Pub Date : 1992-06-15DOI: 10.1109/CVPR.1992.223186
M. Swain, R. E. Kahn, D. Ballard
The high-resolution field of view of the human eye only covers a tiny fraction of the total field of view, which allows for great economy in computational resources but forces the visual system to solve other problems that would not exist with uniformly high resolution. One of these is how to determine where to redirect the fovea, given only the low-resolution information available in the periphery. The advent of spatially variant receptor arrays for cameras has made it imperative that computational solutions to this problem be found. Color has been traditionally associated with foveal vision, but it is shown that color cues are well preserved under low resolution, and an algorithm for locating objects based on color histograms that is both effective under low resolution and computationally efficient is illustrated.<>
{"title":"Low resolution cues for guiding saccadic eye movements","authors":"M. Swain, R. E. Kahn, D. Ballard","doi":"10.1109/CVPR.1992.223186","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223186","url":null,"abstract":"The high-resolution field of view of the human eye only covers a tiny fraction of the total field of view, which allows for great economy in computational resources but forces the visual system to solve other problems that would not exist with uniformly high resolution. One of these is how to determine where to redirect the fovea, given only the low-resolution information available in the periphery. The advent of spatially variant receptor arrays for cameras has made it imperative that computational solutions to this problem be found. Color has been traditionally associated with foveal vision, but it is shown that color cues are well preserved under low resolution, and an algorithm for locating objects based on color histograms that is both effective under low resolution and computationally efficient is illustrated.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126209375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1992-06-15DOI: 10.1109/CVPR.1992.223132
D. Schonfeld
A mathematical framework for the solution of statistical inference problems on a class of random sets is proposed. It is based on a new definition of expected pattern. The least-mean-difference estimator (restoration filter) is proved, under certain conditions, to be equivalent to the minimization of the measure of size (area) of the set-difference between the original pattern and the expected pattern of the estimated (restored) pattern. Consequently, it is proved that, under certain conditions, if the estimator (restoration filter) is unbiased, then it is the least mean difference estimator (restoration filter).<>
{"title":"Optimal nonlinear pattern restoration from noisy binary figures","authors":"D. Schonfeld","doi":"10.1109/CVPR.1992.223132","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223132","url":null,"abstract":"A mathematical framework for the solution of statistical inference problems on a class of random sets is proposed. It is based on a new definition of expected pattern. The least-mean-difference estimator (restoration filter) is proved, under certain conditions, to be equivalent to the minimization of the measure of size (area) of the set-difference between the original pattern and the expected pattern of the estimated (restored) pattern. Consequently, it is proved that, under certain conditions, if the estimator (restoration filter) is unbiased, then it is the least mean difference estimator (restoration filter).<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126397864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1992-06-15DOI: 10.1109/CVPR.1992.223260
B. Super, A. Bovik
A closed-form solution to the problem of computing 3D curved-surface shape from texture cues is presented. An expression showing the dependence of localized image spectral moments on localized surface spectral moments and on local surface orientation is derived. The local image spectra are measured with wavelets, and the expression is solved for the surface orientation at each point. Because the method uses localized spectral information, it operates at a very low level in the visual hierarchy. No extraction of texture or edge elements is required. The wavelet-based computation used is biologically plausible, easily parallelized for rapid computation, and has been shown to be the basis for effective solutions to a variety of other vision tasks. The method is demonstrated on a number of real-world examples.<>
{"title":"Shape-from-texture by wavelet-based measurement of local spectral moments","authors":"B. Super, A. Bovik","doi":"10.1109/CVPR.1992.223260","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223260","url":null,"abstract":"A closed-form solution to the problem of computing 3D curved-surface shape from texture cues is presented. An expression showing the dependence of localized image spectral moments on localized surface spectral moments and on local surface orientation is derived. The local image spectra are measured with wavelets, and the expression is solved for the surface orientation at each point. Because the method uses localized spectral information, it operates at a very low level in the visual hierarchy. No extraction of texture or edge elements is required. The wavelet-based computation used is biologically plausible, easily parallelized for rapid computation, and has been shown to be the basis for effective solutions to a variety of other vision tasks. The method is demonstrated on a number of real-world examples.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126754128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1992-06-15DOI: 10.1109/CVPR.1992.223180
U. R. Dhond, J. Aggarwal
