Pub Date : 1992-06-15DOI: 10.1109/CVPR.1992.223126
D. Mintz, P. Meer, A. Rosenfeld
The robust least-median-of-squares (LMedS) estimator, which can recover a model representing only half the data points, was recently introduced in computer vision. Image data, however, is usually also corrupted by a zero-mean random process (noise) accounting for the measurement uncertainties. It is shown that in the presence of significant noise, LMedS loses its high breakdown point property. A different, two-stage approach in which the uncertainty due to noise is reduced before applying the simplest LMedS procedure is proposed. The superior performance of the technique is proved by comparative graphs.<>
{"title":"Analysis of the least median of squares estimator for computer vision applications","authors":"D. Mintz, P. Meer, A. Rosenfeld","doi":"10.1109/CVPR.1992.223126","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223126","url":null,"abstract":"The robust least-median-of-squares (LMedS) estimator, which can recover a model representing only half the data points, was recently introduced in computer vision. Image data, however, is usually also corrupted by a zero-mean random process (noise) accounting for the measurement uncertainties. It is shown that in the presence of significant noise, LMedS loses its high breakdown point property. A different, two-stage approach in which the uncertainty due to noise is reduced before applying the simplest LMedS procedure is proposed. The superior performance of the technique is proved by comparative graphs.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"16 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":"114260877","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.1992.223130
L. Cohen, I. Cohen
A 3-D generalization of the balloon model as a 3-D deformable surface, which evolves in 3-D images, is presented. It is deformed under the action of internal and external forces attracting the surface toward detected edge elements by means of an attraction potential. To solve the minimization problem for a surface, two simplified approaches are shown, defining a 3-D surface as a series of 2-D planar curves. Then the 3-D model is solved using the finite-element method, yielding greater stability and faster convergence. This model has been used to segment magnetic resonance images.<>
{"title":"Deformable models for 3-D medical images using finite elements and balloons","authors":"L. Cohen, I. Cohen","doi":"10.1109/CVPR.1992.223130","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223130","url":null,"abstract":"A 3-D generalization of the balloon model as a 3-D deformable surface, which evolves in 3-D images, is presented. It is deformed under the action of internal and external forces attracting the surface toward detected edge elements by means of an attraction potential. To solve the minimization problem for a surface, two simplified approaches are shown, defining a 3-D surface as a series of 2-D planar curves. Then the 3-D model is solved using the finite-element method, yielding greater stability and faster convergence. This model has been used to segment magnetic resonance images.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2002 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":"123891981","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.1992.223122
L. Vincent
Gray-scale reconstruction is formally defined for discrete images. A brief summary of the existing techniques to compute it is provided, and a hybrid algorithm that is an order of magnitude faster than any other algorithm is introduced. Some of its application to image filtering and segmentation are listed.<>
{"title":"Morphological grayscale reconstruction: definition, efficient algorithm and applications in image analysis","authors":"L. Vincent","doi":"10.1109/CVPR.1992.223122","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223122","url":null,"abstract":"Gray-scale reconstruction is formally defined for discrete images. A brief summary of the existing techniques to compute it is provided, and a hybrid algorithm that is an order of magnitude faster than any other algorithm is introduced. Some of its application to image filtering and segmentation are listed.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"10 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":"116866583","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.1992.223210
D. Mintz
A robust algorithm for edge detection is presented. The algorithm detects both roof- and step-type edges. A pixel is declared as an edge pixel if there is a consensus between different processes that try to determine if the pixel lies on a discontinuity. Robust methods are used to estimate local fits to windows in the pixel's neighborhood and accumulate votes from each fit. The use of robust estimators allows the transformation of any window possibly containing a discontinuity to a binary window containing a step edge in the location of the discontinuity. Conventional methods are used to detect this step edge.<>
{"title":"Robust consensus based edge detection","authors":"D. Mintz","doi":"10.1109/CVPR.1992.223210","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223210","url":null,"abstract":"A robust algorithm for edge detection is presented. The algorithm detects both roof- and step-type edges. A pixel is declared as an edge pixel if there is a consensus between different processes that try to determine if the pixel lies on a discontinuity. Robust methods are used to estimate local fits to windows in the pixel's neighborhood and accumulate votes from each fit. The use of robust estimators allows the transformation of any window possibly containing a discontinuity to a binary window containing a step edge in the location of the discontinuity. Conventional methods are used to detect this step edge.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"81 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":"115123451","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.1992.223201
T. Boult, G. Wolberg
The use of image warping to reduce the impact of chromatic aberration in vision applications is addressed. The warp is determined using edge displacements which are fit with cubic splines. An image reconstruction algorithm is used for nonlinear resampling. The main contribution of this work is to analyze the quality of the warping approach by comparing it with active lens control. Test results for two different imaging systems are reported.<>
{"title":"Correcting chromatic aberrations using image warping","authors":"T. Boult, G. Wolberg","doi":"10.1109/CVPR.1992.223201","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223201","url":null,"abstract":"The use of image warping to reduce the impact of chromatic aberration in vision applications is addressed. The warp is determined using edge displacements which are fit with cubic splines. An image reconstruction algorithm is used for nonlinear resampling. The main contribution of this work is to analyze the quality of the warping approach by comparing it with active lens control. Test results for two different imaging systems are reported.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"438 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":"123014876","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.1992.223228
H. Rom, G. Medioni
The problem of obtaining intuitive descriptions of planar shapes is addressed. In particular, a method for producing a segmented axial description of a given shape together with a hierarchical decomposition of the shape into its parts is suggested. Smooth local symmetries are used for the axial description of parts. Parallel symmetries are used to provide information on global relationships within the shape. It is assumed that the shape is a closed planar curve. The approach uses both region and contour information, can handle shapes with corners, and addresses the issues of local versus global information, the issue of scale and the notion of part. The method is computationally efficient, robust, and stable. Results showing that it provides an intuitive shape description are presented.<>
