Local stereo matching algorithm using modified dynamic cost computation

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

International Journal of Emerging Technology and Advanced Engineering ◽

10.46338/ijetae0821_07 ◽

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

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

Mathematical Problems in Engineering ◽

10.1155/2021/5556990 ◽

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.

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A New Basic Correlation Measurement for Stereo Matching

10.31219/osf.io/jqux2 ◽

2019 ◽

Author(s):

Bowen Shi ◽

Shan Shi ◽

Junhua Wu ◽

Musheng Chen

Keyword(s):

Time Complexity ◽

Stereo Matching ◽

Computation Method ◽

Local Correlation ◽

Matching Algorithm ◽

Cost Aggregation ◽

Image Patches ◽

Disparity Maps ◽

Aggregation Step ◽

The Cost

In this paper, we propose a new stereo matching algorithm to measure the correlation between two rectified image patches. The difficulty near objects' boundaries and textureless areas is a widely discussed issue in local correlation-based algorithms and most approaches focus on the cost aggregation step to solve the problem. We analyze the inherent limitations of sum of absolute differences (SAD) and sum of squared differences (SSD), then propose a new difference computation method to restrain the noise near objects' boundaries and enlarge the intensity variations in textureless areas. The proposed algorithm can effectively deal with the problems and generate more accurate disparity maps than SAD and SSD without time complexity increasing. Furthermore, proved by experiments, the algorithm can also be applied in some SAD-based and SSD-based algorithms to achieve better results than the original.

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Effective Stereo Matching with Segment-based Cost Aggregation and Dual-path Refinement

10.21203/rs.3.rs-16259/v1 ◽

2020 ◽

Author(s):

Chih-Shuan Huang ◽

Ya-Han Huang ◽

Din-Yuen Chan ◽

Jar-Ferr Yang

Keyword(s):

Stereo Matching ◽

Texture Features ◽

Spatial Proximity ◽

Depth Maps ◽

Cost Aggregation ◽

Main Challenge ◽

Color Similarity ◽

Matching Cost ◽

Iterative Procedures ◽

Selection Of

Abstract Stereo matching is one of the most important topics in computer vision and aims at generating precise depth maps for various applications. The main challenge of stereo matching is to suppress inevitable errors occurring in smooth, occluded and discontinuous regions. To solve the aforementioned problems, in this paper, the proposed robust stereo matching system by using segment-based superpixels and magapixels to design adaptive stereo matching computation and dual-path refinement. After determination for edge and smooth regions and selection of matching cost, we suggest the segment–based adaptive support weights in cost aggregation instead of color similarity and spatial proximity only. The proposed dual-path depth refinements utilize the cross-based support region by referring texture features to correct the inaccurate disparities with iterative procedures to improve the depth maps for shape reserving. Specially for left-most and right most regions, the segment-based refinement can greatly improve the mismatched disparity holes. The experimental results demonstrate that the proposed system can obtain higher accurate depth maps compared with the conventional methods.

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A Parallel Stereo Matching Algorithm Core for FPGA Modeled by DSP Builder

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.536-537.67 ◽

2014 ◽

Vol 536-537 ◽

pp. 67-76

Author(s):

Xiang Zhang ◽

Zhang Wei Chen

Keyword(s):

Stereo Matching ◽

Cost Calculation ◽

Stereo Pair ◽

Disparity Map ◽

High Definition ◽

Matching Algorithm ◽

Census Transform ◽

Cost Aggregation ◽

Dsp Builder ◽

Fpga Chip

This paper proposes a FPGA implementation to apply a stereo matching algorithm based on a kind of sparse census transform in a FPGA chip which can provide a high-definition dense disparity map in real-time. The parallel stereo matching algorithm core involves census transform, cost calculation and cost aggregation modules. The circuits of the algorithm core are modeled by the Matlab/Simulink-based tool box: DSP Builder. The system can process many different sizes of stereo pair images through a configuration interface. The maximum horizon resolution of stereo images is 2048.