Contemporary stereo correspondence algorithms disambiguate multiple candidate matches by using a spatial hierarchy mechanism. Narrow occluding objects in 3-D scenes cannot be handled by the spatial hierarchy mechanism alone. The authors propose a dynamic disparity search (DDS) framework that combines the spatial hierarchy mechanism with a new disparity hierarchy mechanism, to reduce the stereo matching errors caused by narrow occluding objects. They demonstrate the merits of the DDS approach on real stereo images.<>
{"title":"Computing stereo correspondences in the presence of narrow occluding objects","authors":"U. R. Dhond, J. Aggarwal","doi":"10.1109/CVPR.1992.223180","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223180","url":null,"abstract":"Contemporary stereo correspondence algorithms disambiguate multiple candidate matches by using a spatial hierarchy mechanism. Narrow occluding objects in 3-D scenes cannot be handled by the spatial hierarchy mechanism alone. The authors propose a dynamic disparity search (DDS) framework that combines the spatial hierarchy mechanism with a new disparity hierarchy mechanism, to reduce the stereo matching errors caused by narrow occluding objects. They demonstrate the merits of the DDS approach on real stereo images.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114179859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1992-06-15DOI: 10.1109/CVPR.1992.223212
K. Ikeuchi, T. Suehiro
A novel method for programming a robot, called the assembly-plan-from-observation (APO) method, is proposed. The APO method aims to build a system that has the capability of observing a human performing an assembly task, understanding the task based on the observation, and generating the robot program to achieve the same task. Assembly relations that serve as the basic representation of each assembly task are defined. It is verified that such assembly relations can be recovered from the observation of human assembly tasks, and that from such assembly relations it is possible to generate robot motion commands to repeat the same assembly task. An APO system based on the assembly relations is demonstrated.<>
{"title":"Recognizing assembly tasks using face-contact relations","authors":"K. Ikeuchi, T. Suehiro","doi":"10.1109/CVPR.1992.223212","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223212","url":null,"abstract":"A novel method for programming a robot, called the assembly-plan-from-observation (APO) method, is proposed. The APO method aims to build a system that has the capability of observing a human performing an assembly task, understanding the task based on the observation, and generating the robot program to achieve the same task. Assembly relations that serve as the basic representation of each assembly task are defined. It is verified that such assembly relations can be recovered from the observation of human assembly tasks, and that from such assembly relations it is possible to generate robot motion commands to repeat the same assembly task. An APO system based on the assembly relations is demonstrated.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126982291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1992-06-15DOI: 10.1109/CVPR.1992.223195
T. A. Mancini, L. B. Wolff
A methodology for accurate determination of surface normals and light source location from depth and reflectance data is introduced. Estimation of local surface orientation using depth data alone from range finders with standard depth errors can produce significant error, while shape-from-shading using reflectance data alone produces approximate surface orientation results that are highly dependent on the correct initial surface orientation estimates and regularization parameters. Combining these two sources of information gives vastly more accurate surface orientation estimates under general conditions than either one alone, and can also provide better knowledge of local curvature. Novel iterative methods that enforce satisfaction of the image irradiance equation and surface integrability without using regularization are proposed. These iterative methods work when the light source is any finite distance from the object, producing variable incident light orientation over the object.<>
{"title":"3 D shape and light source location from depth and reflectance","authors":"T. A. Mancini, L. B. Wolff","doi":"10.1109/CVPR.1992.223195","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223195","url":null,"abstract":"A methodology for accurate determination of surface normals and light source location from depth and reflectance data is introduced. Estimation of local surface orientation using depth data alone from range finders with standard depth errors can produce significant error, while shape-from-shading using reflectance data alone produces approximate surface orientation results that are highly dependent on the correct initial surface orientation estimates and regularization parameters. Combining these two sources of information gives vastly more accurate surface orientation estimates under general conditions than either one alone, and can also provide better knowledge of local curvature. Novel iterative methods that enforce satisfaction of the image irradiance equation and surface integrability without using regularization are proposed. These iterative methods work when the light source is any finite distance from the object, producing variable incident light orientation over the object.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129149813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1992-06-15DOI: 10.1109/CVPR.1992.223253
G. Gordon
Face recognition from a representation based on features extracted from range images is explored. Depth and curvature features have several advantages over more traditional intensity-based features. Specifically, curvature descriptors have the potential for higher accuracy in describing surface-based events, are better suited to describe properties of the face in areas such as the cheeks, forehead, and chin, and are viewpoint invariant. Faces are represented in terms of a vector of feature descriptors. Comparisons between two faces is made based on their relationship in the feature space. The author provides a detailed analysis of the accuracy and discrimination of the particular features extracted, and the effectiveness of the recognition system for a test database of 24 faces. Recognition rates are in the range of 80% to 100%. In many cases, feature accuracy is limited more by surface resolution than by the extraction process.<>
