people in heterosexual relationships think men should always pay for the first date. This dynamic tends to linger, even as the.
So what topics are off-limits? Here are 12 things from my personal experience you should never say on a first date. Save this question for further down the line. Instead, ask about their childhood, where they went to college, who their favorite baseball player is, and what their preferred part of the Thanksgiving meal is.
Will you resent him for it later? If you want him to pay visit web page he is willing to, then let him. Be sure to express your heartfelt thanks afterward. I just have to make a quick phone call. Keep your phone in your purse. Even resist the urge to pull it out to share those photos of your adorable new puppy; you may see a text or call from someone and be unable to not respond.
Your date does not need to know that your exes are still around. Nor does your date need to know to look forward to meeting these exes if you hit it off. Why are you still single?
Gogglebox star Sandi Bogle slips into a printed bikini as she sports a platinum blonde wig on Benidorm beach break Love Actually fans stunned to discover there are just FIVE years between Keira Knightley and actor who starred as Sam in the film Whit-more could a girl want? But this is the 21st century and we have come to expect a certain level of gender equality. Why do girls swarm to click bathroom in groups?
Whenever a guy asks me this on a first date, I immediately cringe. I never know how to answer, and it just makes me feel awkward. I would love to know more about your background.
10 Things Guys NEVER Want to Hear you Say
How did you get into surfing? It will lead to too many difficult topics, like long-distance relationships.
5 Things Girls Should NEVER Say to a Guy!! (Girls = AWFUL)
I recently went on an awesome first date, and at the end of it, he told me he was moving to Denver the following week. It was disappointing, and frankly, I wished that we had not gone out at all.
here First dates are attracted to you just the way you appear now, so saying right off the bat that you are going to drastically change your look in the near future could cause them to run for the hills.
Project confidence, be light, smile, and make eye contact. Actually, make mine a double.
Ten Things Girls Should Never Say to Guys Ever!
DDTM is an acronym my friends and family use. Regrettably, getting too drunk on a first or second date has caused me to lose respect and interest from many an eligible bachelor. How much money do you make?
Father-of-two who began transitioning into a woman is now BACK to living as a man after changing his mind I just have to make a quick phone call. What does he see in her?
You can ask them what they do and find out where they work. What do you think people never say on a first date? Sign up for our newsletter and receive exclusive stories, breaking news, sale alerts and much more straight to your inbox!
What happened in your last relationship?

subset: commoncrawl


text:
Symptoms Of the Leaking Roof
In the event the roof in your home commences leaking, you could believe that putting out some buckets to collect dripping drinking water is enough to stop any destruction happening. The soiled truth of the matter about leaking roofs is by the time the h2o has landed from the bucket, the bulk of the damage has previously transpired, and when a Do it yourself repair may seem desirable towards your wallet, that carries its own risks.
Indications of A Leaking Roof
The immediate indicator of the leaking roof is visible dripping through the ceiling, or perhaps the sound of dripping h2o. Another indicator would be the formation of h2o stains and drinking water spots. H2o stains frequently search like puddles that happen to be stained brown, but any signs of discoloration in your ceiling or partitions is often indication of leaking drinking water.
Water stains tend to be adopted by mold. Once you recognize mould, you must right away locate the source with the leak. Must you see any lacking roof shingles, that is a signal that in the upcoming downpour, you're pretty much certain to practical experience a leak.
Injury Which might be Attributable to A Roof Leak
The speedy danger by using a leaking roof, is destruction towards the wood and ceiling boards of your respective attic, as well as probably detrimental merchandise saved there. Leaking h2o can also hurt paint and plaster, discoloring them and likely causing effervescent.
Water within the attic can seep into electrical conduit pipes, leading to hurt to the home's electrical process, and ceiling mounted lighting fixtures. Exposed wiring can quick while in the presence of water, which subsequently may lead to a hearth. In spots where by leaks are current, it is recommended to cut off electricity to that location.
Should the h2o seep in the ceiling boards and wooden, or pool and stand, it results in best cases for mould and mildew to develop. Black mold is the most popular style to grow in locations that often get moist. Any mildew that sorts poses a threat to your well being, and taking away and changing mildew influenced locations may become highly-priced.
Water might cause rafters, wall frames, drywall and ceiling boards can result in these things to swell, deteriorate and weaken, which around time, may cause these items to collapse.
Hazards With Do-it-yourself Roofing Repairs
Even though the associated fee saving aspects of a Do-it-yourself roof mend appear pleasing, it truly is not a good idea to tackle roof repairs with no support of the specialist roofing company. Though you might believe that that changing several shingles may perhaps take care of the trouble, it normally is not that simple. Many men and women neglect to check roof seams, as well as the corking and sealing of your roof at details where by the roof fulfills walls and chimneys, that may make get more info the project much more substantial than originally anticipated.
Expert roofers have experience in repairs and know very well what to search for when inspecting a roof and accomplishing repairs. This experience will always guide to some improved position than any tutorial. Roofing may be risky. There is certainly normally the chance of a ladder slipping, or if you roof incorporates a slope, it can be pretty effortless to your footing and slipping off the roof. Knowledgeable roofer won't only have the correct safety gear, but can even have insurance policy to deal with them and you from any injuries which could come about. This insurance also extends to any injury that may arise you your assets during the fix approach.
An outstanding roof maintenance makes certain that you will not experience any future leaks, but additionally maximizes your suppress charm, and makes sure that your assets price will not be influenced by ugly visible damage. Do you have to have professional any roof challenges, or when you have any fears chances are you'll have a leaking roof, be sure to get hold of us.


