Pub Date : 1992-06-15DOI: 10.1109/CVPR.1992.223162
B. S. Manjunath, R. Chellappa, C. Malsburg
A feature-based approach to face recognition in which the features are derived from the intensity data without assuming any knowledge of the face structure is presented. The feature extraction model is biologically motivated, and the locations of the features often correspond to salient facial features such as the eyes, nose, etc. Topological graphs are used to represent relations between features, and a simple deterministic graph-matching scheme that exploits the basic structure is used to recognize familiar faces from a database. Each of the stages in the system can be fully implemented in parallel to achieve real-time recognition. Experimental results for a 128*128 image with very little noise are evaluated.<>
{"title":"A feature based approach to face recognition","authors":"B. S. Manjunath, R. Chellappa, C. Malsburg","doi":"10.1109/CVPR.1992.223162","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223162","url":null,"abstract":"A feature-based approach to face recognition in which the features are derived from the intensity data without assuming any knowledge of the face structure is presented. The feature extraction model is biologically motivated, and the locations of the features often correspond to salient facial features such as the eyes, nose, etc. Topological graphs are used to represent relations between features, and a simple deterministic graph-matching scheme that exploits the basic structure is used to recognize familiar faces from a database. Each of the stages in the system can be fully implemented in parallel to achieve real-time recognition. Experimental results for a 128*128 image with very little noise are evaluated.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"38 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":"114244348","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.1992.223255
J. Burns, E. Riseman
The effective matching of a single 2D image of a cluttered scene to a library of multiple polyhedral models is achieved by organizing the 3D models into a network of descriptions of their 2D projections from expected views. The process of efficiently searching for image-model matches via a view description network is presented and demonstrated on images containing multiple objects and outdoor scenes. The experiments show that a recognition system based on view description networks is capable finding the correct matches to 3D objects in complex images with a potentially high level of efficiency.<>
{"title":"Matching complex images to multiple 3D objects using view description networks","authors":"J. Burns, E. Riseman","doi":"10.1109/CVPR.1992.223255","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223255","url":null,"abstract":"The effective matching of a single 2D image of a cluttered scene to a library of multiple polyhedral models is achieved by organizing the 3D models into a network of descriptions of their 2D projections from expected views. The process of efficiently searching for image-model matches via a view description network is presented and demonstrated on images containing multiple objects and outdoor scenes. The experiments show that a recognition system based on view description networks is capable finding the correct matches to 3D objects in complex images with a potentially high level of efficiency.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"12 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":"114442102","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.1992.223258
Hari N. Nair, C. Stewart
Depth maps obtained from focus ranging can have numerous errors and distortions due to edge bleeding, feature shifts, image noise, and field curvature. An improved algorithm that examines an initial high depth-of-field image of the scene to identify regions susceptible to edge bleeding and image noise is given. Focus evaluation windows are adapted to local image content and optimize the tradeoff between spatial resolution and noise sensitivity. An elliptical paraboloid field curvature model is used to reduce range distortion in peripheral image areas. Spatio-temporal tracking compensates for image feature shifts. The result is a sparse but reliable depth map.<>
{"title":"Robust focus ranging","authors":"Hari N. Nair, C. Stewart","doi":"10.1109/CVPR.1992.223258","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223258","url":null,"abstract":"Depth maps obtained from focus ranging can have numerous errors and distortions due to edge bleeding, feature shifts, image noise, and field curvature. An improved algorithm that examines an initial high depth-of-field image of the scene to identify regions susceptible to edge bleeding and image noise is given. Focus evaluation windows are adapted to local image content and optimize the tradeoff between spatial resolution and noise sensitivity. An elliptical paraboloid field curvature model is used to reduce range distortion in peripheral image areas. Spatio-temporal tracking compensates for image feature shifts. The result is a sparse but reliable depth map.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"1 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":"128985205","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.1992.223147
Kyoung Mu Lee, C.-C. Jay Kuo
Two iterative algorithms for shape reconstruction based on multiple images taken under different lighting conditions, known as photometric stereo, are proposed. It is shown that single-image shape-from-shading (SFS) algorithms have an inherent problem, i.e., the accuracy of the reconstructed surface height is related to the slope of the reflectance map function defined on the gradient space. This observation motivates the authors to generalize the single-image SFS algorithm to two photometric stereo SFS algorithms aiming at more accurate surface reconstruction. The two algorithms directly determine the surface height by minimizing a quadratic cost functional, which is defined to be the square of the brightness error obtained from each individual image in a parallel or cascade manner. The optimal illumination condition that leads to best shape reconstruction is examined.<>
