Pub Date : 1992-06-15DOI: 10.1109/CVPR.1992.223142
D. McKeown, Y. Hsieh
A feature-based stereo matching system that is based on an algorithm for one-dimensional waveform matching is described. It is intended for use in automated cartography, to generate an accurate three-dimensional model of man-made structures and natural terrain. Each epipolar line in the stereo pair is represented as a one-dimensional intensity waveform. The waveform is described as a collection of features, such as peaks and valleys, and represented across a set of hierarchical levels, computed by approximation from the original waveform. These features are matched using an evaluation function that factors similarity of waveform shape, intensity, and symbolic feature description. Waveform matches at coarse resolution are used to constrain matches at finer levels. Intra/inter-scanline corrections are applied and the actual position of the stereo match is adjusted by using the gradient representation of the original waveform. Some representative results are presented for a complex urban scene.<>
{"title":"Hierarchical waveform matching: a new feature-based stereo technique","authors":"D. McKeown, Y. Hsieh","doi":"10.1109/CVPR.1992.223142","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223142","url":null,"abstract":"A feature-based stereo matching system that is based on an algorithm for one-dimensional waveform matching is described. It is intended for use in automated cartography, to generate an accurate three-dimensional model of man-made structures and natural terrain. Each epipolar line in the stereo pair is represented as a one-dimensional intensity waveform. The waveform is described as a collection of features, such as peaks and valleys, and represented across a set of hierarchical levels, computed by approximation from the original waveform. These features are matched using an evaluation function that factors similarity of waveform shape, intensity, and symbolic feature description. Waveform matches at coarse resolution are used to constrain matches at finer levels. Intra/inter-scanline corrections are applied and the actual position of the stereo match is adjusted by using the gradient representation of the original waveform. Some representative results are presented for a complex urban scene.<<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":"129228928","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.1992.223215
D. Wilkes, John K. Tsotsos
The concept of active object recognition is introduced, and a proposal for its solution is described. The camera is mounted on the end of a robot arm on a mobile base. The system exploits the mobility of the camera by using low-level image data to drive the camera to a standard viewpoint with respect to an unknown object. From such a viewpoint, the object recognition task is reduced to a two-dimensional pattern recognition problem. The system uses an efficient tree-based, probabilistic indexing scheme to find the model object that is likely to have generated the observed data, and for line tracking uses a modification of the token-based tracking scheme of J.L. Crowley et al. (1988). The system has been successfully tested on a set of origami objects. Given sufficiently accurate low-level data, recognition time is expected to grow only logarithmically with the number of objects stored.<>
{"title":"Active object recognition","authors":"D. Wilkes, John K. Tsotsos","doi":"10.1109/CVPR.1992.223215","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223215","url":null,"abstract":"The concept of active object recognition is introduced, and a proposal for its solution is described. The camera is mounted on the end of a robot arm on a mobile base. The system exploits the mobility of the camera by using low-level image data to drive the camera to a standard viewpoint with respect to an unknown object. From such a viewpoint, the object recognition task is reduced to a two-dimensional pattern recognition problem. The system uses an efficient tree-based, probabilistic indexing scheme to find the model object that is likely to have generated the observed data, and for line tracking uses a modification of the token-based tracking scheme of J.L. Crowley et al. (1988). The system has been successfully tested on a set of origami objects. Given sufficiently accurate low-level data, recognition time is expected to grow only logarithmically with the number of objects stored.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"49 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":"131070588","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.1992.223261
Maqbool Patel, F. Cohen
The problem of extracting the local shape information of a 3D textured surface from a single 2D image is addressed. The textured objects of interest are planar and developable surfaces that are viewed as originating by laying down a rubber planar sheet with a homogeneous parent texture on it onto the objects. The homogeneous planar parent texture is modeled by a stationary Gaussian Markov random field (GMRF). The probability density function of the projected planar parent texture is an explicit function of the parent GMRF parameters, the surface shape parameters, and the camera geometry. The surface shape parameter estimation is posed as a maximum-likelihood estimation problem. A stereo-windows concept is introduced to obtain a unique and consistent parent texture from the image data.<>
{"title":"Shape from texture using Markov random field models and stereo-windows","authors":"Maqbool Patel, F. Cohen","doi":"10.1109/CVPR.1992.223261","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223261","url":null,"abstract":"The problem of extracting the local shape information of a 3D textured surface from a single 2D image is addressed. The textured objects of interest are planar and developable surfaces that are viewed as originating by laying down a rubber planar sheet with a homogeneous parent texture on it onto the objects. The homogeneous planar parent texture is modeled by a stationary Gaussian Markov random field (GMRF). The probability density function of the projected planar parent texture is an explicit function of the parent GMRF parameters, the surface shape parameters, and the camera geometry. The surface shape parameter estimation is posed as a maximum-likelihood estimation problem. A stereo-windows concept is introduced to obtain a unique and consistent parent texture from the image data.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"166 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":"116403156","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.1992.223249
