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

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.

Download Full-text

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

Download Full-text

Stereo Matching Algorithm Based on HSV Color Space and Improved Census Transform

Mathematical Problems in Engineering ◽

10.1155/2021/1857327 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Chen Lv ◽

Jiahan Li ◽

Qiqi Kou ◽

Huandong Zhuang ◽

Shoufeng Tang

Keyword(s):

Confidence Interval ◽

Stereo Matching ◽

Color Space ◽

Absolute Difference ◽

Cost Calculation ◽

Matching Accuracy ◽

Matching Algorithm ◽

Hsv Color Space ◽

Census Transform ◽

The Cost

Aiming at the problem that stereo matching accuracy is easily affected by noise and amplitude distortion, a stereo matching algorithm based on HSV color space and improved census transform is proposed. In the cost calculation stage, the color image is first converted from RGB space to HSV space; moreover, the hue channel is used as the matching primitive to establish the hue absolute difference (HAD) cost calculation function, which reduces the amount of calculation and enhances the robustness of matching. Then, to solve the problem of the traditional census transform overrelying on the central pixel and to improve the noise resistance of the algorithm, an improved census method based on neighborhood weighting is also proposed. Finally, the HAD cost and the improved census cost are nonlinearly fused as the initial cost. In the aggregation stage, an outlier elimination method based on confidence interval is proposed. By calculating the confidence interval of the aggregation window, this paper eliminates the cost value that is not in the confidence interval and subsequently filters as well as aggregates the remaining costs to further reduce the noise interference and improve the matching accuracy. Experiments show that the proposed method can not only effectively suppress the influence of noise, but also achieve a more robust matching effect in scenes with changing exposure and lighting conditions.

Download Full-text

A Joint 2D-3D Complementary Network for Stereo Matching

Sensors ◽

10.3390/s21041430 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1430

Author(s):

Xiaogang Jia ◽

Wei Chen ◽

Zhengfa Liang ◽

Xin Luo ◽

Mingfei Wu ◽

...

Keyword(s):

Stereo Matching ◽

Computational Cost ◽

Research Field ◽

Disparity Map ◽

Improve Performance ◽

Cost Aggregation ◽

Disparity Range ◽

Public Datasets ◽

Coarse To Fine ◽

Speed And Accuracy

Stereo matching is an important research field of computer vision. Due to the dimension of cost aggregation, current neural network-based stereo methods are difficult to trade-off speed and accuracy. To this end, we integrate fast 2D stereo methods with accurate 3D networks to improve performance and reduce running time. We leverage a 2D encoder-decoder network to generate a rough disparity map and construct a disparity range to guide the 3D aggregation network, which can significantly improve the accuracy and reduce the computational cost. We use a stacked hourglass structure to refine the disparity from coarse to fine. We evaluated our method on three public datasets. According to the KITTI official website results, Our network can generate an accurate result in 80 ms on a modern GPU. Compared to other 2D stereo networks (AANet, DeepPruner, FADNet, etc.), our network has a big improvement in accuracy. Meanwhile, it is significantly faster than other 3D stereo networks (5× than PSMNet, 7.5× than CSN and 22.5× than GANet, etc.), demonstrating the effectiveness of our method.

Download Full-text

A Modified Census Transform Based on the Neighborhood Information for Stereo Matching Algorithm

2013 Seventh International Conference on Image and Graphics ◽

10.1109/icig.2013.113 ◽

2013 ◽

Cited By ~ 16

Author(s):

Li Ma ◽

Jingjiao Li ◽

Ji Ma ◽

Hanyue Zhang

Keyword(s):

Stereo Matching ◽

Matching Algorithm ◽

Census Transform ◽

Neighborhood Information

Download Full-text

Real time area-based stereo matching algorithm for multimedia video devices

Opto-Electronics Review ◽

10.2478/s11772-013-0107-5 ◽

2013 ◽

Vol 21 (4) ◽

Cited By ~ 4

Author(s):

T. Hachaj ◽

M. Ogiela

Keyword(s):

Real Time ◽

Stereo Matching ◽

Global Memory ◽

Stereo Pair ◽

Processing Unit ◽

Process Data ◽

Matching Algorithm ◽

Knowing That ◽

Algorithm Configuration ◽

Graphical Processing

AbstractIn this paper we investigate stereovision algorithms that are suitable for multimedia video devices. The main novel contribution of this article is detailed analysis of modern graphical processing unit (GPU)-based dense local stereovision matching algorithm for real time multimedia applications. We considered two GPU-based implementations and one CPU implementation (as the baseline). The results (in terms of frame per second, fps) were measured twenty times per algorithm configuration and, then averaged (the standard deviation was below 5%). The disparity range was [0,20], [0,40], [0,60], [0,80], [0,100] and [0,120]. We also have used three different matching window sizes (3×3, 5×5 and 7×7) and three stereo pair image resolutions 320×240, 640×480 and 1024×768. We developed our algorithm under assumption that it should process data with the same speed as it arrives from captures’ devices. Because most popular of the shelf video cameras (multimedia video devices) capture data with the frequency of 30Hz, this frequency was threshold to consider implementation of our algorithm to be “real time”. We have proved that our GPU algorithm that uses only global memory can be used successfully in that kind of tasks. It is very important because that kind of implementation is more hardware-independent than algorithms that operate on shared memory. Knowing that we might avoid the algorithms failure while moving the multimedia application between machines operating different hardware. From our knowledge this type of research has not been yet reported.

Download Full-text

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.

Download Full-text

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.

Download Full-text