{"title":"Shape reconstruction from photometric stereo","authors":"Kyoung Mu Lee, C.-C. Jay Kuo","doi":"10.1109/CVPR.1992.223147","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223147","url":null,"abstract":"Two iterative algorithms for shape reconstruction based on multiple images taken under different lighting conditions, known as photometric stereo, are proposed. It is shown that single-image shape-from-shading (SFS) algorithms have an inherent problem, i.e., the accuracy of the reconstructed surface height is related to the slope of the reflectance map function defined on the gradient space. This observation motivates the authors to generalize the single-image SFS algorithm to two photometric stereo SFS algorithms aiming at more accurate surface reconstruction. The two algorithms directly determine the surface height by minimizing a quadratic cost functional, which is defined to be the square of the brightness error obtained from each individual image in a parallel or cascade manner. The optimal illumination condition that leads to best shape reconstruction is examined.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"1 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":"129398793","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.1992.223141
Visvanathan Ramesh, R. Haralick
It is shown how random perturbation models can be set up for a vision algorithm sequence involving edge finding, edge linking, and gap filling. By starting with an appropriate noise model for the input data, the authors derive random perturbation models for the output data at each stage of their example sequence. These random perturbation models are useful for performing model-based theoretical comparisons of the performance of vision algorithms. Parameters of these random perturbation models are related to measures of error such as the probability of misdetection of feature units, probability of false alarm, and the probability of incorrect grouping. Since the parameters of the perturbation model at the output of an algorithm are indicators of the performance of the algorithm, one could utilize these models to automate the selection of various free parameters (thresholds) of the algorithm.<>
{"title":"Random perturbation models and performance characterization in computer vision","authors":"Visvanathan Ramesh, R. Haralick","doi":"10.1109/CVPR.1992.223141","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223141","url":null,"abstract":"It is shown how random perturbation models can be set up for a vision algorithm sequence involving edge finding, edge linking, and gap filling. By starting with an appropriate noise model for the input data, the authors derive random perturbation models for the output data at each stage of their example sequence. These random perturbation models are useful for performing model-based theoretical comparisons of the performance of vision algorithms. Parameters of these random perturbation models are related to measures of error such as the probability of misdetection of feature units, probability of false alarm, and the probability of incorrect grouping. Since the parameters of the perturbation model at the output of an algorithm are indicators of the performance of the algorithm, one could utilize these models to automate the selection of various free parameters (thresholds) of the algorithm.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"1 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":"130447793","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.1992.223144
S. B. Marapane, M. Trivedi
A computational framework for an accurate, robust, and efficient stereo approach is developed. Most of the deficiencies prevailing in current computational models of stereo can be attributed to their use of a single, typically edge-element-based, primitive for stereo analysis and to their use of a single-level control strategy. The multi-primitive hierarchical (MPH) framework for stereo analysis presented is directed toward overcoming these deficiencies. In the MPH model, stereo analysis is performed in multiple stages, incorporating multiple primitives and utilizing a hierarchical control strategy. The higher levels of the hierarchical system are based on primitives containing more semantic information, and the results of stereo analysis at higher levels are used for guidance at the lower levels. It is shown that such a stereo system is superior to a single-level, single-primitive system.<>
{"title":"Multi-primitive hierarchical (MPH) stereo system","authors":"S. B. Marapane, M. Trivedi","doi":"10.1109/CVPR.1992.223144","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223144","url":null,"abstract":"A computational framework for an accurate, robust, and efficient stereo approach is developed. Most of the deficiencies prevailing in current computational models of stereo can be attributed to their use of a single, typically edge-element-based, primitive for stereo analysis and to their use of a single-level control strategy. The multi-primitive hierarchical (MPH) framework for stereo analysis presented is directed toward overcoming these deficiencies. In the MPH model, stereo analysis is performed in multiple stages, incorporating multiple primitives and utilizing a hierarchical control strategy. The higher levels of the hierarchical system are based on primitives containing more semantic information, and the results of stereo analysis at higher levels are used for guidance at the lower levels. It is shown that such a stereo system is superior to a single-level, single-primitive system.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"23 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":"129579361","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.1992.223219