text:
A morpheme study in a corpus of secondary school EFL: The "-(e)s" morpheme for 3rd person, plural and possessive
IdentificadoresURI: http://hdl.handle.net/10481/22162
MetadatosMostrar el registro completo del ítem
AutorArdébol Barea, Juan
DirectorLozano, Cristóbal
DepartamentoUniversidad de Granada. Departamento de Filologías Inglesa y Alemana
Morpheme order studiesSecond language acquisitionAcquisition of morphologyAdquisición de segundas lenguasLearner corporaCorpus de aprendices
PatrocinadorTrabajo fin de máster, Universidad de Granada, Departamento de Filologías Inglesa y Alemana. Curso académico 2011-2012.
This study about the order of acquisition of morphemes in the L2 English brings to the morpheme order studies a new element which had never before been researched, that is, the use as a means of analysis of the data of a written learner corpus created by L1 Spanish learners of L2 English in classroom environments. As well as previous studies such as the ones carried out by Dulay and Burt (1973), Bailey, Madden and Krashen (1974) or Pica (1983), this study demonstrates that there exists a natural order in the acquisition of morphemes, which implies that some morphemes will be acquired earlier than others. Moreover, an-in depth analysis of the "-(e)s) morphemes, that is, plural, third person singular and possessive, shows that despite being three morpho-phonologycaly similar morphemes, they are acquired in different stages, as previous studies had shown.


text:
- All news articles on our blog are created by learners as part of their Digital Media coursework.
Blog Archive
Tag Archives: Phoenix Fm
Live interview with 92.5 Phoenix FM 👍
Yesterday -Niamh, Dan, Robert and Becky were invited to 92.5 Phoenix FM to give a live interview about our Erasmus + project! You guys make the centre proud- well done ! If you missed yesterday's interview on Phoenix FM you can … Continue reading
Posted in News, Uncategorized Tagged Catering, Cook book, erasmus+, Leargas, Obesity, Phoenix Fm Leave a comment


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On behalf of Ethisphere Institute's Business Ethics Leadership Alliance (BELA) and Kimberly-Clark, we are pleased to invite you to participate in a roundtable discussion intended for GCs, CCOs, and executive compliance and ethics leadership from respected organizations from the local business community. What to expect at this roundtable:
Hui Chen, Compliance Consultant and former Compliance Counsel Expert to the U.S. Department of Justice's Fraud Section, will be participating in the roundtable and sharing practical, data-driven, and evidence-based insights and best practices.
Ethisphere will lead a group discussion and share their insights into how organizations are conducting meaningful root cause analysis, as well as the latest findings around measuring culture from their Ethical Culture and Perceptions data-set.
Compliance leaders at Kimberly-Clark will provide a glimpse into their program and share recent E&C initiatives, leading into a group discussion.
This is an invite-only executive roundtable.
To request an invitation please contact Sarah Neumann, Director, Engagement – Business Ethics Leadership Alliance (BELA)
Registration is limited to in-house company professionals with lead responsibility for the compliance & ethics programs and culture of integrity.


