Window-Based Three-Dimensional Aggregation for Stereo Matching

Liu, Tong; Peng, Xiyuan; Qiao, Liyan

doi:10.1109/lsp.2016.2578944

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Liu et al 43 proposed a stereo framework and formulated a joint second-order smoothness prior, avoiding the blend of the foreground and background and retaining the disparity discontinuities of strong texture regions. Liu et al 44 presented a 3D aggregation strategy based on adaptive support window and good performance in slanted regions can be reached. Chuang et al 45 incorporated penalty tuning method and edge content in gradual SGM cost aggregation, balancing the smoothness and fine structures of disparity result.…”

Section: Related Workmentioning

confidence: 99%

State measurement of isolating switch using cost fusion and smoothness prior based stereo matching

Luo

et al. 2020

International Journal of Advanced Robotic Systems

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To better monitor the state of isolating switches, an efficient binocular vision-based state measurement system is proposed in this article. Two optimal cameras are selected as the vision of our inspection system. Firstly, stereo calibration and distortion rectification are performed on acquired image pair. Secondly, to recover the three-dimensional information of switch, we propose a semi-global stereo matching method by using data- and structure-driven cost volume fusion and then optimizing raw disparity map with weighted- and edge discriminated-smoothness prior. Gradient content is enforced on the weight for suppressing small-weight-accumulation problem in weak-textured regions. Besides, Hough transform with feature constraints is implemented for removing the chaotic lines and extracting center line of the switch arm. Finally, based on the center line and corresponding disparity map of the switch arm, triangulation principle is used for calculating the true angle between the switch arm and insulator such that whether or not the isolating switch is fully closed can be detected. The experimental results demonstrate that the proposed stereo matching method can achieve good performance in Middlebury v.3 data set and switch images, and the system can precisely measure the state of switches.

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Section: Related Workmentioning

confidence: 99%

State measurement of isolating switch using cost fusion and smoothness prior based stereo matching

Luo

et al. 2020

International Journal of Advanced Robotic Systems

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“…There exist research works demonstrating that stereo matching methods are inherently suitable to be converted into adaptive versions [18,21,22,39]. Adaptive window methods [21,22] try to find an optimal support window to determine the size of matching and improve the matching accuracy. An adaptive guided image filter [39] is proposed to feature an adaptive rectangular support window instead of the traditionally fixed window.…”

Section: Related Workmentioning

confidence: 99%

Quality Estimation and Optimization of Adaptive Stereo Matching Algorithms for Smart Vehicles

Chen

2020

ACM Trans. Embed. Comput. Syst.

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Stereo matching is a promising approach for smart vehicles to find the depth of nearby objects. Transforming a traditional stereo matching algorithm to its adaptive version has potential advantages to achieve the maximum quality (depth accuracy) in a best-effort manner. However, it is very challenging to support this adaptive feature, since (1) the internal mechanism of adaptive stereo matching (ASM) has to be accurately modeled, and (2) scheduling ASM tasks on multiprocessors to generate the maximum quality is difficult under strict real-time constraints of smart vehicles. In this article, we propose a framework for constructing an ASM application and optimizing its output quality on smart vehicles. First, we empirically convert stereo matching into ASM by exploiting its inherent characteristics of disparity–cycle correspondence and introduce an exponential quality model that accurately represents the quality–cycle relationship. Second, with the explicit quality model, we propose an efficient quadratic programming-based dynamic voltage/frequency scaling (DVFS) algorithm to decide the optimal operating strategy, which maximizes the output quality under timing, energy, and temperature constraints. Third, we propose two novel methods to efficiently estimate the parameters of the quality model, namely location similarity-based feature point thresholding and street scenario-confined CNN prediction. Results show that our DVFS algorithm achieves at least 1.61 times quality improvement compared to the state-of-the-art techniques, and average parameter estimation for the quality model achieves 96.35% accuracy on the straight road.

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Order-Based Disparity Refinement Including Occlusion Handling for Stereo Matching

Chen

et al. 2017

IEEE Signal Process. Lett.

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Window-Based Three-Dimensional Aggregation for Stereo Matching

Cited by 5 publications

References 14 publications

State measurement of isolating switch using cost fusion and smoothness prior based stereo matching

State measurement of isolating switch using cost fusion and smoothness prior based stereo matching

Quality Estimation and Optimization of Adaptive Stereo Matching Algorithms for Smart Vehicles

Order-Based Disparity Refinement Including Occlusion Handling for Stereo Matching

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