Pub Date : 1992-06-15DOI: 10.1109/CVPR.1992.223226
R. Ogniewicz, M. Ilg
A novel method of robust skeletonization based on the Voronoi diagram of boundary points, which is characterized by correct Euclidean metries and inherent preservation of connectivity, is presented. The regularization of the Voronoi medial axis (VMA) in the sense of H. Blum's (1967) prairie fire analogy is done by attributing to each component of the VMA a measure of prominence and stability. The resulting Voronoi skeletons appear largely invariant with respect to typical noise conditions in the image and geometric transformations. Hierarchical clustering of the skeleton branches, the so-called skeleton pyramid, leads to further simplification of the skeleton. Several applications demonstrate the suitability of the Voronoi skeleton to higher-order tasks such as object recognition.<>
{"title":"Voronoi skeletons: theory and applications","authors":"R. Ogniewicz, M. Ilg","doi":"10.1109/CVPR.1992.223226","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223226","url":null,"abstract":"A novel method of robust skeletonization based on the Voronoi diagram of boundary points, which is characterized by correct Euclidean metries and inherent preservation of connectivity, is presented. The regularization of the Voronoi medial axis (VMA) in the sense of H. Blum's (1967) prairie fire analogy is done by attributing to each component of the VMA a measure of prominence and stability. The resulting Voronoi skeletons appear largely invariant with respect to typical noise conditions in the image and geometric transformations. Hierarchical clustering of the skeleton branches, the so-called skeleton pyramid, leads to further simplification of the skeleton. Several applications demonstrate the suitability of the Voronoi skeleton to higher-order tasks such as object recognition.<<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":"129550222","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.1992.223198
H. Sato, T. Binford
Algorithms to detect pairs of edges that could be ends of a straight homogeneous generalized cylinder (SHGC) are proposed. Geometrical constraints for the ends of an SHGC are utilized to group edge elements and edge segments in a complex image. Two methods are investigated. The first algorithm is for a subset of SHGCs for which scaling factors of the cross section at both ends are the same. The second algorithm is for any SHGC. However, a modified version is implemented to reduce computation; given a reference and edge, it finds the edges possibly paired with it. Several examples of ends extracted from real images are reported to show the feasibility of the algorithm.<>
{"title":"On finding the ends of straight homogeneous generalized cylinders","authors":"H. Sato, T. Binford","doi":"10.1109/CVPR.1992.223198","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223198","url":null,"abstract":"Algorithms to detect pairs of edges that could be ends of a straight homogeneous generalized cylinder (SHGC) are proposed. Geometrical constraints for the ends of an SHGC are utilized to group edge elements and edge segments in a complex image. Two methods are investigated. The first algorithm is for a subset of SHGCs for which scaling factors of the cross section at both ends are the same. The second algorithm is for any SHGC. However, a modified version is implemented to reduce computation; given a reference and edge, it finds the edges possibly paired with it. Several examples of ends extracted from real images are reported to show the feasibility of the algorithm.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"27 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":"127499856","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.1992.223123
R. Krishnapuram, Sundeep Gupta
Two morphological methods for edge detection in range images are proposed. The first method uses the opening and closing residues of structuring elements in orthogonal directions to detect roof and crease edges, and is essentially a morphological implementation of residue analysis techniques. The more general second method is based on a morphological version of the first derivative operator. This method utilizes dilation and erosion residues of structuring elements at different scales to reliably extract step edges along with roof edges and crease edges, and to classify each pixel as belonging to eight possible structure types: positive roof, negative roof, positive crease, negative crease, top of step, base of step, ramp, and constant surface. This method may be thought of as a morphological multiscale method.<>
{"title":"Edge detection in range images through morphological residue analysis","authors":"R. Krishnapuram, Sundeep Gupta","doi":"10.1109/CVPR.1992.223123","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223123","url":null,"abstract":"Two morphological methods for edge detection in range images are proposed. The first method uses the opening and closing residues of structuring elements in orthogonal directions to detect roof and crease edges, and is essentially a morphological implementation of residue analysis techniques. The more general second method is based on a morphological version of the first derivative operator. This method utilizes dilation and erosion residues of structuring elements at different scales to reliably extract step edges along with roof edges and crease edges, and to classify each pixel as belonging to eight possible structure types: positive roof, negative roof, positive crease, negative crease, top of step, base of step, ramp, and constant surface. This method may be thought of as a morphological multiscale method.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"285 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":"132485437","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.1992.223182
Gin-Shu Young, T. Hong, M. Herman, Jackson C. S. Yang
A technique for the calibration of an active camera system is presented. The calibration of manipulator, camera-to-manipulator, camera, and base-to-world is treated in a unified and elegant way. In this approach, the camera frames and manipulator link frames are all related to the world frame, therefore the camera-to-manipulator and base-to-world calibration is very straightforward. The approach is simple, since it uses the form of one equation solving one parameter. Two experiments that verify the accuracy of the technique are reported.<>