{"title":"Hierarchical decomposition and axial shape description","authors":"H. Rom, G. Medioni","doi":"10.1109/CVPR.1992.223228","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223228","url":null,"abstract":"The problem of obtaining intuitive descriptions of planar shapes is addressed. In particular, a method for producing a segmented axial description of a given shape together with a hierarchical decomposition of the shape into its parts is suggested. Smooth local symmetries are used for the axial description of parts. Parallel symmetries are used to provide information on global relationships within the shape. It is assumed that the shape is a closed planar curve. The approach uses both region and contour information, can handle shapes with corners, and addresses the issues of local versus global information, the issue of scale and the notion of part. The method is computationally efficient, robust, and stable. Results showing that it provides an intuitive shape description are presented.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"9 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":"115565310","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.1992.223138
Andrew Stein, M. Werman
The concept of robustness in statistics is examined. Starting from the concepts of the breakdown point and equivariance properties of an estimator, the desired equivariance properties for shape fitting are defined, and high breakdown point methods with these properties are found.<>
{"title":"Robust statistics in shape fitting","authors":"Andrew Stein, M. Werman","doi":"10.1109/CVPR.1992.223138","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223138","url":null,"abstract":"The concept of robustness in statistics is examined. Starting from the concepts of the breakdown point and equivariance properties of an estimator, the desired equivariance properties for shape fitting are defined, and high breakdown point methods with these properties are found.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"29 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":"127539063","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.1992.223266
E. Saund
An algorithm for labeling curvilinear structure at multiple scales in line drawings and edge images is presented. Symbolic curve-element tokens residing in a spatially indexed and scale-indexed data structure denote circular arcs fit to image data. Tokens are computed via a small-to-large scale grouping procedure using a greedy best-first strategy for choosing the support of new tokens. The resulting image description is rich and redundant in that a given segment of image contour may be described by multiple tokens at different scales, and by more than one token at any given scale. This property facilitates selection and characterization of portions of the image based on curve-element attributes.<>
{"title":"Labeling of curvilinear structure across scales by token grouping","authors":"E. Saund","doi":"10.1109/CVPR.1992.223266","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223266","url":null,"abstract":"An algorithm for labeling curvilinear structure at multiple scales in line drawings and edge images is presented. Symbolic curve-element tokens residing in a spatially indexed and scale-indexed data structure denote circular arcs fit to image data. Tokens are computed via a small-to-large scale grouping procedure using a greedy best-first strategy for choosing the support of new tokens. The resulting image description is rich and redundant in that a given segment of image contour may be described by multiple tokens at different scales, and by more than one token at any given scale. This property facilitates selection and characterization of portions of the image based on curve-element attributes.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"75 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":"115952040","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.1992.223194
M. Vriesenga, G. Healey, K. Peleg, J. Sklansky
The authors describe how to design color illumination to improve the discriminability of objects in color images. This procedure is useful in applications where the illumination can be controlled, such as inspection tasks. From the physics of color image formation, the optimal color illumination for discriminating materials is derived using a parametrically defined set of illuminants. The authors suggest how such an approach might be extended to sets of materials and more general classes of light sources. Experiments with painted color patches and live potato plantlets are used to illustrate the usefulness of actively controlling illumination color in machine vision.<>
{"title":"Controlling illumination color to enhance object discriminability","authors":"M. Vriesenga, G. Healey, K. Peleg, J. Sklansky","doi":"10.1109/CVPR.1992.223194","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223194","url":null,"abstract":"The authors describe how to design color illumination to improve the discriminability of objects in color images. This procedure is useful in applications where the illumination can be controlled, such as inspection tasks. From the physics of color image formation, the optimal color illumination for discriminating materials is derived using a parametrically defined set of illuminants. The authors suggest how such an approach might be extended to sets of materials and more general classes of light sources. Experiments with painted color patches and live potato plantlets are used to illustrate the usefulness of actively controlling illumination color in machine vision.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"14 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":"128206230","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.1992.223158
A. Young, L. Axel
A measure of deformation energy suitable for fitting deformable models to image data is described. An object's displacement is constrained to be globally smooth by penalizing the variation of the deformation gradient tensor. This homogeneous deformation measure is invariant to arbitrary rigid body motion of object and viewpoint, given the correspondence between model and data. It remains quadratic in the displacement parameters, leading to linear-least-squares fits. The method was used to reconstruct the nonhomogeneous 3-D motion of the heart wall from tomographic magnetic resonance images. A finite-element model of the left ventricle was deformed to fit material points tracked in biplanar views. Only the in-plane components were available from each separate image, the through-plane components being reconstructed in the fit.<>
{"title":"Non-rigid heart wall motion using MR tagging","authors":"A. Young, L. Axel","doi":"10.1109/CVPR.1992.223158","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223158","url":null,"abstract":"A measure of deformation energy suitable for fitting deformable models to image data is described. An object's displacement is constrained to be globally smooth by penalizing the variation of the deformation gradient tensor. This homogeneous deformation measure is invariant to arbitrary rigid body motion of object and viewpoint, given the correspondence between model and data. It remains quadratic in the displacement parameters, leading to linear-least-squares fits. The method was used to reconstruct the nonhomogeneous 3-D motion of the heart wall from tomographic magnetic resonance images. A finite-element model of the left ventricle was deformed to fit material points tracked in biplanar views. Only the in-plane components were available from each separate image, the through-plane components being reconstructed in the fit.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"202 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":"134331541","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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