S. Liu-Yu, M. Thonnat
The notions of the apparent boundary and the strict apparent boundary of an object, which provide an automatic approach of boundary detection, are presented. It is shown that the apparent boundary and the strict apparent boundary have the same diameter and the same convex hull as the original object. It is also shown that the strict apparent boundary is weakly externally visible, and is a fixpoint of the two operators that find the apparent boundary and the strict apparent boundary.<>
{"title":"Determination of the apparent boundary of an object","authors":"S. Liu-Yu, M. Thonnat","doi":"10.1109/CVPR.1992.223249","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223249","url":null,"abstract":"The notions of the apparent boundary and the strict apparent boundary of an object, which provide an automatic approach of boundary detection, are presented. It is shown that the apparent boundary and the strict apparent boundary have the same diameter and the same convex hull as the original object. It is also shown that the strict apparent boundary is weakly externally visible, and is a fixpoint of the two operators that find the apparent boundary and the strict apparent boundary.<<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":"116307552","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.1992.223185
Liuqing Huang, Y. Aloimonos
Under the traditional paradigm of considering vision as a recovery problem, visual interception is just another application of the structure-from-motion module. However, the inherent difficulties of three-dimensional reconstruction have delayed any real-time applications. The authors offer a robust solution under the active qualitative vision paradigm. From the image intensity function, they obtain the locomotive intrinsics of the agent and the target. Based on this relative information, they present a control strategy that decides in real time whether the velocity of the agent should be increased or decreased at any time instant, thus guiding the agent to intercept the target. The problem of visual interception can thus be solved by simple computation without correspondence.<>
{"title":"The geometry of visual interception","authors":"Liuqing Huang, Y. Aloimonos","doi":"10.1109/CVPR.1992.223185","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223185","url":null,"abstract":"Under the traditional paradigm of considering vision as a recovery problem, visual interception is just another application of the structure-from-motion module. However, the inherent difficulties of three-dimensional reconstruction have delayed any real-time applications. The authors offer a robust solution under the active qualitative vision paradigm. From the image intensity function, they obtain the locomotive intrinsics of the agent and the target. Based on this relative information, they present a control strategy that decides in real time whether the velocity of the agent should be increased or decreased at any time instant, thus guiding the agent to intercept the target. The problem of visual interception can thus be solved by simple computation without correspondence.<<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":"124797175","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.1992.223164
M. Herbert
Progress in building and recognizing models of objects for an autonomous vehicle for on-road and cross-country navigation is reported. The object models are stored in a map and are used as landmarks for estimating vehicle position. The landmarks can be used as intermediate control points at which the vehicle must take some prescribed action in the case of a complex mission. Robust object tracking using sequences of range images and building and updating 3-D object representations is presented. Tracking uses object prediction from one image to the next to accurately compute object locations. Object representations are built by merging sets of points from individual images into a single set in an object-centered coordinate frame. The sparse set of points is then segmented into shapes yielding compact and general object representations. An algorithm for landmark identification in range images is introduced in the context of map-based navigation.<>
{"title":"3-D landmark recognition from range images","authors":"M. Herbert","doi":"10.1109/CVPR.1992.223164","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223164","url":null,"abstract":"Progress in building and recognizing models of objects for an autonomous vehicle for on-road and cross-country navigation is reported. The object models are stored in a map and are used as landmarks for estimating vehicle position. The landmarks can be used as intermediate control points at which the vehicle must take some prescribed action in the case of a complex mission. Robust object tracking using sequences of range images and building and updating 3-D object representations is presented. Tracking uses object prediction from one image to the next to accurately compute object locations. Object representations are built by merging sets of points from individual images into a single set in an object-centered coordinate frame. The sparse set of points is then segmented into shapes yielding compact and general object representations. An algorithm for landmark identification in range images is introduced in the context of map-based navigation.<<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":"116769247","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.1992.223191
E. Arbogast, R. Mohr
The observation of curved contours in image sequences is used in egomotion estimation and in surface reconstruction. An egomotion technique that can be applied when no point or straight line correspondences are available is presented. It generalizes egomotion to the case of arbitrarily shaped contours, which is especially valuable in the case of nonpolyhedral objects. The computation uses a very simple finite differences scheme and quickly provides a good estimation of the motion parameters. Experiments conducted on synthetic and real data show the validity of the approach.<>
