Stereo matching with superpixels

Roszkowski, Mikołaj

doi:10.1117/12.2000139

Cited by 4 publications

(5 citation statements)

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“…Methodologies involving preprocessing of input images have also been followed where the input images were segmented [18] into superpixels. The proposed algorithm's idea is that the pixels belonging to each segment belong to that particular object of the segment.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance analysis of a fuzzy disparity selector for stereo matching of image segments under radiometric variations

Shetty¹,

George

Nayak

et al. 2020

Turk J Elec Eng & Comp Sci

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Stereo matching algorithms generate disparity maps, which contain the depth information of the environment, from two or more images of a scene taken from different viewpoints. The process of obtaining dense disparity maps is a problem which is still being actively researched. The presence of radiometric differences in the images only further complicates the stereo matching problem. In the present research work, the images are initially split into small patches of pixels, such that pixels in each patch have similar intensities. The authors attempt to study the effect of the parameters, namely, tuning parameter ' α ' and the number of segments, while the images are subjected to variations in exposure and illumination. The value ' α ' performs the function of a weight signifying the contribution of each data cost, when the two data costs are combined in a linear fashion. Lastly, the results of this methodology are compared with other methods that try to tackle the problem of stereo matching under radiometric variations.

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Section: Literature Reviewmentioning

confidence: 99%

Performance analysis of a fuzzy disparity selector for stereo matching of image segments under radiometric variations

Shetty¹,

George

Nayak

et al. 2020

Turk J Elec Eng & Comp Sci

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“…Methods with SPs for depth image generation have been proposed. For reducing computational complexity, a way to perform block matching only on the center of the SP rather than the whole image has been proposed [1]. The use of SPs in this method is only pixel sampling to estimate disparity.…”

Section: Related Workmentioning

confidence: 99%

“…For the center search, block matching is performed only at the center of the SP [1]. The disparity of the center pixel is used as the disparity of the entire super-pixel.…”

Section: ) Sp Segmentation;mentioning

confidence: 99%

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Fast Stereo Matching Fully Utilizing Super-Pixels

Miyama

2018

JCC

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In this paper, we propose a depth image generation method by stereo matching on super-pixel (SP) basis. In the proposed method, block matching is performed only at the center of the SP, and the obtained disparity is applied to all pixels of the SP. Next, in order to improve the disparity, a new SP-based cost filter is introduced. This filter multiplies the matching cost of the surrounding SP by a weight based on reliability and similarity and sums the weighted costs of neighbors. In addition, we propose two new error checking methods. One-way check uses only a unidirectional disparity estimation with a small amount of calculation to detect errors. Cross recovery uses cross checking and error recovery to repair lacks of objects that are problematic with SP-based matching. As a result of the experiment, the execution time of the proposed method using the one-way check was about 1/100 of the full search, and the accuracy was almost equivalent. The accuracy using cross recovery exceeded the full search, and the execution time was about 1/60. Speeding up while maintaining accuracy increases the application range of depth images.

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“…In recent years, superpixel segmentation is used as preprocessing link of stereo matching essentially. [11][12][13][14] Superpixel is the aggregate of pixels that is adjacent to each other and has similar characteristics (color, brightness and texture, etc) in an image. However, the depth map obtained in this way is not ideal for integral imaging.…”

Section: Introductionmentioning

confidence: 99%

An efficient depth map acquisition technology based on generalized superpixel for integral imaging

Piao

Deng

et al. 2021

Jpn. J. Appl. Phys.

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This paper proposes a simplified method to generate elemental images using the image pair of the scene for integral imaging stereo display. Firstly, the generalized superpixel matching algorithm is designed for integral imaging to obtain the depth map. We define the discrete boundary according to the characteristics of the low-texture region. A kind of consistency check method by calculating the inclination degree of the discrete boundary is proposed. The remaining texture regions are handled by assisted growth matching method. The precise depth map is obtained through the above two steps. Secondly, the depth map and color image are used to generate multiple elemental images according to the reverse mapping principle. Therefore the reconstruction quality depends on the accuracy of the depth map. The experiments verify that the depth map has a small error range and prominent details which can improve the accuracy of the reconstructed light field and is suitable for integral imaging.

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Stereo matching with superpixels

Cited by 4 publications

References 0 publications

Performance analysis of a fuzzy disparity selector for stereo matching of image segments under radiometric variations

Performance analysis of a fuzzy disparity selector for stereo matching of image segments under radiometric variations

Fast Stereo Matching Fully Utilizing Super-Pixels

An efficient depth map acquisition technology based on generalized superpixel for integral imaging

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