{"title":"An O(N) iterative solution to the Poisson equation in low-level vision problems","authors":"S. Lai, B. Vemuri","doi":"10.1109/CVPR.1994.323804","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323804","url":null,"abstract":"In this paper, we present a novel iterative numerical solution to the Poisson equation whose solution is needed in a variety of low-level vision problems. Our algorithm is an O(N) (N being the number of discretization points) iterative technique and does not make any assumptions on the shape of the input domain unlike the polyhedral domain assumption in the proof of convergence of multigrid techniques. We present two major results namely, a generalized version of the capacitance matrix theorem and a theorem on O(N) convergence of the alternating direction implicit method (ADI) used in our algorithm. Using this generalized theorem, we express the linear system corresponding to the discretized Poisson equation as a Lyapunov and a capacitance matrix equation. The former is solved using the ADI method while the solution to the later is obtained using a modified bi-conjugate gradient algorithm. We demonstrate the algorithm performance on synthesized data for the surface reconstruction and the SFS problems.<<ETX>>","PeriodicalId":160667,"journal":{"name":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127544365","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}
{"title":"Adaptive-complexity registration of images","authors":"J. R. Muller, P. Anandan, J. Bergen","doi":"10.1109/CVPR.1994.323932","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323932","url":null,"abstract":"We present a framework for image registration algorithms that finds a lowest-order model of the flow between two images. Low-order models are useful in image registration, because they leave scene structure intact. But in real images complexity varies, and cannot be determined ahead of time. Algorithms in our framework adapt model complexity to image data during a coarse-fine parameter estimation process. Complexity increases keep residual flow small enough that motion can be correctly estimated at each subsequent resolution level. We present one algorithm within this framework which increases complexity by replacing global estimates with estimates over successively smaller patches. We show results of applying this algorithm to the task of mosaicing panoramic aerial images with unknown lens distortion and unknown camera position.<<ETX>>","PeriodicalId":160667,"journal":{"name":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127953225","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}
{"title":"Efficient indexing techniques for model based sensing","authors":"A. Wallack, J. Canny","doi":"10.1109/cvpr.1994.323838","DOIUrl":"https://doi.org/10.1109/cvpr.1994.323838","url":null,"abstract":"Indexing is a model-based recognition technique, in which unknown objects are identified using lookup tables. Indexing coordinates are extracted from sensed features, and the indexing coordinates specify a table entry containing the object's identity. Usually, only a small fraction of the possible indexing coordinates correspond to modeled objects, and hash tables are often used to save space. In this paper, we present a new indexing data structure called a tree grid which has two advantages over hash tables. (i) The tree grid preserves spatial ordering, so that nearby indexing entries can be retrieved efficiently (ii) The tree grid compacts the storage size of the table by a factor of as much as two orders of magnitude. k coordinates index an ordering of the interpretations, and 1 coordinate determines the consistent interpretations for objects with k degrees of freedom. We also show that for almost all model sets, 2k+1 indexing coordinates care sufficient to discriminate between two generic models, implying that 2k+1 indexing coordinates specify a unique in interpretation. We have implemented an indexing algorithm for recognizing 3D objects from pairs of image rays using the tree grid technique, and the results are reported.<<ETX>>","PeriodicalId":160667,"journal":{"name":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129449382","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}
{"title":"Complementary data fusion for limited-angle tomography","authors":"J. Boyd, J. Little","doi":"10.1109/CVPR.1994.323842","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323842","url":null,"abstract":"Ambiguity in the solution of inverse problems arises when data are insufficient to define a unique solution (i.e., the problem is ill-posed). Data fusion has the potential to reduce this ambiguity by using other sensory data that complement the original data. This paper examines the application of data fusion to limited-angle computed tomography (CT) to resolve ambiguity. While CT in its conventional form is ill-posed with a small null space, limited-angle CT has a much larger null space. Structures that lie primarily in the null space of the limited-angle Radon transform are particularly prone to ambiguity. We describe a novel constraint-based data fusion system that fuses spatial support and ultrasound measurements with x-ray data. The ensuing problem is less ambiguous, has a reduced null space, and permits accurate reconstruction of a sandwich structure where otherwise impossible.<<ETX>>","PeriodicalId":160667,"journal":{"name":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129474431","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}
{"title":"Accurate structure and motion computation in the presence of range image distortions due to sequential acquisition","authors":"Philip W. Smith, N. Nandhakumar","doi":"10.1109/CVPR.1994.323926","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323926","url":null,"abstract":"An innovative technique for rigid body motion estimation for use with sequential, time-of-flight laser radar scanners is presented. The method is an iterative, linear, feature-based approach which uses the non-zero image acquisition time constraint to accurately recover the motion parameters from the distorted structure of the 3-D range maps. The performance of the technique is experimentally verified using computer simulations.<<ETX>>","PeriodicalId":160667,"journal":{"name":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128912030","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}
