An iterative prediction and correction method for automatic stereocomparison

Mori, Kenichi; Kidode, Masatsugu; Asada, H. Harry

doi:10.1016/0146-664x(73)90016-6

Cited by 62 publications

(20 citation statements)

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“…Levine, O'Handley, and Yagi [71 use an adaptive correlation window, the size of which varies inversely with the variance of the region surrounding the point. Mori, Kidode, and Asada [12] use a Gaussianweighted correlation window to minimize the errors due to distortion of the extremities. They also vary the window size with ambiguity of the match and use a prediction/correction algorithm, modifying one image to fit the other according to a predicted matching and iteratively using the error of fit to improve the prediction.…”

Section: Introduction Differences In Images Of Real World Scenes Mmentioning

confidence: 99%

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Disparity Analysis of Images

Barnard

Thompson

1980

IEEE Trans. Pattern Anal. Mach. Intell.

703

174

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Abstract-An algorithm for matching images of real world scenes is presented. The matching is a specification of the geometrical disparity between the images and may be used to partially reconstruct the threedimensional structure of the scene. Sets of candidate matching points are selected independently in each image. These points are the locations of small, distinct features which are likely to be detectable in both images. An initial network of possible matches between the two sets of candidates is constructed. Each possible match specifies a possible disparity of a candidate point in a selected reference image. An initial estimate of the probability of each possible disparity is made, based on the similarity of subimages surrounding the points. These estimates are iteratively improved by a relaxation labeling technique making use of the local continuity property of disparity that is a consequence of the continuity of real world surfaces. The algorithm is effective for binocular parallax, motion parallax, and object motion. It quickly converges to good estimates of disparity, which reflect the spatial organization of the scene.

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Section: Introduction Differences In Images Of Real World Scenes Mmentioning

confidence: 99%

“…Studies of stereopsis use a fixed camera model to constrain the search to one dimension [5], [7], [81, [12]. Nevatia uses a series of progressive views to constrain disparity to small values [8].…”

Section: Introduction Differences In Images Of Real World Scenes Mmentioning

confidence: 99%

Disparity Analysis of Images

Barnard

Thompson

1980

IEEE Trans. Pattern Anal. Mach. Intell.

703

174

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show abstract

“…For example in traditional approaches with regularization and Markov Random Fields, continuation [9] and simulated annealing [28] have been used; and in more recent approaches max-flow [70] and graph-cut [45] have been employed to solve the global optimization problems. Local stereo matching methods generally fall into two broad categories [19]: area-based (e.g., [59], [38], [65]) and feature-based techniques (e.g., [64], [58]). Area-based algorithms are employed to solve the correspondence problem for every single pixel in the image.…”

Section: Stereo Correspondence Techniquesmentioning

confidence: 99%

“…Furthermore, choosing the right size for the regions is not easy, as in most cases smaller regions will lead to more mismatches but shorter run-time; while larger regions will produce more accurate results at the expense of higher computational time. Well known algorithms of this type are by Mori et al [59], Hannah [38] and Okutomi and Kanade [65].…”

Section: Stereo Correspondence Techniquesmentioning

confidence: 99%

Structure from Infrared Stereo Images

Hajebi

Zelek

2008

2008 Canadian Conference on Computer and Robot Vision

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“…Stereo matching by computing correlation or sum of squared differences (SSD) is a basic technique for obtaining a dense depth map from images [MSK89] [FP86][Woo83] [MKA73]. A central problem with this method lies in selecting an appropriate window size.…”

Section: Introductionmentioning

confidence: 99%

A stereo matching algorithm with an adaptive window: theory and experiment

Kanade

Okutomi

1994

IEEE Trans. Pattern Anal. Machine Intell.

905

382

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A central problem in stereo matching by computing correlation or sum of squared differences (SSD) lies in selecting an appropriate window size. If the window is too small and does not cover enough intensity variation, it gives a poor disparity estimate, because the signal (intensity variation) to noise ratio is low. If, on the other hand, the window is too large and covers a region in which the depth of scene points varies, then the disparity within the window is not constant. As a result, the position of maximum correlation or minimum SSD may not represent a correct estimate of disparity. For this reason, an appropriate window size must be selected locally. There has been, however, little research directed toward the adaptive selection of matching windows. The stereo algorithm we propose in this paper selects a window adaptively by evaluating the local variation of the intensity and the disparity. We employ a statistical model that represents uncertainty of disparity of points over the window: the uncertainty is assumed to increase with the distance of the point from the center point. This modeling enables us to assess how disparity variation within a window affects the estimation of disparity. As a result, we can compute the uncertainty of the disparity estimate which takes into account both intensity and disparity variances. So, the algorithm can search for a window that produces the estimate of disparity with the least uncertainty for each pixel of an image. The method controls not only the size but also the shape (rectangle) of the window. The algorithm has been tested on both synthetic and real images, and the quality of the disparity maps obtained demonstrates the effectiveness of the algorithm.

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An iterative prediction and correction method for automatic stereocomparison

Cited by 62 publications

References 0 publications

Disparity Analysis of Images

Disparity Analysis of Images

Structure from Infrared Stereo Images

A stereo matching algorithm with an adaptive window: theory and experiment

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