text:
Kuwaiti Minister of Information Sheikh Hamad Jaber Al-Ali Al-Sabah (2nd from right) and Justice, Education and Higher Education Minister Ahmad Al-Mulaifi (3rd from right), tour a polling station in Kuwait City during the Feb. 2 , 2012 parliamentary elections. According to local media, the information minister expressed optimism that the elections would lead up to the creation of a parliament that would deliver and push forward the development agenda. The justice minister told reporters that he was confident that the voting process would run smoothly. Some 285 candidates are running in the polls to vote for a new 50-seat parliament.Add to Lightbox Download
- Filename
- miller_20120202_DSC2719.jpg
- Copyright
- © Noah W. Miller
- Image Size
- 4256x2832 / 5.6MB
- Contained in galleries
- Parliamentary Election in Kuwait


text:
Supervision of measurement equipment in metallurgical production
- 9 Downloads
KeywordsPhysical Chemistry Analytical Chemistry Measurement Equipment Metallurgical Production
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Copyright information
© Instrument Society of America 1962


text:
We don't want our users to have to type the spaces and parenthesis but we do want the phone number field to display them once entered, ignoring them when typed. This is when we use the display patterns.
Because we also want our users to paste a formatted value into the field, including the spaces and parenthesis, we will start with a Text Field. If we used a Numeric Field then we won't be able to perform a paste, if the value contains anything but valid numeric characters then the whole paste would fail.
This means we must add some character filtering code to the change event so only digits are accepted. Filtering characters means testing xfa.event.change when the characters are coming one at a time, as they do when we are typing or, typically, testing xfa.event.newText when we are pasting a value.
But in this case we also want to set the maximum character limit to 10, this means the maximum length of xfa.event.newText is also set to 10 characters. This becomes a problem when the pasted value is formatted, in which case it will be either 12 or 14 characters. This is when we must use the xfa.event.fullText, which gives the full value before the truncation has occurred.
So our change event code now becomes;
xfa.event.change=(xfa.event.change.length > 1) ?That is if the value causing the change is longer than one character, remove all non-digit characters, otherwise if the change is one character and that character is not a digit then ignore it.
The final part of our phone number field is to change the display pattern depending on which type of phone number has been entered, so our exit event code becomes;
var formatString = "";Setting a display pattern now means we can test for an invalid phone number using;
if (!this.isNull)
if (LandLinePhoneNumberRegex.test(this.rawValue))
formatString = "'('99')' 9999 9999";
if (MobilePhoneRegex.test(this.rawValue))
formatString = "9999 999 999";
this.format.picture.value = formatString;
if (!this.isNull)
if (LandLinePhoneNumberRegex.test(this.rawValue))
formatString = "'('99')' 9999 9999";
if (MobilePhoneRegex.test(this.rawValue))
formatString = "9999 999 999";
this.format.picture.value = formatString;
TelephoneMobile.rawValue === TelephoneMobile.formattedValue
This works because the field will only be formatted when a value matches the display pattern, all other values are display as is and so the formatted value will equal the raw value.
Format strings with single quotesIf you ever want a single quote as part of the display pattern, as in a feet and inches measurement like 6'2" then you will need to use two single quotes and wrap then in single quotes. So four altogether,


text:
Bwin quote
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San Francisco just voted to regulate the glut of shared electric scooters that startups are putting all over the city's streets
San Francisco has voted to regulate electric scooters, requiring them to apply for a permit before operating on city streets. The legislation was passed unanimously. The San Francisco MTA expects applications for permits to be ready in May. Just weeks after a raft of electric scooters have filled the streets of San Francisco — and drawn complaints from local residents — the city has voted to regulate them. In a unanimous vote, the San Francisco Board of Supervisors voted on Tuesday to require electronic scooter startups to […]More info


