The stereo matching algorithm based on an improved adaptive support window

Stereo matching of the disparity discontinuous boundaries and weak texture regions is still a problem of computer vision. Local-based stereo matching algorithm with the advantages of fast speed and high accuracy is the most common method. In order to improve the matching accuracy of the mentioned regions,a stereo matching algorithm based on edge feature of segmented image is proposed. Firstly, the reference image was segmented by Mean-Shift algorithm. Then, support window was dynamically allocated based on edge feature of segmented image. Finally, the disparity distribution of support window was adjusted by introducing weighting factor. The experimental results show that this algorithm can reduce noise and effectively improve the matching accuracy.

Stereo matching using weighted dynamic programming on a single-direction four-connected tree

The stereo matching algorithm is a technique that analyses two or more images captured at diverse view points in order to find positions in real 3D space for the pixels of 2D image. The stereo matching methods have been used in various fields such as drawing the topographical map from aerial photograph and finding the depth information of objects in machine vision system. Nowdays, optical motion capture techniques using stereo matching algorithms are being developed for visual applications such as virtual reality or 3D graphics. Stereo matching algorithms can be generally classified into two methods: feature-based and area-based ones. The feature-based method matches the feature elements between two images, and uses the interpolation to obtain the disparity information for the pixels other than the feature elements, while the area-based method performs matching between pixels in two images by calculating the correlations of the pixels residing in the search window. Area-based method cannot match feature element with more accuracy than feature-based method even though it can make more dense disparity map. Moreover, it has more possibility of error in the area of insufficient texture information or depth discontinuities. In this paper we present a novel technique for area-based stereo matching algorithm which provides more accurate and error-prone matching capabilities by using adaptive search range and window size. We propose two new strategies (1) for determining search range adaptively from the disparity map and multiresolutional images of region segments obtained by applying feature-based algorithm, and (2) for changing the window size adaptively according to the edge information derived from the wavelet transform such that the combination of two adaptive methods in search range and window size greatly enhance accuracy while reducing errors. We test our matching algorithms for various types of images, and shall show the outperformance of our stereo matching algorithm.

Stacking learning with coalesced cost filtering for accurate stereo matching

In order to reduce the mismatch rate of binocular stereo matching algorithm, a stereo matching algorithm based on improved Census transform is proposed in this paper. Firstly, the classical Census transform is improved, the mean value rather than the center pixel is used as the reference value, and the three states based on the pixels' distances are used to describe the relative relationship between each pixel in the transform window and the reference value; Secondly, the sparse transform window is used to reduce the computational complexity of the algorithm, and the transformed Hamming distance is calculated as the matching cost; Finally, in the maximum disparity range, the points with the least matching cost are selected as the matching points by WTA algorithm, and the disparity is refined by sub-pixel fitting. Experimental results show that the improved Census transformed image has more detailed information in the depth discontinuous region. Compared with the algorithm that directly uses the gray feature as the matching base elements, the proposed algorithm has better operation speed and matching accuracy.

Area-based stereo matching algorithms are based on the support of a surrounding area to establish a point-to-point correspondence in two images. Two important problems arise in this context: How to obtain subpixel information and how to choose the optimal surrounding area. In this paper we present a non-iterative two-step algorithm for subpixel accurate stereo matching by using an adaptive window. In contrast to existing algorithms the window is not restricted to a rectangle but can be of any general shape. Starting from an initial sparse disparity estimate, the first step is to find the general shape of the window. This is performed by estimating the local disparity of each pixel in a box of maximum size using a bank of Gabor filters, and by applying a consistency constraint. In the second step the projective distortion is computed using the masked window. The performed experiments show the accurate and robust behavior of the proposed algorithm.

With the wide application of artificial intelligence in intelligent driving, visual SLAM technology has become one of the popular research areas. Binocular SLAM, as one of the cheapest cost technologies in SLAM, has become a research hotspot for many scholars. For the problems of high time consumption, poor accuracy and high mis-matching rate of traditional binocular stereo matching algorithm, an adaptive weight cost aggregation D* algorithm based on Dijkstra's idea is proposed. The algorithm improves on the local stereo matching Census algorithm, and fuses the Census algorithm and grayscale histogram in the cost matching stage to make it compatible with computational efficiency and accuracy, which can achieve real-time effect when combined with hardware acceleration. The experimental results show that the improved Census algorithm greatly improves the stereo matching accuracy, and computational efficiency. In comparison with other algorithms on the Middlebury 2.0 testbed, the improved algorithm is found to be 42% more efficient and 1.2% better in terms of mis-matching rate.