{"title":"Curved contours and surface reconstruction","authors":"E. Arbogast, R. Mohr","doi":"10.1109/CVPR.1992.223191","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223191","url":null,"abstract":"The observation of curved contours in image sequences is used in egomotion estimation and in surface reconstruction. An egomotion technique that can be applied when no point or straight line correspondences are available is presented. It generalizes egomotion to the case of arbitrarily shaped contours, which is especially valuable in the case of nonpolyhedral objects. The computation uses a very simple finite differences scheme and quickly provides a good estimation of the motion parameters. Experiments conducted on synthetic and real data show the validity of the approach.<<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":"114925327","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.1992.223237
T. Moons, E. Pauwels, L. Gool, A. Oosterlinck
It is shown how object recognition and optical flow can be captured within a single framework. These examples have been selected because they illustrate two complementary problems which can be tackled using the same unified approach based on Lie theory. The object recognition work referred to is based on the extraction of shape invariants and has been reported elsewhere. The present study focuses on using the same framework for the calculation of the optical flow. Besides the introduction of some new methods, it is shown that several well-known schemes can be derived following the same principles.<>
{"title":"Towards a general framework for feature extraction","authors":"T. Moons, E. Pauwels, L. Gool, A. Oosterlinck","doi":"10.1109/CVPR.1992.223237","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223237","url":null,"abstract":"It is shown how object recognition and optical flow can be captured within a single framework. These examples have been selected because they illustrate two complementary problems which can be tackled using the same unified approach based on Lie theory. The object recognition work referred to is based on the extraction of shape invariants and has been reported elsewhere. The present study focuses on using the same framework for the calculation of the optical flow. Besides the introduction of some new methods, it is shown that several well-known schemes can be derived following the same principles.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"21 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":"116929908","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.1992.223245
Yong C. Kim, K. Price
In automated feature-based motion analysis of multiple frames, correspondence data are usually noisy and fragmented. A technique that gradually refines the initial noisy correspondence data and links fragments of a single trajectory using feedback from 3D motion estimation is presented. First, 3D motion parameters are estimated using the initial correspondence data. Then, each noisy trajectory is partitioned into subsets of points, each of which conforms to the estimated motion. The best set is used as the input to the next motion estimation. This process is repeated, and the gaps in the refined correspondence data are filled by guidance from the predicted motion. Test results for a standard real image sequence are presented.<>
{"title":"Refinement of noisy correspondence using feedback from 3D motion","authors":"Yong C. Kim, K. Price","doi":"10.1109/CVPR.1992.223245","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223245","url":null,"abstract":"In automated feature-based motion analysis of multiple frames, correspondence data are usually noisy and fragmented. A technique that gradually refines the initial noisy correspondence data and links fragments of a single trajectory using feedback from 3D motion estimation is presented. First, 3D motion parameters are estimated using the initial correspondence data. Then, each noisy trajectory is partitioned into subsets of points, each of which conforms to the estimated motion. The best set is used as the input to the next motion estimation. This process is repeated, and the gaps in the refined correspondence data are filled by guidance from the predicted motion. Test results for a standard real image sequence are presented.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"15 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":"130623245","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.1992.223153
D. Jacobs
It is shown that the set of 2-D images produced by a group of 3-D point features of a rigid model can be optimally represented with two lines in two high-dimensional spaces. This result is used to match images and model groups by table lookup. The table is efficiently built and accessed through analytic methods that account for the effect of sensing error. In real images, it reduces the set of potential matches by a factor of several thousand. This representation of a model's images is used to analyze two other approaches to recognition. It is determined when invariants exist in several domains, and it is shown that there is an infinite set of qualitatively similar nonaccidental properties.<>
{"title":"Space efficient 3-D model indexing","authors":"D. Jacobs","doi":"10.1109/CVPR.1992.223153","DOIUrl":"https://doi.org/10.1109/CVPR.1992.223153","url":null,"abstract":"It is shown that the set of 2-D images produced by a group of 3-D point features of a rigid model can be optimally represented with two lines in two high-dimensional spaces. This result is used to match images and model groups by table lookup. The table is efficiently built and accessed through analytic methods that account for the effect of sensing error. In real images, it reduces the set of potential matches by a factor of several thousand. This representation of a model's images is used to analyze two other approaches to recognition. It is determined when invariants exist in several domains, and it is shown that there is an infinite set of qualitatively similar nonaccidental properties.<<ETX>>","PeriodicalId":325476,"journal":{"name":"Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"89 11 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":"129753427","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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