Charlie Rothwell, Andrew Zisserman, J. Mundy, D. Forsyth
Projectively invariant shape descriptors allow fast indexing into model libraries without the need for pose computation or camera calibration. Progress in building a model-based vision system for plane objects that uses algebraic projective invariants is described. A brief account of these descriptors is given, and the recognition system is described, giving examples of the invariant techniques working on real images.<>
{"title":"Efficient model library access by projectively invariant indexing functions","authors":"Charlie Rothwell, Andrew Zisserman, J. Mundy, D. Forsyth","doi":"10.1109/CVPR.1992.223219","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223219","url":null,"abstract":"Projectively invariant shape descriptors allow fast indexing into model libraries without the need for pose computation or camera calibration. Progress in building a model-based vision system for plane objects that uses algebraic projective invariants is described. A brief account of these descriptors is given, and the recognition system is described, giving examples of the invariant techniques working on real images.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"46 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":"131028932","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.1992.223267
Sudeep Sarkar, K. Boyer
It is shown that the formalism of Bayesian networks provides an elegant solution, in a probabilistic framework, to the problem of integrating top-down and bottom-up visual processes as well serving as a knowledge base. The formalism is modified to handle spatial data and thus extends the applicability of Bayesian networks to visual processing. The modified form is called the perceptual inference network (PIN). The theoretical background of a PIN is presented, and its viability is demonstrated in the context of perceptual organization. The PIN imparts an active inferential and integrating nature to perceptual organization.<>
{"title":"Perceptual organization using Bayesian networks","authors":"Sudeep Sarkar, K. Boyer","doi":"10.1109/CVPR.1992.223267","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223267","url":null,"abstract":"It is shown that the formalism of Bayesian networks provides an elegant solution, in a probabilistic framework, to the problem of integrating top-down and bottom-up visual processes as well serving as a knowledge base. The formalism is modified to handle spatial data and thus extends the applicability of Bayesian networks to visual processing. The modified form is called the perceptual inference network (PIN). The theoretical background of a PIN is presented, and its viability is demonstrated in the context of perceptual organization. The PIN imparts an active inferential and integrating nature to perceptual organization.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"5 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":"134594003","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.1992.223187
P. Tsai, M. Shah
A method for computing depth from a single shaded image is presented. Discrete approximations for p and q using finite differences are used, and the reflectance in Z/sub ij/ is linearized. The method is faster, since each operation is purely local. In addition, it gives good results for spherical surfaces, in contrast to other linear methods.<>
{"title":"A fast linear shape from shading","authors":"P. Tsai, M. Shah","doi":"10.1109/CVPR.1992.223187","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223187","url":null,"abstract":"A method for computing depth from a single shaded image is presented. Discrete approximations for p and q using finite differences are used, and the reflectance in Z/sub ij/ is linearized. The method is faster, since each operation is purely local. In addition, it gives good results for spherical surfaces, in contrast to other linear methods.<<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":"123470079","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.1992.223139
I. Herlin, C. Nguyen, C. Graffigne
The problem of improving an initial segmentation of medical data by making use of gray level, texture, and gradient information is addressed. The mathematical environment is that of Markov random fields and stochastic processes. This yields two major advantages: automatic selection of program parameters and ergonomic software that can be used to test homogeneity properties of regions. The method is applied to echocardiographic images in order to segment cardiac cavities.<>
{"title":"A deformable region model using stochastic processes applied to echocardiographic images","authors":"I. Herlin, C. Nguyen, C. Graffigne","doi":"10.1109/CVPR.1992.223139","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223139","url":null,"abstract":"The problem of improving an initial segmentation of medical data by making use of gray level, texture, and gradient information is addressed. The mathematical environment is that of Markov random fields and stochastic processes. This yields two major advantages: automatic selection of program parameters and ergonomic software that can be used to test homogeneity properties of regions. The method is applied to echocardiographic images in order to segment cardiac cavities.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"355 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":"127202669","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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