In this paper, we propose a coarse-fine matching algorithm based on parallax waveform analysis and conjoint measurement, which aims at performing the binocular stereo matching in natural scene. For the reason that human only notices the most saliency parts in image, we first extract the saliency areas of the two images, which suppresses the interference of background and reduces the computation cost. According to the saliency map, a global algorithm based on parallax waveform analysis is proposed to achieve the best coarse matching. On this basis, the fine matching is completed by analyzing the local features with the conjoint measurement of SAD and SSIM, which further improves the accuracy in binocular stereo matching. The performance of our algorithm is well demonstrated by experiments.

Traditional two-dimensional vision only has basic pixel information and no depth information, while three-dimensional vision has depth information, which has high application value in the era of intelligence. The mainstream methods for obtaining depth information of objects include binocular stereo matching and TOF (Time of flight) cameras. The depth obtained by binocular stereo matching is with high precision in high-texture objects but with low precision in low-texture and repeated-texture objects, while the precision of depth obtained by TOF is the exact opposite. By combining binocular stereo matching and TOF, high-precision depth maps can be obtained in various scenarios. This paper proposes a new method of combining binocular stereo matching and TOF technology. The credible depth information is obtained through TOF to guide the stereo matching of the binocular camera, and then the information obtained after the guided matching is fused with the TOF information. The final fusion result is a high-precision depth map. The experimental results show that the fused depth map has higher accuracy and the execution speed within a reasonable range.

Stereo matching is one of the most active research fields in computer vision. The paper introduces the categories and the performance index of stereo matching and introduces three high-speed and state-of-the-art stereo matching algorithms with different cost aggregation: fast bilateral stereo (FBS), binary stereo matching (BSM), and a non-local cost aggregation method (NLCA). By comparing the performance in terms of both quality and speed, we concluded that FSB deals with the effects of noise well; BSM is suitable for embedded devices and has a good performance with radiometric differences; NLCA combines the efficiency with the accuracy of state-of-the-art algorithms.

In order to obtain the depth information of the underwater target, it’s necessary to generate the disparity map based on binocular vision stereo matching. In the circulation water channel, the stereo matching experiments with underwater target were carried out by using the BM algorithm, SGBM algorithms and SIFT algorithm respectively. Then the characteristics of the disparity maps were analyzed for the three kinds of stereo matching algorithms. Compared with the BM algorithm and SGBM algorithms, the SIFT algorithm has been proved to be more suitable for underwater stereo matching. In order to obtain more feature points of underwater image, it is necessary to improved SIFT algorithm parameter. Underwater image matching experiments were made to determine the principal curvature coefficientγ. The results illustrated that the improvedγis better than the original value for underwater disparity map generation.

A fast stereo matching algorithm suitable for embedded real-time systems

Efficient local stereo matching algorithm based on fast gradient domain guided image filtering

Stereo matching is the process of finding disparity map between a pair of stereo images. Markov Random Field (MRF) model has been used in stereo matching algorithms in which the goal is to minimize total data, smoothness and occlusion cost. Recent MRF based stereo matching algorithms assign the same smoothness cost to adjacent pixels which is not effective at objects boundaries. In this paper, we propose a method to constrain the smoothness cost such that the cost applied to disparity discontinuity within an object is higher than that of the object's boundary. Data cost function is also normalized before computing its probability distribution function (PDF) to enforce an uniform PDF for the entire image. Experimental results have demonstrated that the proposed method is competitive to the state-of-the-art stereo matching algorithms.

The rapid advancement of intelligent systems, especially robotics and autonomous driving, is highly reliant on low-complexity and high-accuracy stereo matching algorithms. However, the performance of state-of-the-art stereo matching algorithms still has great space for improvement by gaining awareness of the implicit information hidden in the cost volume layers. In this paper, we propose a low-complexity local stereo matching algorithm named Cross-Layer Information Fusion (CLIF), to improve the matching accuracy by exploring the hidden information. First, we analyze and extract the hidden information into an auxiliary extractor using a novel fusion method. Second, we propose an information sharing strategy that transforms the extractor into a regularization term on each cost volume layer. Then we improve the design by re-constructing the information extractor between the adjacent cost volume layers and form a pipelined hardware architecture on the FPGA platform. Experimental results show that the proposed CLIF algorithm improves 6.53% average accuracy incurring negligible resources and performance impacts, compared to the state-of-the-art solutions.