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Facts About cranberry tablets Revealed
Black seed oil was also proven to be practical in battling candida and fungal bacterial infections within the digestive program and on the pores and skin.
B Vitamins MedlinePlus notes that B vitamins are vital for energy generation because these vital nutrients allow you to make use of (metabolize) energy from foods. The College of Maryland Medical Heart suggests B vitamins change the carbohydrates, protein, and Body fat you try to eat into energy and they are needed for proper nervous technique and Mind capabilities (like psychological alertness). On top of that, staying deficient in vitamins B6, B12, folate, or iron can cause anemia, resulting in Extraordinary fatigue. This is exactly why B vitamins tend to be Utilized in energy-boosting vitamin combos, beverages, and other dietary supplements. To maximise energy concentrations from B vitamins, take in at the least the RDA or AI from vitamin B-prosperous foods or supplements, but Really don't exceed the tolerable upper ingestion degree (UL) when there is just one. Doing so is usually risky. Vitamin B1 (Thiamin) Vitamin B1 can help change carbohydrates to glucose, which Your entire body utilizes for energy. Try to eat plenty of vitamin B1-prosperous foods and take supplements In the event your diet regime lacks ample B1. Consume at the very least: one.2 milligrams for men and 1.1 milligrams for Ladies every day Upper Restrict (UL): Not decided Vitamin B2 (Riboflavin) Vitamin B2 will help launch energy from proteins and aids in purple blood cell generation.
The excellent news, suggests Baard, is that we can easily counter these energy killers by programming extra peace routines into our working day.
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text:
Thinking about replacing your furnace?
Posted on December 10, 2018
Now that Winter is here, you may be experiencing problems keeping your home warm. Naturally, that leads to questions like, "When is a good time to replace a furnace?", and, "How old should my furnace be before it's time to replace it?". Efficiency has improved over the last ten years, so 96% efficient furnaces are common now. There are several options available to match every budget these days, so what should you be looking for? Davis Pro Heating and Air can help you determine the right matched system that fits your budget. Give us a call, or send us a contact us form from our website - we will be happy to help you make that all important decision!


text:
PTCL has announced a new weekend offer that will enable landline customers to call local PTCL and Vfone numbers, nation wide calls and mobile calls at half rate on Sundays.
This promotion will be effective from Sunday October 5, 2008.
On other hands, PTCL is taking back free calls (at night) offer. PTCL's limited time free local call promotion allowing free local call between 11pm to 6am shall end at midnight Sep 30, 2008. This service was launched on July 01, 2008.
It merits mentioning here that PTCL's recent packages include Pakistan Plus offering 2500 nationwide minutes for Rs 199 only, per month. International Plus offers international calls as low as 80 paisas per minute and local call offering of call charges @ 67 paisas per minutes.


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Michel Design Works presents fantastic 4 different new products under The Bohemia Collection, Lemon Verbena collection, Lotus Leaf collection, Rose Geranium collection and Sandalwood Spice collection Inspired by the deep, gem-like colours of mosaic tiles and the intricate patterns of the Arts and Crafts Movement, the eye-catching designs in The Bohemia Collection are perfect for mix-and-match displays and each is paired with a rich, distinctive scent. Lotus Leaf features exotic patterns, deep purples and copper foil accents which are perfectly matched by a meditative, spa-like scent. Available September 2016. A hand cream will be added to the collection in Spring 2017.


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Avengers 4 title and first look expected to be unveiled on June 13
Fans searching for answers at the end of Avengers: Infinity Wars might get some clues about the Avengers 4 movie on June 13. It is expected that the Avengers 4 movie title and trailer might be released next week on Wednesday at CineEurope in Barcelona. Avengers directors Joe and Anthony Russo had already admitted that the Avengers 4 title was too revealing to release straight away. They said it would not be disclosed until suitable time had passed after Infinity War hit cinemas.
Avengers 4, is an upcoming American superhero film based on the Marvel Comics superhero team the Avengers, produced by Marvel Studios and distributed by Walt Disney Studios Motion Pictures. It is intended to be the direct sequel to 2018's Avengers: Infinity War, as well as the sequel to 2012's Marvel's The Avengers and 2015's Avengers: Age of Ultron. Avengers 4 has Brie Larson as Carol Denvers aka Captain Marvel; the first female lead in the MCU. The movie is slated to release in May next year.