{"title":"A Markov random field model for object matching under contextual constraints","authors":"S. Li","doi":"10.1109/CVPR.1994.323915","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323915","url":null,"abstract":"This paper presents a Markov random field (MRF) model for object recognition in high level vision. The labeling state of a scene in terms of a model object is considered as an MRF or couples MRFs. Within the Bayesian framework the optimal solution is defined as the maximum a posteriori (MAP) estimate of the MRF. The posterior distribution is derived based on sound mathematical principles from theories of MRF and probability, which is in contrast to heuristic formulations. An experimental result is presented.<<ETX>>","PeriodicalId":160667,"journal":{"name":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117264388","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}
{"title":"Simplex meshes: a general representation for 3D shape reconstruction","authors":"H. Delingette","doi":"10.1109/CVPR.1994.323913","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323913","url":null,"abstract":"Simplex meshes are simply connected meshes that are topologically dual of triangulations. In a previous work we have introduced the Simplex mesh representation for performing recognition of partially occluded smooth objects (ICCV'93, p.103-12). In this paper, we present a physically-based approach for recovering three-dimensional objects, based on the geometry of Simplex meshes. Elastic behavior is modelled by local stabilizing functionals, controlling the mean curvature through the Simplex angle extracted at each vertex. Those functionals are viewpoint-invariant, intrinsic and scale-sensitive. Unlike deformable surfaces defined on regular grids, Simplex meshes are highly adaptive structures, and we have developed a refinement process for increasing the mesh resolution at highly curved or inaccurate parts. Furthermore, operations for connecting Simplex meshes are performed to recover complex models from parts with simpler shapes.<<ETX>>","PeriodicalId":160667,"journal":{"name":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115621884","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}
{"title":"Active stereo vision and cyclotorsion","authors":"M. Jenkin, John K. Tsotsos","doi":"10.1109/CVPR.1994.323903","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323903","url":null,"abstract":"When a particular point is fixated by an active stereo system different portions of the world are brought into interocular alignment. This region is known as the horoptor. Through an examination of the horoptor under different viewing conditions it is demonstrated that for certain binocular tasks it is desirable to manipulate the horoptor by rotating (torquing) the cameras about their optical axes. This manipulation can be passive for operations such as stereo based obstacle detection for mobile robots, or active for active binocular heads. Techniques for both situations are presented.<<ETX>>","PeriodicalId":160667,"journal":{"name":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122966752","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}
{"title":"Using illumination invariant descriptors for recognition","authors":"G. Healey, D. Slater","doi":"10.1109/CVPR.1994.323851","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323851","url":null,"abstract":"Color pixel distributions provide a useful cue for object recognition. Recently, for example, a technique called color indexing due to M. Swain and D. Ballard (1991) used color histograms for the efficient recognition of objects from a large database in the presence of changes in scene geometry and occlusion. The effectiveness of this and other approaches that match color distributions, however, depends on the approximate constancy of the scene illumination. In this paper, we develop color histogram descriptors that are invariant to changes in the intensity and spectral distribution of the illumination. We present a set of experiments that demonstrate the effectiveness of these descriptors for object recognition in the presence of changes in illuminant spectral power distribution.<<ETX>>","PeriodicalId":160667,"journal":{"name":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122478097","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}
{"title":"Recognition by functional parts [function-based object recognition]","authors":"E. Rivlin, Sven J. Dickinson, A. Rosenfeld","doi":"10.1109/CVPR.1994.323839","DOIUrl":"https://doi.org/10.1109/CVPR.1994.323839","url":null,"abstract":"We present an approach to function-based object recognition that reasons about the functionality of an object's initiative parts. We extend the popular \"recognition by parts\" shape recognition framework to support \"recognition, by functional parts\", by combining a set of functional primitives and their relations with a set of abstract volumetric shape primitives and their relations. Previous approaches have relied on more global object features, often ignoring the problem of object segmentation, and thereby restricting themselves to range images of unoccluded scenes. We show how these shape primitives and relations can be easily recovered from superquadric ellipsoids which, in turn, can be recovered from either range or intensity images of occluded scenes. Furthermore, the proposed framework supports both unexpected (bottom-up) object recognition and expected (top-down) object recognition. We demonstrate the approach on, a simple domain by recognizing a restricted class of hand-tools from 2-D images.<<ETX>>","PeriodicalId":160667,"journal":{"name":"1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132326320","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}