A progressive framework for dense stereo matching

Jia, Bingxi; Liu, Shan; Du, Zhuoyang

doi:10.1134/s1054661816020036

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…where the intensity from the gray level of the histogram for the pixel in Y (x, y) represents by I(Y(x,y)) while the ⊗ designates a bitwise catenation, the is the relationship function and can be well-defined where q1 is the centre pixel while q2 is the closest neighbour pixel. Then, the Hamming distance is calculated using the Hamming function to determine the differences between both transform vectors that expressed as (5).…”

Section: Figure 2 the Framework Blocks Of The Developed Algorithmmentioning

confidence: 99%

“…Most of the algorithms established are based on the traditional framework of Schartein and Szeliski, comprised of four stages; 1) Matching cost, 2) Aggregation of cost, 3) Disparity optimisation, and 4) Final disparity refinement [4]. The framework used an essential cost function of matching to measure the stereo images' corresponding points from two or multiple perspectives [5].…”

Section: Introductionmentioning

confidence: 99%

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Local stereo matching algorithm using modified dynamic cost computation

Kadmin

Hamzah

Manap

et al. 2021

IJEECS

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Stereo matching is an essential subject in stereo vision architecture. Traditional framework composition consists of several constraints in stereo correspondences such as illumination variations in images and inadequate or non-uniform light due to uncontrollable environments. This work improves the local method stereo matching algorithm based on the dynamic cost computation method for depth measurement. This approach utilised modified dynamic cost computation in the matching cost. A modified census transform with dynamic histogram is used to provide the cost in the cost computation. The algorithm applied the fixed-window strategy with bilateral filtering to retain image depth information and edge in the cost aggregation stage. A winner takes all (WTA) optimisation and left-right check with adaptive bilateral median filtering are employed for disparity refinement. Based on the Middlebury benchmark dataset, the algorithm developed in this work has better accuracy and outperformed several other state-of-the-art algorithms.

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Section: Figure 2 the Framework Blocks Of The Developed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Local stereo matching algorithm using modified dynamic cost computation

Kadmin

Hamzah

Manap

et al. 2021

IJEECS

View full text Add to dashboard Cite

show abstract

“…Final disparity refinement [4]. The taxonomy used an essential cost function of matching to measure the stereo images' corresponding points from two or multiple perspectives [5]. This taxonomy aims to acquire a disparity map; thus, it can be translated into depth assessment for depth-based processing and communications.…”

Section: Introductionmentioning

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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