Pub Date : 1992-06-15DOI: 10.1109/CVPR.1992.223179
R. Hartley, R. Gupta, Tom Chang
The problem of computing placement of points in 3-D space, given two uncalibrated perspective views, is considered. The main theorem shows that the placement of the points is determined only up to an arbitrary projective transformation of 3-space. Given additional ground control points, however, the location of the points and the camera parameters may be determined. The method is linear and noniterative, whereas previously known methods for solving the camera calibration and placement problem to take proper account of both ground-control points and image correspondences are unsatisfactory in requiring either iterative methods or model restrictions. As a result of the main theorem, it is possible to determine projective invariants of 3-D geometric configurations from two perspective views.<>
{"title":"Stereo from uncalibrated cameras","authors":"R. Hartley, R. Gupta, Tom Chang","doi":"10.1109/CVPR.1992.223179","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223179","url":null,"abstract":"The problem of computing placement of points in 3-D space, given two uncalibrated perspective views, is considered. The main theorem shows that the placement of the points is determined only up to an arbitrary projective transformation of 3-space. Given additional ground control points, however, the location of the points and the camera parameters may be determined. The method is linear and noniterative, whereas previously known methods for solving the camera calibration and placement problem to take proper account of both ground-control points and image correspondences are unsatisfactory in requiring either iterative methods or model restrictions. As a result of the main theorem, it is possible to determine projective invariants of 3-D geometric configurations from two perspective views.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"31 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":"129279258","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.1992.223213
Rajeev Sharma, Y. Aloimonos
The development of the visual processes that would be needed by a mobile robot system for visually intercepting a moving target is considered. Many relevant active visual processes are proposed that provide robust input for qualitative motion control strategies. The processes for detecting independent motion and for monitoring progress toward the moving target are summarized.<>
{"title":"Visual motion analysis under interceptive behavior","authors":"Rajeev Sharma, Y. Aloimonos","doi":"10.1109/CVPR.1992.223213","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223213","url":null,"abstract":"The development of the visual processes that would be needed by a mobile robot system for visually intercepting a moving target is considered. Many relevant active visual processes are proposed that provide robust input for qualitative motion control strategies. The processes for detecting independent motion and for monitoring progress toward the moving target are summarized.<<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":"130385406","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.1992.223178
L. Matthies
To design high-performance obstacle detection systems for semi-autonomous navigation, it will be necessary to characterize the performance of obstacle detection sensors in quantitative, statistical terms and to develop design methodologies that relate task requirements (e.g., vehicle speed) to sensor system parameters (e.g., image resolution). Steps to be taken to realize such a methodology are outlined. For the specific case of obstacle detection with passive stereo range imagery, the development of the statistical models needed for the methodology is begun, and experimental results for outdoor images of a gravel road, which test the models empirically, are presented. The experimental results show sample error distributions for estimates of disparity and range, illustrate systematic errors caused by partial occlusion, and demonstrate that effective obstacle detection is achievable.<>
{"title":"Toward stochastic modeling of obstacle detectability in passive stereo range imagery","authors":"L. Matthies","doi":"10.1109/CVPR.1992.223178","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223178","url":null,"abstract":"To design high-performance obstacle detection systems for semi-autonomous navigation, it will be necessary to characterize the performance of obstacle detection sensors in quantitative, statistical terms and to develop design methodologies that relate task requirements (e.g., vehicle speed) to sensor system parameters (e.g., image resolution). Steps to be taken to realize such a methodology are outlined. For the specific case of obstacle detection with passive stereo range imagery, the development of the statistical models needed for the methodology is begun, and experimental results for outdoor images of a gravel road, which test the models empirically, are presented. The experimental results show sample error distributions for estimates of disparity and range, illustrate systematic errors caused by partial occlusion, and demonstrate that effective obstacle detection is achievable.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"7 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":"122208962","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.1992.223251
D. S. Chen, R. Jain, B. G. Schunck
A surface reconstruction method using multilayer feedforward neural networks is proposed. The parametric form represented by multilayer neural networks can model piecewise smooth surfaces in a way that is more general and flexible than many of the classical methods. The approximation method is based on a robust backpropagation (BP) algorithm, which extends the basic BP algorithm to handle errors, especially others, in the training data.<>