{"title":"Kinematic calibration of an active camera system","authors":"Gin-Shu Young, T. Hong, M. Herman, Jackson C. S. Yang","doi":"10.1109/CVPR.1992.223182","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223182","url":null,"abstract":"A technique for the calibration of an active camera system is presented. The calibration of manipulator, camera-to-manipulator, camera, and base-to-world is treated in a unified and elegant way. In this approach, the camera frames and manipulator link frames are all related to the world frame, therefore the camera-to-manipulator and base-to-world calibration is very straightforward. The approach is simple, since it uses the form of one equation solving one parameter. Two experiments that verify the accuracy of the technique are reported.<<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":"131703238","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.1992.223134
X. Zhuang
The author attempts to solve the structuring function decomposition problem where the structuring function refers to the gray scale structuring element. A morphologically realizable representation for the structuring function that reduces the structuring function decomposition into a series of binary structuring element decompositions is presented. Recursive algorithms that are pipelinable for efficiently performing gray scale morphological operations are developed on the basis of proposed representation and decomposition. The results are beneficial to real-time image analysis in terms of computer architecture and software development.<>
{"title":"Morphological structuring function decomposition","authors":"X. Zhuang","doi":"10.1109/CVPR.1992.223134","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223134","url":null,"abstract":"The author attempts to solve the structuring function decomposition problem where the structuring function refers to the gray scale structuring element. A morphologically realizable representation for the structuring function that reduces the structuring function decomposition into a series of binary structuring element decompositions is presented. Recursive algorithms that are pipelinable for efficiently performing gray scale morphological operations are developed on the basis of proposed representation and decomposition. The results are beneficial to real-time image analysis in terms of computer architecture and software development.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"17 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":"129201639","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.1992.223262
J. Krumm, S. Shafer
It is shown how local spatial image frequency is related to the surface normal of a textured surface. It is found that the Fourier power spectra of any two similarly textured patches on a plane are approximately related to each other by an affine transformation. The transformation parameters are a function of the plane's surface normal. This relationship is used as the basis of an algorithm for finding surface normals of textured shapes using the spectrogram, which is one type of local spatial frequency representation. The relationship is validated by testing the algorithm on real textures. By analyzing shape and texture in terms of the local spatial frequency representation, the advantages of the representation for the shape-from-texture problem can be exploited. Specifically, the algorithm requires no feature detection and can give correct results even when the texture is aliased.<>
{"title":"Shape from periodic texture using the spectrogram","authors":"J. Krumm, S. Shafer","doi":"10.1109/CVPR.1992.223262","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223262","url":null,"abstract":"It is shown how local spatial image frequency is related to the surface normal of a textured surface. It is found that the Fourier power spectra of any two similarly textured patches on a plane are approximately related to each other by an affine transformation. The transformation parameters are a function of the plane's surface normal. This relationship is used as the basis of an algorithm for finding surface normals of textured shapes using the spectrogram, which is one type of local spatial frequency representation. The relationship is validated by testing the algorithm on real textures. By analyzing shape and texture in terms of the local spatial frequency representation, the advantages of the representation for the shape-from-texture problem can be exploited. Specifically, the algorithm requires no feature detection and can give correct results even when the texture is aliased.<<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":"121104186","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.1992.223168
K. Tarabanis, R. Tsai
Methods for computing the locus of all viewpoints from which features on known polyhedral objects can be viewed in their entirety without being occluded by anything in the environment are presented. Convex and concave polyhedra with or without holes and the viewing model of perspective projection are used in this work. Based on properties of the occlusion-free and occluded loci of viewpoints, two methods for constructing these loci together with their complexity analysis are presented. In one method, a boundary representation of the occlusion-free locus is obtained. In the other, the locus of occluded viewpoints is expressed in terms of a constructive solid geometry representation that consists of a union of component solids. Implementation results are shown.<>
{"title":"Computing occlusion-free viewpoints","authors":"K. Tarabanis, R. Tsai","doi":"10.1109/CVPR.1992.223168","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223168","url":null,"abstract":"Methods for computing the locus of all viewpoints from which features on known polyhedral objects can be viewed in their entirety without being occluded by anything in the environment are presented. Convex and concave polyhedra with or without holes and the viewing model of perspective projection are used in this work. Based on properties of the occlusion-free and occluded loci of viewpoints, two methods for constructing these loci together with their complexity analysis are presented. In one method, a boundary representation of the occlusion-free locus is obtained. In the other, the locus of occluded viewpoints is expressed in terms of a constructive solid geometry representation that consists of a union of component solids. Implementation results are shown.<<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":"121833672","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.1992.223224
