Secrets of adaptive support weight techniques for local stereo matching

Abstract-It is an urgent task for the coal industry in China and even the world to realize the position of the injured and the excavation equipment under the mine. Due to its advantages of high efficiency, low cost, and suitable accuracy, binocular stereo vision measurement methods have become a popular trend in recent years to achieve dynamic target positioning. The stereo matching problem is a key problem in the binocular stereo vision system. This paper analyzes and improves several classical matching algorithms, and combines the improved algorithms together for positioning research. Finally through simulation experiments, an area adaptive matching algorithm with advantages is obtained.

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“…The idea is to use weight and distance information to weight each pixel. The weight of the domain pixel [5] is defined as follows: [6] .…”

Section: A Adaptive Weight Methodsmentioning

confidence: 99%

Improvement of Several Classical Matching Algorithms for Underground Positioning

Huang¹,

Tong²,

Tang³

et al. 2018

Proceedings of the 2018 3rd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2018)

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“…Zhang et al [35] separately carried out horizontal and vertical passes for cost aggregation using orthogonal integral images. Hosni et al [9] took an approach to estimate a support region of pixel via color segmentation. This method calculates the geodesic distance from all pixels to the center pixel of the window in a square support window.…”

Section: : Refines Disparity B : Aggregates Cost a : Computes Matchmentioning

confidence: 99%

Visual stereo matching combined with intuitive transition of pixel values

Shin

Kim

Chung

2015

Multimed Tools Appl

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The objective of stereo matching is to find the corresponding pixels from similar two or more images. However, it is difficult problem to get precise and consistent disparity under a variety of real world situations. In other words, the color values of stereo images are easily influenced by radiometric factors such as illumination direction, illumination color, and camera exposure. Therefore, conventional stereo matching methods can have low performances under radiometric conditions. In this paper, we propose a novel stereo matching approach that is robust in controlling various radiometric variations such as local and global radiometric variations. We designed a hybrid stereo matching approach using transition of pixel values and data fitting. Transition of pixel values is utilized for the coarse stereo matching stage, and polynomial curve fitting is used for the fine stereo matching stage. Experimental results show that the proposed method has better performances compared to the stereo matching algorithms of comparison group under severely different radiometric conditions between stereo images. Consequentially, we demonstrate that the proposed method is less sensitive to various radiometric variations, and shows an outstanding performance in computational complexity.

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“…This paper, therefore, proposes an improved stereo matching algorithm [12] based on the existing ACT and analyzes its results. To achieve better matching accuracy and robustness to noise, the proposed algorithm adopts the truncated absolute difference (TAD) [8,9] and the multiple sparse windows (MSWs) method [13]. Section 2 of the paper introduces the existing ACT, Section 3 explains the proposed stereo matching algorithm, Section 4 evaluates the experimental results, and Section 5 concludes it.…”

Section: Introductionmentioning

confidence: 99%

“…To ameliorate the matching accuracy of local matching algorithms, researchers have recently used the adaptive support weight (ASW) approach [8][9][10]. Overcoming the image ambiguity problem, this approach entails the application of various weights to the raw matching costs (RMCs) of pixels in a support window [8,11].…”

Section: Introductionmentioning

confidence: 99%

An Improved Stereo Matching Algorithm with Robustness to Noise Based on Adaptive Support Weight

2017

J Inf Process Syst

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An active research area in computer vision, stereo matching is aimed at obtaining three-dimensional (3D) information from a stereo image pair captured by a stereo camera. To extract accurate 3D information, a number of studies have examined stereo matching algorithms that employ adaptive support weight. Among them, the adaptive census transform (ACT) algorithm has yielded a relatively strong matching capability. The drawbacks of the ACT, however, are that it produces low matching accuracy at the border of an object and is vulnerable to noise. To mitigate these drawbacks, this paper proposes and analyzes the features of an improved stereo matching algorithm that not only enhances matching accuracy but also is also robust to noise. The proposed algorithm, based on the ACT, adopts the truncated absolute difference and the multiple sparse windows method. The experimental results show that compared to the ACT, the proposed algorithm reduces the average error rate of depth maps on Middlebury dataset images by as much as 2% and that is has a strong robustness to noise.

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Secrets of adaptive support weight techniques for local stereo matching

Cited by 107 publications

References 36 publications

Improvement of Several Classical Matching Algorithms for Underground Positioning

Improvement of Several Classical Matching Algorithms for Underground Positioning

Visual stereo matching combined with intuitive transition of pixel values

An Improved Stereo Matching Algorithm with Robustness to Noise Based on Adaptive Support Weight

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