{"title":"Surface reconstruction using neural networks","authors":"D. S. Chen, R. Jain, B. G. Schunck","doi":"10.1109/CVPR.1992.223251","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223251","url":null,"abstract":"A surface reconstruction method using multilayer feedforward neural networks is proposed. The parametric form represented by multilayer neural networks can model piecewise smooth surfaces in a way that is more general and flexible than many of the classical methods. The approximation method is based on a robust backpropagation (BP) algorithm, which extends the basic BP algorithm to handle errors, especially others, in the training data.<<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":"130205187","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.1992.223172
Michael F. Polis, D. McKeown
A technique for producing a triangulated irregular network (TIN) from a digital elevation model (DEM) is described. The overall goal is to produce an approximate terrain description that preserves the major topographic features using a greatly reduced set of points selected from the original DEM. The TIN generation process is iterative; at each iteration, areas in the DEM that lie outside of a user-supplied error tolerance in the TIN are identified, and points are chosen from the DEM to more accurately model these areas. Point selection involves the computation of the difference between the actual DEM and an approximate DEM. This approximate DEM is calculated by interpolating elevation points from the TIN.<>
{"title":"Iterative TIN generation from digital evaluation models","authors":"Michael F. Polis, D. McKeown","doi":"10.1109/CVPR.1992.223172","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223172","url":null,"abstract":"A technique for producing a triangulated irregular network (TIN) from a digital elevation model (DEM) is described. The overall goal is to produce an approximate terrain description that preserves the major topographic features using a greatly reduced set of points selected from the original DEM. The TIN generation process is iterative; at each iteration, areas in the DEM that lie outside of a user-supplied error tolerance in the TIN are identified, and points are chosen from the DEM to more accurately model these areas. Point selection involves the computation of the difference between the actual DEM and an approximate DEM. This approximate DEM is calculated by interpolating elevation points from the TIN.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"26 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":"131728592","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.1992.223277
T. Fontaine, L. Shastri
A connectionist model for recognizing unconstrained handprinted digits is described. Instead of treating the input as a static signal, the image is canned over time and converted into a time-varying signal. The temporalized image is processed by a spatiotemporal connectionist network. The resulting system offers shift-invariance along the temporalized axis, a reduction in the number of free parameters, and the ability to process images of arbitrary length. For a set of real-world ZIP code digit images, the system achieved a 99.1% recognition rate on the training set and a 96.0% recognition rate on the test with no rejections. A 99.0% recognition rate on the test set was achieved when 14.6% of the images were rejected.<>
{"title":"Handprinted digit recognition using spatiotemporal connectionist models","authors":"T. Fontaine, L. Shastri","doi":"10.1109/CVPR.1992.223277","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223277","url":null,"abstract":"A connectionist model for recognizing unconstrained handprinted digits is described. Instead of treating the input as a static signal, the image is canned over time and converted into a time-varying signal. The temporalized image is processed by a spatiotemporal connectionist network. The resulting system offers shift-invariance along the temporalized axis, a reduction in the number of free parameters, and the ability to process images of arbitrary length. For a set of real-world ZIP code digit images, the system achieved a 99.1% recognition rate on the training set and a 96.0% recognition rate on the test with no rejections. A 99.0% recognition rate on the test set was achieved when 14.6% of the images were rejected.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"35 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":"131058438","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.1992.223157
J. Mundy
The history of the image understanding environment (IUE) project, a five-year program to develop a common software environment for the development of algorithms and application systems, is reviewed. An overview of some of the data structures that are currently evolving as a specification for the IUE is provided. The ultimate goal of the project is to provide the basic data structures and algorithms that are required to carry state-of-the-art research in image understanding.<>
{"title":"The image understanding environment program","authors":"J. Mundy","doi":"10.1109/CVPR.1992.223157","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223157","url":null,"abstract":"The history of the image understanding environment (IUE) project, a five-year program to develop a common software environment for the development of algorithms and application systems, is reviewed. An overview of some of the data structures that are currently evolving as a specification for the IUE is provided. The ultimate goal of the project is to provide the basic data structures and algorithms that are required to carry state-of-the-art research in image understanding.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"47 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":"131250323","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.1992.223197
