Efficient registration of stereo images by matching graph descriptions of edge segments

Ayache, Nicholas; Faverjon, B.

doi:10.1007/bf00123161

Cited by 164 publications

(79 citation statements)

References 20 publications

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“…Feature-based methods use sets of pixels with similar attributes, picking out feature points of high distinctiveness, either pixels belonging to edges [7] or the corresponding edges themselves [8]; leading to a sparse depth map used mainly for object segmentation and recognition, sometimes reconstructing the rest of the surface by interpolation [9,10]. Examples of feature-based approaches are SIFT (scale-invariant feature transform) or FAST (features from accelerated segment test).…”

Section: Revision Of Methodsmentioning

confidence: 99%

A Matlab-Based Testbed for Integration, Evaluation and Comparison of Heterogeneous Stereo Vision Matching Algorithms

2016

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Stereo matching is a heavily researched area with a prolific published literature and a broad spectrum of heterogeneous algorithms available in diverse programming languages. This paper presents a Matlab-based testbed that aims to centralize and standardize this variety of both current and prospective stereo matching approaches. The proposed testbed aims to facilitate the application of stereo-based methods to real situations. It allows for configuring and executing algorithms, as well as comparing results, in a fast, easy and friendly setting. Algorithms can be combined so that a series of processes can be chained and executed consecutively, using the output of a process as input for the next; some additional filtering and image processing techniques have been included within the testbed for this purpose. A use case is included to illustrate how these processes are sequenced and its effect on the results for real applications. The testbed has been conceived as a collaborative and incremental open-source project, where its code is accessible and modifiable, with the objective of receiving contributions and releasing future versions to include new algorithms and features. It is currently available online for the research community.

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Section: Revision Of Methodsmentioning

confidence: 99%

A Matlab-Based Testbed for Integration, Evaluation and Comparison of Heterogeneous Stereo Vision Matching Algorithms

2016

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“…Let (1) where S 0 is the criterion of similarity between two regions. In other words, we test every pair of incompatible mappings to see if they generate a merged region which matches to the common region better than each splitted region.…”

Section: The Incompatibilities Association Graphmentioning

confidence: 99%

Robust region-based stereo vision to build environment maps for robotics applications

López

Pla

1998

Tasks and Methods in Applied Artificial Intelligence

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Abstract. Stereoscopic vision is an appropiate tool for building maps of the environment of a robot. When matching regions of the images, segmentation errors should be avoided. In this paper an algorithm to deal with errors in region matching is proposed, and the results in the presence of noise are analyzed. The selection of an appropiate similarity criterion to create the initial nodes in the graph-based matching process is very important for reducing the time of computation considerably. The experimental results show that the method is robust in the presence of noise.

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“…2 Feature-based methods use sets of pixels with similar attributes, normally either pixels belonging to edges ŽKim and Aggarwal, 1987;Marr and Poggio, 1979; . Mousavi and Schalkoff, 1994;Pollard et al, 1981 Ž or the corresponding edges themselves Ayache and Faverjon, 1987;Cruz et al, 1995a,b;Hoff and Ahuja, . 1989;Medioni and Nevatia, 1985;Pajares, 1995 . As Ž .…”

Section: Introductionmentioning

confidence: 99%

Stereo matching based on the self-organizing feature-mapping algorithm

Pajares

Cruz

Almansa

1998

Pattern Recognition Letters

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This paper presents an approach to the local stereo matching problem using edge segments as features with several attributes. We have verified that the differences in attributes for the true matches cluster in a cloud around a center. The correspondence is established on the basis of the minimum squared Mahalanobis distance between the difference of the Ž . attributes for a current pair of features and the cluster center similarity constraint . We introduce a learning strategy based on the Self-Organizing feature-mapping method to get the best cluster center. A comparative analysis among methods without learning is illustrated. q

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Efficient registration of stereo images by matching graph descriptions of edge segments

Cited by 164 publications

References 20 publications

A Matlab-Based Testbed for Integration, Evaluation and Comparison of Heterogeneous Stereo Vision Matching Algorithms

A Matlab-Based Testbed for Integration, Evaluation and Comparison of Heterogeneous Stereo Vision Matching Algorithms

Robust region-based stereo vision to build environment maps for robotics applications

Stereo matching based on the self-organizing feature-mapping algorithm

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