Stereo matching based on census transformation of image gradients

Stentoumis, Christos; Γραμματικόπουλος, Λάζαρος; Kalisperakis, I.; Karras, George C.; Petsa, E.

doi:10.1117/12.2184763

Cited by 4 publications

(5 citation statements)

References 25 publications

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“…Various approaches have been applied, such as shiftable window and support window [17]- [19]. Still, the support window's rectangular constrained shape having the difficulty at the near the depth discontinuity regions to measure disparity values of the pixels [20].…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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show abstract

“…By using the combination of multiple single similarity measures into a composite similarity measure, it has been proven to be an effective method to calculate the matching cost [7][8][9][10]. The adaptive multi-cost approach proposed in this work defines a novel multi-cost function to calculate the raw matching score and employs an adaptive window aggregation strategy to filter the cost volume.…”

Section: Initial Disparity Map Estimationmentioning

confidence: 99%

“…In order to obtain accurate disparity results at near depth discontinuities, an appropriate support window should be constructed. An adaptive cross-based window that relies on a linearly expanded cross skeleton support region for cost aggregation is adopted [7,10,18,28]. The shape of the adaptive support window is visually presented in the fifth line of Figure 4.…”

Section: Cost Aggregationmentioning

confidence: 99%

Accurate Dense Stereo Matching Based on Image Segmentation Using an Adaptive Multi-Cost Approach

Men

et al. 2016

Symmetry

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This paper presents a segmentation-based stereo matching algorithm using an adaptive multi-cost approach, which is exploited for obtaining accuracy disparity maps. The main contribution is to integrate the appealing properties of multi-cost approach into the segmentation-based framework. Firstly, the reference image is segmented by using the mean-shift algorithm. Secondly, the initial disparity of each segment is estimated by an adaptive multi-cost method, which consists of a novel multi-cost function and an adaptive support window cost aggregation strategy. The multi-cost function increases the robustness of the initial raw matching costs calculation and the adaptive window reduces the matching ambiguity effectively. Thirdly, an iterative outlier suppression and disparity plane parameters fitting algorithm is designed to estimate the disparity plane parameters. Lastly, an energy function is formulated in segment domain, and the optimal plane label is approximated by belief propagation. The experimental results with the Middlebury stereo datasets, along with synthesized and real-world stereo images, demonstrate the effectiveness of the proposed approach.

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“…The inadequacy of the previous approaches has led to the development of transform‐based matching cost functions. Transform‐based matching cost functions include the soft rank transform (SRT) [4], rank (Rank) and census (Census) transforms [9], modified census (MC) [10], census of the image gradient (CG) [11], cross‐comparison census (CCC) [12], support local binary pattern (SLBP) [13], and centre symmetric census transform (CSCT) [14]. Since transform‐based matching cost functions rely on the relative order of the pixels, they are also invariant under all radiometric changes that preserve the relative order.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…However, this improvement is less helpful with large support windows. CG [11] increases the toleration to intensity transformations between stereo images by computing Census on the image gradient. CCC and SLBP enhance Census by computing the relationship between pixel pairs that are pixel neighbours of the anchor pixel.…”

Section: Introduction and Related Workmentioning

confidence: 99%