Yoshinobu Sato, J. Ohya, K. Ishii
A formulation of the part decomposition problem motived by the minimum-description-length (MDL) criteria is presented. Unlike previous MDL approaches which use analytic functions, a general geometric constraint, convexity, is used as a part constraint. Therefore, the method is suitable for complex natural shapes such as human faces. The recovery process consists of a bottom-up grouping process and a subsequent optimization process based on the MDL criteria. The definite causal relations of part structure between different sensitivity levels are used to recover the hierarchy of part structure. Part decomposition experiments involving real 3-D range images are reported.<>
{"title":"Recovery of hierarchical part structure of 3-D shape from range image","authors":"Yoshinobu Sato, J. Ohya, K. Ishii","doi":"10.1109/CVPR.1992.223197","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223197","url":null,"abstract":"A formulation of the part decomposition problem motived by the minimum-description-length (MDL) criteria is presented. Unlike previous MDL approaches which use analytic functions, a general geometric constraint, convexity, is used as a part constraint. Therefore, the method is suitable for complex natural shapes such as human faces. The recovery process consists of a bottom-up grouping process and a subsequent optimization process based on the MDL criteria. The definite causal relations of part structure between different sensitivity levels are used to recover the hierarchy of part structure. Part decomposition experiments involving real 3-D range images are reported.<<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":"133575171","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.1992.223135
R. Haralick, Su S. Chen, T. Kanungo
The opening transformation on N-dimensional discrete space Z/sup N/ is discussed. The transform efficiently computes the binary opening (closing) with any size structuring element. It also provides a quick way to calculate the pattern spectrum of an image. The pattern spectrum is found to be nothing more than a histogram of the opening transform. An efficient two-pass recursive opening transform algorithm is developed and implemented. The correctness of the algorithm is proved, and some experimental results are given. The results show that the execution time of the algorithm is a linear function of n, where n is the product of the number of points in the structuring element. When the input binary image size is 256*256 and 50% of the image is covered by the binary-one pixels, it takes approximately 250 ms to do an arbitrary sized line opening and approximately 500 ms to do an arbitrary size box opening on the Sun/Sparc II workstation (with C compiler optimization flag on).<>
{"title":"Recursive opening transform","authors":"R. Haralick, Su S. Chen, T. Kanungo","doi":"10.1109/CVPR.1992.223135","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223135","url":null,"abstract":"The opening transformation on N-dimensional discrete space Z/sup N/ is discussed. The transform efficiently computes the binary opening (closing) with any size structuring element. It also provides a quick way to calculate the pattern spectrum of an image. The pattern spectrum is found to be nothing more than a histogram of the opening transform. An efficient two-pass recursive opening transform algorithm is developed and implemented. The correctness of the algorithm is proved, and some experimental results are given. The results show that the execution time of the algorithm is a linear function of n, where n is the product of the number of points in the structuring element. When the input binary image size is 256*256 and 50% of the image is covered by the binary-one pixels, it takes approximately 250 ms to do an arbitrary sized line opening and approximately 500 ms to do an arbitrary size box opening on the Sun/Sparc II workstation (with C compiler optimization flag on).<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"52 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":"125848578","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.1992.223196
H. Hel-Or, Shmuel Peleg, D. Avnir
The authors view symmetry as a continuous feature, and define a continuous symmetry measure (CSM) of shapes. The general definition of symmetry measure allows a comparison of the amount of symmetry of different shapes and the amount of different symmetries of a single shape. Furthermore, the CSM is associated with the symmetric shape that is closest to the given one, enabling visual evaluation of the CSM.<>
{"title":"A measure of symmetry based on shape similarity","authors":"H. Hel-Or, Shmuel Peleg, D. Avnir","doi":"10.1109/CVPR.1992.223196","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223196","url":null,"abstract":"The authors view symmetry as a continuous feature, and define a continuous symmetry measure (CSM) of shapes. The general definition of symmetry measure allows a comparison of the amount of symmetry of different shapes and the amount of different symmetries of a single shape. Furthermore, the CSM is associated with the symmetric shape that is closest to the given one, enabling visual evaluation of the CSM.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"73 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":"124705116","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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