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

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v25.i1.pp223-231 ◽

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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Improved Cost Computation and Adaptive Shape Guided Filter for Local Stereo Matching of Low Texture Stereo Images

Applied Sciences ◽

10.3390/app10051869 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1869

Author(s):

Hua Liu ◽

Rui Wang ◽

Yuanping Xia ◽

Xiaoming Zhang

Keyword(s):

Cost Function ◽

Stereo Matching ◽

Guided Filter ◽

Disparity Map ◽

Cost Aggregation ◽

Dense Stereo Matching ◽

Gradient Based ◽

Matching Cost ◽

Cost Measurement ◽

Image Pairs

Dense stereo matching has been widely used in photogrammetry and computer vision applications. Even though it has a long research history, dense stereo matching is still challenging for occluded, textureless and discontinuous regions. This paper proposed an efficient and effective matching cost measurement and an adaptive shape guided filter-based matching cost aggregation method to improve the stereo matching performance for large textureless regions. At first, an efficient matching cost function combining enhanced image gradient-based matching cost and improved census transform-based matching cost is introduced. This proposed matching cost function is robust against radiometric variations and textureless regions. Following this, an adaptive shape cross-based window is constructed for each pixel and a modified guided filter based on this adaptive shape window is implemented for cost aggregation. The final disparity map is obtained after disparity selection and multiple steps disparity refinement. Experiments were conducted on the Middlebury benchmark dataset to evaluate the effectiveness of the proposed cost measurement and cost aggregation strategy. The experimental results demonstrated that the average matching error rate on Middlebury standard image pairs is 9.40%. Compared with the traditional guided filter-based stereo matching method, the proposed method achieved a better matching result in textureless regions.

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Stereo Matching Algorithm Based on Hybrid Convolutional Neural Network and Directional Intensity Difference

International Journal of Emerging Technology and Advanced Engineering ◽

10.46338/ijetae0621_10 ◽

2021 ◽

Vol 11 (6) ◽

pp. 86-96

Author(s):

Mohd Saad Hamid ◽

◽

Nurulfajar Abd Manap ◽

Rostam Affendi Hamzah ◽

Ahmad Fauzan Kadmin

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Stereo Matching ◽

Good Accuracy ◽

Intensity Difference ◽

Disparity Map ◽

Matching Algorithm ◽

Matching Cost ◽

Autonomous Vehicle Navigation ◽

Repetitive Pattern

Fundamentally, a stereo matching algorithm produces a disparity map or depth map. This map contains valuable information for many applications, such as range estimation, autonomous vehicle navigation and 3D surface reconstruction. The stereo matching process faces various challenges to get an accurate result for example low texture area, repetitive pattern and discontinuity regions. The proposed algorithm must be robust and viable with all of these challenges and is capable to deliver good accuracy. Hence, this article proposes a new stereo matching algorithm based on a hybrid Convolutional Neural Network (CNN) combined with directional intensity differences at the matching cost stage. The proposed algorithm contains a deep learning-based method and a handcrafted method. Then, the bilateral filter is used to aggregate the matching cost volume while preserving the object edges. The Winner-Take-All (WTA) is utilized at the optimization stage which the WTA normalizes the disparity values. At the last stage, a series of refinement processes will be applied to enhance the final disparity map. A standard benchmarking evaluation system from the Middlebury Stereo dataset is used to measure the algorithm performance. This dataset provides images with the characteristics of low texture area, repetitive pattern and discontinuity regions. The average error produced for all pixel regions is 8.51%, while the nonoccluded region is 5.77%. Based on the experimental results, the proposed algorithm produces good accuracy and robustness against the stereo matching challenges. It is also competitive with other published methods and can be used as a complete algorithm

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DEPTH MAP ESTIMATION IN LIGHT FIELDS USING AN STEREO-LIKE TAXONOMY

Revista de Investigaciones Universidad del Quindío ◽

10.33975/riuq.vol28n1.37 ◽

2016 ◽

Vol 28 (1) ◽

pp. 92-100

Author(s):

Francisco C. Calderon ◽

Carlos A. Parra ◽

Cesar L. Niño

Keyword(s):

Light Field ◽

Spatial Information ◽

Depth Map ◽

Ground Truth ◽

Light Fields ◽

Single Camera ◽

Matching Cost ◽

Winner Takes All ◽

The One ◽

The Cost

The light field or LF is a function that describes the amount of light traveling in every direction (angular) through every point (spatial) in a scene, this LF can be captured in several ways, using arrays of cameras, or more recently using a single camera with an special lens, that allows the capture of angular and spatial information of light rays of a scene (LF). This recent camera implementation gives a different approach to find the dept of a scene using only a single camera. In order to estimate the depth, we describe a taxonomy, similar to the one used in stereo Depth-map algorithms. That consist in the creation of a cost tensor to represent the matching cost between different disparities, then, using a support weight window, aggregate the cost tensor, finally, using a winner-takes-all optimization algorithm, search for the best disparities. This paper explains in detail the several changes made to an stereo-like taxonomy, to be applied in a light field, and evaluate this algorithm using a recent database that for the first time, provides several ground-truth light fields, with a respective ground-truth depth map.

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