Stereo matching based on absolute differences for multiple objects detection

Hamzah, Rostam Affendi; Wei, Melvin Gan Yeou; Anwar, N. S. N.

doi:10.12928/telkomnika.v17i1.9185

Cited by 8 publications

(9 citation statements)

References 23 publications

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“…𝜉 𝑞 1 , 𝑞 2 = {0, 𝑞 ≤ 𝑞 1, 𝑞 > 𝑞 (12) where q1 q2 are the center pixel and a closely neighboring pixel. The distance and difference between the census value of both transform vectors can be estimated applying the Hamming distance function as expressed in Eq.…”

Section: Methodsmentioning

confidence: 99%

“…The cost value of all candidates in the matching cost computation between the reference and the target image is aggregated in the cost aggregation stage. A square-fixedwindow aggregation employing the box filter method is implemented [12]. The square-fixed-window size applied is (2r + 1) x (2r + 1) for r = 2, which sets the windows size to 5 x 5 so that the cost aggregation value is computed as in Eq.…”

Section: Methodsmentioning

confidence: 99%

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New Stereo Vision Algorithm Composition Using Weighted Adaptive Histogram Equalization and Gamma Correction

et al. 2021

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This work presents the composition of a new algorithm for a stereo vision system to acquire accurate depth measurement from stereo correspondence. Stereo correspondence produced by matching is commonly affected by image noise such as illumination variation, blurry boundaries, and radiometric differences. The proposed algorithm introduces a pre-processing step based on the combination of Contrast Limited Adaptive Histogram Equalization (CLAHE) and Adaptive Gamma Correction Weighted Distribution (AGCWD) with a guided filter (GF). The cost value of the pre-processing step is determined in the matching cost step using the census transform (CT), which is followed by aggregation using the fixed-window and GF technique. A winner-takes-all (WTA) approach is employed to select the minimum disparity map value and final refinement using left-right consistency checking (LR) along with a weighted median filter (WMF) to remove outliers. The algorithm improved the accuracy 31.65% for all pixel errors and 23.35% for pixel errors in nonoccluded regions compared to several established algorithms on a Middlebury dataset.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

New Stereo Vision Algorithm Composition Using Weighted Adaptive Histogram Equalization and Gamma Correction

et al. 2021

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“…A combination of minimum spanning tree based on support region and joining object flow in cost aggregation is adopted by [24]- [27] rather than using fixed-sized windows. An enhanced MST called 3DMST-CM was proposed by [28] handling a cases based on ambiguity of image pixel to achieve a high level accuracy in disparity map.…”

Section: Figure 1: Traditional Stereo Matching Taxonomy By Scharstein and Szeliskimentioning

confidence: 99%

mproved Stereo Matching Algorithm based on Census Transform and Dynamic Histogram Cost Computation

Kadmin¹,

Hamzah²,

Manap³

et al. 2021

IJETAE

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Stereo matching is a significant subject in the stereo vision algorithm. Traditional taxonomy composition consists of several issues in the stereo correspondences process such as radiometric distortion, discontinuity, and low accuracy at the low texture regions. This new taxonomy improves the local method of stereo matching algorithm based on the dynamic cost computation for disparity map measurement. This method utilised modified dynamic cost computation in the matching cost stage. A modified Census Transform with dynamic histogram is used to provide the cost volume. An adaptive bilateral filtering is applied to retain the image depth and edge information in the cost aggregation stage. A Winner Takes All (WTA) optimisation is applied in the disparity selection and a left-right check with an adaptive bilateral median filtering are employed for final refinement. Based on the dataset of standard Middlebury, the taxonomy has better accuracy and outperformed several other state-ofthe-art algorithms. Keywords—Stereo matching, disparity map, dynamic cost, census transform, local method

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“…In placing more emphasis, [17], [18] a cross-scale design was suggested to enhance the cost averaging for effective stereo matching. Recursive edge-aware (REAF) [19] filters provided for precise and effective stereo matching.…”

Section: Introductionmentioning

confidence: 99%

Development of depth map from stereo images using sum of absolute differences and edge filters

Hamzah

Azali

Noh

et al. 2022

IJEECS

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This article proposes a framework for the depth map reconstruction using stereo images. Fundamentally, this map provides an important information which commonly used in essential applications such as autonomous vehicle navigation, drone’s navigation and 3D surface reconstruction. To develop an accurate depth map, the framework must be robust against the challenging regions of low texture, plain color and repetitive pattern on the input stereo image. The development of this map requires several stages which starts with matching cost calculation, cost aggregation, optimization and refinement stage. Hence, this work develops a framework with sum of absolute difference (SAD) and the combination of two edge preserving filters to increase the robustness against the challenging regions. The SAD convolves using block matching technique to increase the efficiency of matching process on the low texture and plain color regions. Moreover, two edge preserving filters will increase the accuracy on the repetitive pattern region. The results show that the proposed method is accurate and capable to work with the challenging regions. The results are provided by the Middlebury standard dataset. The framework is also efficiently and can be applied on the 3D surface reconstruction. Moreover, this work is greatly competitive with previously available methods.

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Stereo matching based on absolute differences for multiple objects detection

Cited by 8 publications

References 23 publications

New Stereo Vision Algorithm Composition Using Weighted Adaptive Histogram Equalization and Gamma Correction

New Stereo Vision Algorithm Composition Using Weighted Adaptive Histogram Equalization and Gamma Correction

mproved Stereo Matching Algorithm based on Census Transform and Dynamic Histogram Cost Computation

Development of depth map from stereo images using sum of absolute differences and edge filters

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