Improved Census Transforms for Resource-Optimized Stereo Vision

Fife, Wade S.; Archibald, James K.

doi:10.1109/tcsvt.2012.2203197

Cited by 60 publications

(39 citation statements)

References 16 publications

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“…1(b), is the CT method, which compares the neighborhood pixels with each other in a symmetrical pattern on the basis of the central pixel. 9 GCT is more robust to noise than the other methods because it compares the brightness values of various neighborhood pixels in the direction of the arrow, not the central pixel.…”

Section: Census Transformmentioning

confidence: 99%

“…This result is consistent with the analysis result of the previous study. 9 In the three cases shown in Fig. 4, peak-ratio naive (PKRN) is employed to measure the method of obtaining the reliable disparity value of the minimum matching cost obtained from the Winner Takes All method.…”

Section: Star-census Transformmentioning

confidence: 99%

“…In other words, the probability of false matching greatly increases when the central pixel is affected by noise or other conditions. 8,9 Several suggestions have been proposed, such as the mini-census transform (MCT) 8 and generalized-census transform (GCT), 9 to improve the performance of the existing CT. CT compares all the pixels in the neighborhood based on the central pixels within the matching window; thus, if the size of the matching window increases, the computational complexity also increases. MCT compares only six pixels of the neighborhood, which are selected empirically, with a central pixel.…”

mentioning

confidence: 99%

“…Furthermore, GCT was proposed for robust matching performance toward Gaussian noise. 9 This method applies pixels separated by a certain distance within the matching window, not the central pixel. MCT has a drawback whereby it is affected by noise due to the characteristic of comparing the brightness values of neighborhood pixels based on a single central pixel.…”

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confidence: 99%

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Improved census transform for noise robust stereo matching

et al. 2016

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Abstract. Census transform (CT), a stereo matching algorithm, has a strong advantage in radial distortion and brightness changes. However, CT is noise-sensitive because it compares the brightness of a single central pixel based on the brightness values of neighborhood pixels within a matching window. Star-census transform, which compares the brightness of pixels separated by a certain distance along a symmetrical pattern within the matching window, is presented. The proposed method can select the distance between the pixels for comparison and comparison patterns. The experiment results show that the proposed method yields a better performance than the previous CT methods. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Census Transformmentioning

confidence: 99%

Section: Star-census Transformmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Improved census transform for noise robust stereo matching

et al. 2016

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“…Monocular vision can only recognize a single plane of an object. Since monocular vision can only determine the 2D information of an object that is commonly used for image recognition and image tracking [1]; [2] discussed how to take an image of an object using multilocular approach; [3] proposed a vision algorithm theory that can be applied to binocular appliances using the visual distortions of the two images to produce a 3D image with depth; [4] combine both binocular vision system and controller for image servo that can be installed onto motile robots for environmental detections. Many robotic research has mainly used infrared [5] radiation, ultrasound, and laser detectors for distance detection.…”

Section: Introductionmentioning

confidence: 99%

Application of Stereo Vision 3D Target Recognition Using Camera Calibration Algorithm

Huang¹,

Tseng²

2015

Advances in Computer Science Research

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Abstract-This paper proposes a method of 3D(threedimensional) object recognition using binocular parallax. The calibration object is a 2D(two-dimensional) object and a calibration label is placed on its plane. The internal, external, and distortion parameters of the camera are collaborated by taking pictures of the calibration object in different angles. This research allows image distortions caused by the camera to be calibrated, allowing the image coordinate of the characteristic point to better match with its actual coordinate. After calibration, the depth, localization, and identification of stereo vision of the image developed by the left and right cameras are optimized; the coordinate, distance, and the angle of rotation can be precisely identified.

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