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A taxonomy and evaluation of dense two-frame stereo correspondence algorithms

A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,10.1109/SMBV.2001.988771,Daniel Scharstein,Richard Szeliski,R. Zabih

A taxonomy and evaluation of dense two-frame stereo correspondence algorithms   (Citations: 525)
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Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame stereo methods designed to assess the different components and design decisions made in individual stereo algorithms. Using this taxonomy, we compare existing stereo methods and present experiments evaluating the performance of many different variants. In order to establish a common software platform and a collection of data sets for easy evaluation, we have designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can be easily extended to include new algorithms. We have also produced several new multiframe stereo data sets with ground truth, and are making both the code and data sets available on the Web
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    • ...Markov Random Field Stereo MRF-based stereo optimization typically uses a data term to enforce photometric consistency between matched pixels, and a smoothness term to penalize the inconsistency of disparities between pixel neighbors [3, 11, 16]...
    • ...We use such combined images to evaluate the proposed algorithm on the Middlebury datasets [11, 12], where the interval range is known from the ground truth disparity range...
    • ...Figure 4. Interval maps recovered from the Middlebury dataset [11, 12]...

    Changchang Wuet al. Repetition-based Dense Single-View Reconstruction

    • ...An exhaustive overview on stereo matching can be found in [19], while a complete introductiontostereovisioncanbefoundin[20]...
    • ...Finally, in accordance with the last point, we tested the algorithm using the image pairs from the very well-known Middlebury test bed [19]...
    • ...Fig. 2. Test images from the Middlebury test bed [19]...
    • ...For this purpose, the wellknown Middlebury test bed1 is used [19]...

    Mikel Galaret al. Interval-Valued Fuzzy Sets Applied to Stereo Matching of Color Images

    • ...Figure 3. Qualitative results of our algorithm with data from [37, 5, 33, 36, 35]...

    Ahmad Humayunet al. Learning to Find Occlusion Regions

    • ...One of these problems is stereo matching [5], [6]...
    • ...In the experimental section we will show the ability of IVFSs to find correct correspondences with an appropriate management of the existing uncertainty by testing our algorithm using the image pairs from the very wellknown Middlebury test-bed [5]...
    • ...We use the very well-known Middlebury test bed (http://cat.middlebury.edu/stereo) [5] which is widely used to test stereo matching algorithms...
    • ...Fig. 1. Test images from the Middlebury test bed [5]...

    Mikel Galaret al. Representing images by means of interval-valued fuzzy sets. Applicatio...

    • ...Exhaustive surveys of stereo vision algorithms can be found in [3] and [12]...
    • ...However, this method is not a traditional global approach since the minimization of the energy function is computed, similarly to Dynamic Programming or Scanline Optimization approaches, in a 1D domain [12]...

    Carlo Dal Muttoet al. Scene Segmentation Assisted by Stereo Vision

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