Multiple correspondences in stereo vision under a genetic algorithm approach

Silva, J.D.S. da; Simoni, P.O.; Bharadwaj, Kamal Kant

doi:10.1109/sibgra.2000.883894

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…A GAs-based approach has been introduced to point correspondence of a pair of stereo images [33]. This approach treats the problem as an unimodal combinatorial optimization one and therefore employs an appropriately adapted version of the standard GAs.…”

Section: Introductionmentioning

confidence: 99%

Multimodal genetic algorithms-based algorithm for automatic point correspondence

Delibasis

Asvestas

Matsopoulos

2010

Pattern Recognition

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Section: Introductionmentioning

confidence: 99%

Multimodal genetic algorithms-based algorithm for automatic point correspondence

Delibasis

Asvestas

Matsopoulos

2010

Pattern Recognition

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“…The integer (or mixed) nature of the optimisation problem posed from the correspondence procedure means that random search methods such as evolutionary algorithms [3] and simulated annealing [14] are a good choice. Another class of methods exploits some properties of the singular value decomposition (SVD) to satisfy both the exclusion and proximity principles resulting in a direct way to associate features of two arbitrary patterns [13].…”

Section: Introductionmentioning

confidence: 99%

An Iterative Algorithm for Finding Point Correspondences

Jia

Niblock

McMenemy

2008

2008 Congress on Image and Signal Processing

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This paper presents a solution to a general correspondence problem between a set of points (or image features) and a template, where the matching criterion includes linear parameters which reflect dynamic camera zooming during image-based tracking procedures. An algorithm is proposed which will solve this type of correspondence problem. Unlike most existing algorithms, which approach the solution using relaxation and mathematical programming, the proposed algorithm searches for the solution by iteratively interchanging the position of feature-pairs within the feature set until a necessary condition is satisfied. This being, if the interchange of two features from the feature set degrades the matching criterion.Three sets of correspondence examples are detailed which illustrate the effectiveness and efficiency of the proposed correspondence technique in solving cases, or image data, which are affected by large translation and scaling. A standard technique, the Scott and Longuet-Higgins method, taken from literature is also tested as a comparison.

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“…Feature-based algorithms always give a sparse disparity map for those feature points; therefore, interpolation is required to get a dense map for better reconstruction, while area-based algorithms give a dense disparity map instead. However, area-based algorithms are usually sensitive to illumination variations, occlusion, and window size and shape [7] .…”

Section: Introductionmentioning

confidence: 99%

<title>Solving correspondence problems with 1D signal matching</title>

2004

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Signal matching can be applied to many applications, such as shape matching, stereo vision, image registration, and so on. With the development of hardware, 1D signal matching can be implemented with hardware to make fast processing more feasible. This is especially important for many real-time 3D vision applications such as unmanned air vehicles and mobile robots. When lighting variance is not significant in a controlled lighting environment or when the baseline is short, images taken from two viewpoints are quite similar. It is also true for each scan line pair if the attention is drawn to 1D signal. By processing 1D signal line by line, a dense disparity map can be achieved and 3D scene can be reconstructed. In this paper, we present a robust 1D signal matching method, which combines spline representation and genetic algorithm to obtain a dense disparity map. By imposing smoothness constraint implicitly, matching parameters can be solved in terms of their spline representations by minimizing a certain cost function. Genetic algorithm can then be used to perform the optimization task. Reconstruction results of three different scene settings are shown to prove the validity of our algorithm. Due to the similarity of the problem in nature, this algorithm can be easily extended to solve image registration and motion detection problems.

show abstract

Multiple correspondences in stereo vision under a genetic algorithm approach

Cited by 3 publications

References 11 publications

Multimodal genetic algorithms-based algorithm for automatic point correspondence

Multimodal genetic algorithms-based algorithm for automatic point correspondence

An Iterative Algorithm for Finding Point Correspondences

<title>Solving correspondence problems with 1D signal matching</title>

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