Y. Hel-Or, M. Werman
A general and flexible method for fusing and integrating different 2D and 3D measurements for pose estimation is proposed. The 2D measured data are viewed as 3D data with infinite uncertainty in a particular direction. This representation unifies the two categories of the absolute orientation problem into a single problem that varies only in the uncertainty values associated with the measurements. With this paradigm a uniform mathematical formulation of the problem is obtained, and different kinds of measurements that can be fused to obtain a better solution. The method, which is implemented using Kalman filtering, is robust and easily parallelizable.<>
{"title":"Absolute orientation from uncertain point data: a unified approach","authors":"Y. Hel-Or, M. Werman","doi":"10.1109/CVPR.1992.223224","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223224","url":null,"abstract":"A general and flexible method for fusing and integrating different 2D and 3D measurements for pose estimation is proposed. The 2D measured data are viewed as 3D data with infinite uncertainty in a particular direction. This representation unifies the two categories of the absolute orientation problem into a single problem that varies only in the uncertainty values associated with the measurements. With this paradigm a uniform mathematical formulation of the problem is obtained, and different kinds of measurements that can be fused to obtain a better solution. The method, which is implemented using Kalman filtering, is robust and easily parallelizable.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"381 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":"121769232","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.1992.223272
M. Irani, Shmuel Peleg
A method for detecting and tracking multiple moving objects, using both a large spatial region and a large temporal region, without assuming temporal motion constancy is described. When the large spatial region of analysis has multiple moving objects, the motion parameters and the locations of the objects are computed for one object after another. A method for segmenting the image plane into differently moving objects and computing their motions using two frames is presented. The tracking of detected objects using temporal integration and the algorithms for enhancement of tracked objects by filling-in occluded regions and by improving the spatial resolution of the imaged objects are described.<>
{"title":"Image sequence enhancement using multiple motions analysis","authors":"M. Irani, Shmuel Peleg","doi":"10.1109/CVPR.1992.223272","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223272","url":null,"abstract":"A method for detecting and tracking multiple moving objects, using both a large spatial region and a large temporal region, without assuming temporal motion constancy is described. When the large spatial region of analysis has multiple moving objects, the motion parameters and the locations of the objects are computed for one object after another. A method for segmenting the image plane into differently moving objects and computing their motions using two frames is presented. The tracking of detected objects using temporal integration and the algorithms for enhancement of tracked objects by filling-in occluded regions and by improving the spatial resolution of the imaged objects are described.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"52-54 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":"132215017","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.1992.223124
T. Kanungo, R. Haralick
Restricted domains, which are a restricted class of 2-D shapes, are defined. It is proved that any restricted domain can be decomposed as n-fold dilations of thirteen basis structuring elements and hence can be represented in a thirteen-dimensional space. This thirteen-dimensional space is spanned by the thirteen basis structuring elements comprising of lines, triangles, and a rhombus. It is shown that there is a linear transformation from this thirteen-dimensional space to an eight-dimensional space wherein a restricted domain is represented in terms of its side lengths. Furthermore, the decomposition in general is not unique, and all the decompositions can be constructed by finding the homogeneous solutions of the transformation and adding it to a particular solution. An algorithm for finding all possible decompositions is provided.<>
{"title":"Morphological decomposition of restricted domains: a vector space solution","authors":"T. Kanungo, R. Haralick","doi":"10.1109/CVPR.1992.223124","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223124","url":null,"abstract":"Restricted domains, which are a restricted class of 2-D shapes, are defined. It is proved that any restricted domain can be decomposed as n-fold dilations of thirteen basis structuring elements and hence can be represented in a thirteen-dimensional space. This thirteen-dimensional space is spanned by the thirteen basis structuring elements comprising of lines, triangles, and a rhombus. It is shown that there is a linear transformation from this thirteen-dimensional space to an eight-dimensional space wherein a restricted domain is represented in terms of its side lengths. Furthermore, the decomposition in general is not unique, and all the decompositions can be constructed by finding the homogeneous solutions of the transformation and adding it to a particular solution. An algorithm for finding all possible decompositions is provided.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"65 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":"115024385","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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