Local stereo matching algorithm with efficient matching cost and adaptive guided image filter

2016 ◽  
Vol 33 (9) ◽  
pp. 1087-1102 ◽  
Author(s):  
Shiping Zhu ◽  
Lina Yan
Keyword(s):  
Stereo Matching ◽  
Image Filter ◽  
Matching Algorithm ◽  
Guided Image Filter ◽  
Matching Cost ◽  
Efficient Matching

Accurate Image-Guided Stereo Matching With Efficient Matching Cost and Disparity Refinement

2016 ◽  
Vol 26 (9) ◽  
pp. 1632-1645 ◽  
Author(s):  
Yunlong Zhan ◽  
Yuzhang Gu ◽  
Kui Huang ◽  
Cheng Zhang ◽  
Keli Hu
Keyword(s):  
Stereo Matching ◽  
Image Guided ◽  
Matching Cost ◽  
Efficient Matching

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

2021 ◽  
Vol 11 (8) ◽  
pp. 48-57
Author(s):  
A. F. Kadmin ◽  
◽  
R. A. Hamzah ◽  
M. N. Abd Manap ◽  
M. S. Hamid ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Disparity Map ◽  
Local Method ◽  
Matching Algorithm ◽  
Census Transform ◽  
Traditional Taxonomy ◽  
Matching Cost ◽  
Winner Takes All ◽  
The Cost ◽  
Vision Algorithm

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

scholarly journals Local Stereo Matching Using Adaptive Cross-Region-Based Guided Image Filtering with Orthogonal Weights

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Lingyin Kong ◽  
Jiangping Zhu ◽  
Sancong Ying
Keyword(s):  
Stereo Matching ◽  
Computing Time ◽  
Image Filtering ◽  
Computation Method ◽  
Aggregation Method ◽  
Additional Time ◽  
Matching Algorithm ◽  
Cost Aggregation ◽  
Matching Cost ◽  
Guided Image Filtering

Adaptive cross-region-based guided image filtering (ACR-GIF) is a commonly used cost aggregation method. However, the weights of points in the adaptive cross-region (ACR) are generally not considered, which affects the accuracy of disparity results. In this study, we propose an improved cost aggregation method to address this issue. First, the orthogonal weight is proposed according to the structural feature of the ACR, and then the orthogonal weight of each point in the ACR is computed. Second, the matching cost volume is filtered using ACR-GIF with orthogonal weights (ACR-GIF-OW). In order to reduce the computing time of the proposed method, an efficient weighted aggregation computing method based on orthogonal weights is proposed. Additionally, by combining ACR-GIF-OW with our recently proposed matching cost computation method and disparity refinement method, a local stereo matching algorithm is proposed as well. The results of Middlebury evaluation platform show that, compared with ACR-GIF, the proposed cost aggregation method can significantly improve the disparity accuracy with less additional time overhead, and the performance of the proposed stereo matching algorithm outperforms other state-of-the-art local and nonlocal algorithms.

scholarly journals Local stereo matching algorithm using modified dynamic cost computation

2021 ◽  
Vol 22 (3) ◽  
pp. 1312
Author(s):  
A. F. Kadmin ◽  
R. A. Hamzah ◽  
M. N. Abd Manap ◽  
M. S. Hamid ◽  
T. F. Tg. Wook
Keyword(s):  
Stereo Matching ◽  
Depth Information ◽  
Computation Method ◽  
Local Method ◽  
Matching Algorithm ◽  
Depth Measurement ◽  
Cost Aggregation ◽  
Matching Cost ◽  
Winner Takes All ◽  
The Cost

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.

scholarly journals A new function of stereo matching algorithm based on hybrid convolutional neural network

2022 ◽  
Vol 25 (1) ◽  
pp. 223
Author(s):  
Mohd Saad Hamid ◽  
Nurulfajar Abd Manap ◽  
Rostam Affendi Hamzah ◽  
Ahmad Fauzan Kadmin ◽  
Shamsul Fakhar Abd Gani ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Bilateral Filter ◽  
Disparity Map ◽  
Matching Algorithm ◽  
Matching Cost ◽  
The Difference ◽  
Aggregation Step

This paper proposes a new hybrid method between the learning-based and handcrafted methods for a stereo matching algorithm. The main purpose of the stereo matching algorithm is to produce a disparity map. This map is essential for many applications, including three-dimensional (3D) reconstruction. The raw disparity map computed by a convolutional neural network (CNN) is still prone to errors in the low texture region. The algorithm is set to improve the matching cost computation stage with hybrid CNN-based combined with truncated directional intensity computation. The difference in truncated directional intensity value is employed to decrease radiometric errors. The proposed method’s raw matching cost went through the cost aggregation step using the bilateral filter (BF) to improve accuracy. The winner-take-all (WTA) optimization uses the aggregated cost volume to produce an initial disparity map. Finally, a series of refinement processes enhance the initial disparity map for a more accurate final disparity map. This paper verified the performance of the algorithm using the Middlebury online stereo benchmarking system. The proposed algorithm achieves the objective of generating a more accurate and smooth disparity map with different depths at low texture regions through better matching cost quality.

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