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CHOCOLAY TOWNSHIP — In the middle of the summer, Lake Superior can be one of the best places to be. It can also be one of the most dangerous places.
The Chocolay Township Fire Department has raised the necessary funds to install three rescue stations near Lake Superior. The fire department began working on this project during the winter.
Each of the three rescue stations will be built along M-28. The rescue stations will contain life jackets and other life-saving devices for anyone to use in case an emergency arises in the water.
"These are essentially important because people are out there swimming, there's rip currents and if somebody does get in trouble there's a device right there handy. These are priceless," said Chocolay Township Assistant Fire Chief Lee Gould. "The devices that are going to be out there and for if somebody was struggling out in the water and needed a device, the safety devices will be there, you could use them to rescue them."
Gould is aiming to have the rescue stations up next month, just in time for swim season.


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The Basic Principles Of wine gift boxes
It consumes some volume on the bottle, enabling the bottle to seem larger sized for the same amount of wine, which may impress the purchaser.[22]
Penalty box, a booth Employed in numerous ball-group sporting activities exactly where a participant sits to serve some time of the presented penalty.
Warmed up wood will ignite additional quickly, and the fire doesn't need to expend precious Electricity to provide the logs closer to combustion temperature.
A bulk box is a significant box normally Utilized in industrial environments. It can be sized to suit properly on a pallet.
Some bottles of wine Possess a paper strip beneath the foil, to be a seal of authenticity, which have to be broken prior to the bottle may be uncorked. Bottles of large-conclude Rioja wine could possibly have a masking of gold wire netting, Spaniard Camilo Hurtado de Amézaga, Marqués de Riscal Started a winery in Rioja, in 1858, naming it soon after his very own noble title, which were developed in 1708 by Philip V.
These are the universally revered wines from Bordeaux, France. Within the modest wine district of Bordeaux, you will discover around 10,000 chateaux producing wine. It is not simply as a business but a enthusiasm of daily life! Two bottles will likely be sent in your gift receiver each other thirty day period.
Even though anecdotal data regarding the contributions of vibration in wine storage states that it contributes into the accelerated ageing of wine with adverse consequences, this continues to be a research area with somewhat small details. In a selected research, vibrations of different frequencies happen to be shown to get their unique distinct effect on the chemistry with the wine.
Consists of: Initial cargo gift boxed with individualized gift card, No cost wine accent, and all transport for the whole phrase of your gift.
Whatsoever your requirements, we could make it. Prospects arrive at us for his or her promotional and packaging requires noting they really like the natural natural beauty and stability that wood features. Created from a range of hardwoods and several softwoods, our goods might be produced with various corner joinery choices and might be Improved with tailor made hinges and latches and handles, dividers, flocking, custom liners, glass and acrylic.
We will be the just one supply for reasonably priced custom made labels that glimpse Expert, prosperous and they are only tasteful. We'll style and design, print and supply a finished item to you that is certainly Skilled still individual and certain to impress.
If your wine is subjected to temperatures which have been way too cold, the wine can freeze and expand, creating the cork to get pushed out or (far more normally) the bottle to crack; this will permit a lot more oxygen for being exposed to the wine. Dramatic temperature swings (which include repeated transferring a wine from a warm home to the great fridge) may bring about adverse chemical reactions while in the wine which could result in a variety of wine faults. Most specialists, for instance Jancis Robinson, advocate that wine be kept at regular temperatures involving 50 and 59 °File (10 and fifteen °C).[one] Tom Stevenson speculates that 52 °File (11 °C) would be the most best temperature for storage and getting older.
Once you reserve your personal workshop day and time, you might get a custom-made party url to the useful source hostess to ship out to the attendees by using e mail with instructions on registering.
Although most wines can take advantage of lying on important site their own side, Champagne and various glowing wines are likely to age better If they're kept upright.[1] This is due to The inner tension due to the trapped carbonic gas gives ample humidity and safety from oxygen. Caterer Magazine claims which the preference for upright storage of Champagne is shared from the Comité Interprofessionnel du Vin de Champagne (CIVC) who performed an extensive review of Champagnes which were stored in various situations and orientations.
Host your individual chocolate and wine social gathering pairing different wines and chocolates. You can't go Improper, helpful resources and any blend will be a winner with your company.


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