Fast stereo matching using adaptive guided filtering

Yang, Qingqing; Ji, Pan; Li, Dongxiao; Yao, Shao-Jun; Zhang, Ming

doi:10.1016/j.imavis.2014.01.001

Cited by 82 publications

(60 citation statements)

References 13 publications

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“…where the α is added to balance the color and gradient terms as implemented by Yang et al (2014). The value of α controls the sensitivity of radiometric differences.…”

Section: Matching Cost Computationmentioning

confidence: 99%

“…From cost matching step, the raw disparity values are vast and too sensitive to noise. In this work, the guided filter is chosen since it is designed to reduce the noise and preserve the edges (Yang et al, 2014). The guided means that the filter is using the selected guided imaging, (i.e., left or right grayscale image as a guide for the filtering process).…”

Section: Cost Aggregationmentioning

confidence: 99%

“…These methods are also referred as window-based or area-based methods (Hamzah and Ibrahim, 2016). There are several approaches related to window-based such as fixed window (Yang et al, 2014), multiple window (Hirschmuller et al, 2002) and adaptive window (Lu et al, 2008). Such methods use only local information.…”

Section: Introductionmentioning

confidence: 99%

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Stereo Matching Algorithm Based on Illumination Control to Improve the Accuracy

2016

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This paper presents a new method of pixel based stereo matching algorithm using illumination control. The state of the art algorithm for absolute difference (AD) works fast, but only precise at low texture areas. Besides, it is sensitive to radiometric distortions (i.e., contrast or brightness) and discontinuity areas. To overcome the problem, this paper proposes an algorithm that utilizes an illumination control to enhance the image quality of absolute difference (AD) matching. Thus, pixel intensities at this step are more consistent, especially at the object boundaries. Then, the gradient difference value is added to empower the reduction of the radiometric errors. The gradient characteristics are known for its robustness with regard to the radiometric errors. The experimental results demonstrate that the proposed algorithm performs much better when using a standard benchmarking dataset from the Middlebury Stereo Vision dataset. The main contribution of this work is a reduction of discontinuity errors that leads to a significant enhancement on matching quality and accuracy of disparity maps.

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“…where the α is added to balance the color and gradient terms as implemented by Yang et al (2014). The value of α controls the sensitivity of radiometric differences.…”

Section: Matching Cost Computationmentioning

confidence: 99%

Section: Cost Aggregationmentioning

confidence: 99%

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Stereo Matching Algorithm Based on Illumination Control to Improve the Accuracy

2016

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“…The performance evaluations on different cost aggregation approaches [10,11] show that adaptive-weight [12] and segment-support [13] outperform the rest of cost aggregation approaches. More recent cost aggregation methods include successive weighted summation [8] and guided image filter [14,15].…”

Section: Review Of Previous Workmentioning

confidence: 99%

Enhanced disparity estimation in stereo images

Kordelas

Alexiadis

Daras

et al. 2015

Image and Vision Computing

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“…However, it is an expensive cost to compute the adaptive support region for each pixel. Adaptive kernel window [4] was adopted to improve the performance on depth discontinuities. The extra cost is to compute the adaptive support arms.…”

Section: Introductionmentioning

confidence: 99%

Stereo Matching Based on Efficient Image-Guided Cost Aggregation

Zhan

Zhang

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYCost aggregation is one of the most important steps in local stereo matching, while it is difficult to fulfill both accuracy and speed. In this letter, a novel cost aggregation, consisting of guidance image, fast aggregation function and simplified scan-line optimization, is developed. Experiments demonstrate that the proposed algorithm has competitive performance compared with the state-of-art aggregation methods on 32 Middlebury stereo datasets in both accuracy and speed.

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Fast stereo matching using adaptive guided filtering

Cited by 82 publications

References 13 publications

Stereo Matching Algorithm Based on Illumination Control to Improve the Accuracy

Stereo Matching Algorithm Based on Illumination Control to Improve the Accuracy

Enhanced disparity estimation in stereo images

Stereo Matching Based on Efficient Image-Guided Cost Aggregation

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