{"title":"Face recognition based on depth and curvature features","authors":"G. Gordon","doi":"10.1109/CVPR.1992.223253","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223253","url":null,"abstract":"Face recognition from a representation based on features extracted from range images is explored. Depth and curvature features have several advantages over more traditional intensity-based features. Specifically, curvature descriptors have the potential for higher accuracy in describing surface-based events, are better suited to describe properties of the face in areas such as the cheeks, forehead, and chin, and are viewpoint invariant. Faces are represented in terms of a vector of feature descriptors. Comparisons between two faces is made based on their relationship in the feature space. The author provides a detailed analysis of the accuracy and discrimination of the particular features extracted, and the effectiveness of the recognition system for a test database of 24 faces. Recognition rates are in the range of 80% to 100%. In many cases, feature accuracy is limited more by surface resolution than by the extraction process.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131442053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1992-06-15DOI: 10.1109/CVPR.1992.223202
J. Skrzypek, B. Ringer
A physiologically motivated model of illusory contour perception is examined by simulating a neural network architecture that was tested with gray-level images. The results indicate that a model that combines a bottom-up feature aggregation strategy with recurrent processing is best suited for describing this type of perceptual completion.<>
{"title":"Neural network models for illusory contour perception","authors":"J. Skrzypek, B. Ringer","doi":"10.1109/CVPR.1992.223202","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223202","url":null,"abstract":"A physiologically motivated model of illusory contour perception is examined by simulating a neural network architecture that was tested with gray-level images. The results indicate that a model that combines a bottom-up feature aggregation strategy with recurrent processing is best suited for describing this type of perceptual completion.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117167908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1992-06-15DOI: 10.1109/CVPR.1992.223189
M. Asada, Takayuki Nakamura, Y. Shirai
Weak Lambertian assumption is proposed and used to determine shape and pose of cylindrical objects from a monocular intensity image. The method does not require the knowledge of lighting conditions (light intensity and lighting direction), surface properties, or albedos. Experimental results for both synthesized and real images showing the validity of the method are presented.<>
{"title":"Weak Lambertian assumption for determining cylindrical shape and pose from shading and contour","authors":"M. Asada, Takayuki Nakamura, Y. Shirai","doi":"10.1109/CVPR.1992.223189","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223189","url":null,"abstract":"Weak Lambertian assumption is proposed and used to determine shape and pose of cylindrical objects from a monocular intensity image. The method does not require the knowledge of lighting conditions (light intensity and lighting direction), surface properties, or albedos. Experimental results for both synthesized and real images showing the validity of the method are presented.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116738544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1992-06-15DOI: 10.1109/CVPR.1992.223136
W. Costa, R. Haralick
The opening of a model signal with a convex, zero-height structuring element is studied empirically. Experiments are performed in which the input signal model parameters and the opening length are varied over an acceptable range and the corresponding grey level distributions in the opened signal are fit to Pearson distributions. Regressions are then used to relate the Pearson distribution parameters to the input parameters, resulting in equations that may be used to predict the effect of an opening. Characterization experiments show that the maximum absolute errors between actual and predicted cumulative distributions using these regression equations have a mean of 0.036 and a standard deviation of 0.011 (for a range of zero to one); the worst-case maximum absolute error encountered between the cumulative distributions is 0.066.<>
{"title":"Predicting expected gray level statistics of opened signals","authors":"W. Costa, R. Haralick","doi":"10.1109/CVPR.1992.223136","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223136","url":null,"abstract":"The opening of a model signal with a convex, zero-height structuring element is studied empirically. Experiments are performed in which the input signal model parameters and the opening length are varied over an acceptable range and the corresponding grey level distributions in the opened signal are fit to Pearson distributions. Regressions are then used to relate the Pearson distribution parameters to the input parameters, resulting in equations that may be used to predict the effect of an opening. Characterization experiments show that the maximum absolute errors between actual and predicted cumulative distributions using these regression equations have a mean of 0.036 and a standard deviation of 0.011 (for a range of zero to one); the worst-case maximum absolute error encountered between the cumulative distributions is 0.066.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115136226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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