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- Felsberg, Michael, 1974-, et al.
(författare)
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Online Learning of Correspondences between Images
- 2013
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : IEEE Computer Society. - 0162-8828 .- 1939-3539. ; 35:1, s. 118-129
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Tidskriftsartikel (refereegranskat)abstract
- We propose a novel method for iterative learning of point correspondences between image sequences. Points moving on surfaces in 3D space are projected into two images. Given a point in either view, the considered problem is to determine the corresponding location in the other view. The geometry and distortions of the projections are unknown as is the shape of the surface. Given several pairs of point-sets but no access to the 3D scene, correspondence mappings can be found by excessive global optimization or by the fundamental matrix if a perspective projective model is assumed. However, an iterative solution on sequences of point-set pairs with general imaging geometry is preferable. We derive such a method that optimizes the mapping based on Neyman's chi-square divergence between the densities representing the uncertainties of the estimated and the actual locations. The densities are represented as channel vectors computed with a basis function approach. The mapping between these vectors is updated with each new pair of images such that fast convergence and high accuracy are achieved. The resulting algorithm runs in real-time and is superior to state-of-the-art methods in terms of convergence and accuracy in a number of experiments.
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| 2. |
- Westelius, Carl-Johan, et al.
(författare)
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Phase-based Disparity Estimation
- 1995
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Ingår i: Vision as Process. - Berlin : Springer-Verlag. - 354058143X - 038758143X ; , s. 157-178
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- The problem of estimating depth information from two or more images of a scene is one which has received considerable attention over the years and a wide variety of methods have been proposed to solve it [Barnard and Fichsler, 1982; Fleck, 1991]. Methods based on correlation and methods using some form of feature matching between the images have found most widespread use. Of these, the latter have attracted increasing attention since the work of Marr [Marr, 1982], in which the features are zero-crossings on varying scales. These methods share an underlying basis of spatial domain operations.In recent years, however, increasing interest has been shown in computational models of vision based primarily on a localized frequency domain representation - the Gabor representation [Gabor, 1946; Adelson and Bergen, 1985], first suggested in the context of computer vision by Granlund [Granlund, 1978].
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| 3. |
- Westelius, Carl-Johan, et al.
(författare)
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Prototyping, Visualization and Simulation Using the Application Visualization System
- 1994
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Ingår i: Experimental Environments for Computer Vision and Image Processing. - Singapore : World Scientific Publishing Co. Pte. Ltd.. - 981021510X ; , s. 33-62
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- The Application Visualization System software from Advanced Visual Systems Inc is an interactive visualization environment for scientists, engineers and technical professionals. This report contains a short overview of the AVS software packages and a discussion about its general performance. The software package has actively been used at the Computer Vision Laboratory, Linköping University, during the last three years. The AVS package has been used in many applications. Examples are generating images from a virtual environment, simulation of a controllable robot with a stereo camera head and visualization of multidimensional data structures. Lately we also have used AVS for handling communication between different processes which may be distributed on different machines. AVS was primarily developed as a tool for visualization of complex data sets. However, another important aspect of the software is that it can be used as an advanced workbench for controlling networks of Unix processes (including external ones on different machine types) using simple visual programming.
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