Stereo Matching in Mean Shift Attractor Space

2010 ◽  
pp. 465-473
Author(s):  
Michal Krumnikl
Keyword(s):  
Stereo Matching ◽  
Mean Shift

scholarly journals A Disparity Refinement Algorithm for Satellite Remote Sensing Images Based on Mean-Shift Plane Segmentation

2021 ◽  
Vol 13 (10) ◽  
pp. 1903
Author(s):  
Zhihui Li ◽  
Jiaxin Liu ◽  
Yang Yang ◽  
Jing Zhang
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Stereo Matching ◽  
Mean Shift ◽  
Segmentation Method ◽  
Remote Sensing Images ◽  
Edge Matching ◽  
Refinement Method ◽  
Initial Segmentation ◽  
Refinement Algorithm

Objects in satellite remote sensing image sequences often have large deformations, and the stereo matching of this kind of image is so difficult that the matching rate generally drops. A disparity refinement method is needed to correct and fill the disparity. A method for disparity refinement based on the results of plane segmentation is proposed in this paper. The plane segmentation algorithm includes two steps: Initial segmentation based on mean-shift and alpha-expansion-based energy minimization. According to the results of plane segmentation and fitting, the disparity is refined by filling missed matching regions and removing outliers. The experimental results showed that the proposed plane segmentation method could not only accurately fit the plane in the presence of noise but also approximate the surface by plane combination. After the proposed plane segmentation method was applied to the disparity refinement of remote sensing images, many missed matches were filled, and the elevation errors were reduced. This proved that the proposed algorithm was effective. For difficult evaluations resulting from significant variations in remote sensing images of different satellites, the edge matching rate and the edge matching map are proposed as new stereo matching evaluation and analysis tools. Experiment results showed that they were easy to use, intuitive, and effective.

A Disparity Refinement in Stereo Matching based on Mean-shift Segmentation and Spatiotemporal Domain

2020 ◽  
Vol 64 (2) ◽  
pp. 20505-1-20505-12
Author(s):  
Hui-Yu Huang ◽  
Zhe-Hao Liu
Keyword(s):  
Stereo Matching ◽  
Mean Shift ◽  
Disparity Map ◽  
Video Streams ◽  
Segmentation Strategy ◽  
Information Matching ◽  
Color Similarity ◽  
Spatial Domains ◽  
Disparity Maps ◽  
The Cost

Abstract A stereo matching algorithm is used to find the best match between a pair of images. To compute the cost of the matching points from the sequence of images, the disparity maps from video streams are estimated. However, the estimated disparity sequences may cause undesirable flickering errors. These errors result in low visibility of the synthesized video and reduce video coding. In order to solve this problem, in this article, the authors propose a spatiotemporal disparity refinement on local stereo matching based on the segmentation strategy. Based on segmentation information, matching point searching, and color similarity, adaptive disparity values to recover the disparity errors in disparity sequences can be obtained. The flickering errors are also effectively removed, and the boundaries of objects are well preserved. The procedures of the proposed approach consist of a segmentation process, matching point searching, and refinement in the temporal and spatial domains. Experimental results verify that the proposed approach can yield a high quantitative evaluation and a high-quality disparity map compared with other methods.

Stereo Matching Algorithm Based on Edge Feature of Segmented Image

2013 ◽  
Vol 333-335 ◽  
pp. 948-953
Author(s):  
Yi Zhang ◽  
Cheng Liang Huang ◽  
Lian Fa Bai
Keyword(s):  
Stereo Matching ◽  
Mean Shift ◽  
Weighting Factor ◽  
Reference Image ◽  
Matching Accuracy ◽  
Matching Algorithm ◽  
Mean Shift Algorithm ◽  
Fast Speed ◽  
Segmented Image ◽  
Edge Feature

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.

Ambiguous Region Matching and Boundary Pixel Optimization for Stereo Computation

2012 ◽  
Vol 546-547 ◽  
pp. 735-740
Author(s):  
Xing Nian Cui ◽  
Fan Yang ◽  
Qing Min Liao
Keyword(s):  
Stereo Matching ◽  
Belief Propagation ◽  
Mean Shift ◽  
Mixed Pixel ◽  
Region Matching ◽  
Mixed Pixels ◽  
Belief Propagation Algorithm ◽  
The Mean ◽  
Boundary Pixel ◽  
Coarse To Fine

In this paper, we present a stereo matching algorithm based on planar surface hypothesis. It improves the results of low texture regions and mixed pixels on object boundaries. First, regions are segmented by applying the mean-shift segmentation method. Then we propose a coarse-to-fine algorithm to increase the reliable correspondences in low texture regions. Third, the Belief Propagation algorithm is used to optimize disparity plane labeling. Finally, for a mixed pixel, we utilize the results of the depth plane and the local region of it to regulate its disparity. Experimental results using the Middlebury stereo test show that the performance of our method is high.

scholarly journals A Mean-Shift-Based Feature Descriptor for Wide Baseline Stereo Matching

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Yiwen Dou ◽  
Kuangrong Hao ◽  
Yongsheng Ding ◽  
Min Mao
Keyword(s):  
Cross Correlation ◽  
Stereo Matching ◽  
Mean Shift ◽  
Depth Image ◽  
Feature Descriptor ◽  
Feature Points ◽  
Accuracy Rate ◽  
Feature Description ◽  
Feature Scale ◽  
Stable Feature

We propose a novel Mean-Shift-based building approach in wide baseline. Initially, scale-invariance feature transform (SIFT) approach is used to extract relatively stable feature points. As to each matching SIFT feature point, it needs a reasonable neighborhood range so as to choose feature points set. Subsequently, in view of selecting repeatable and high robust feature points, Mean-Shift controls corresponding feature scale. At last, our approach is employed to depth image acquirement in wide baseline and Graph Cut algorithm optimizes disparity information. Compared with the existing methods such as SIFT, speeded up robust feature (SURF), and normalized cross-correlation (NCC), the presented approach has the advantages of higher robustness and accuracy rate. Experimental results on low resolution image and weak feature description in wide baseline confirm the validity of our approach.

scholarly journals A Survey of Learning Approaches and Application for 3D Vision

2018 ◽  
Vol 173 ◽  
pp. 03053
Author(s):  
Luanhao Lu
Keyword(s):  
Stereo Matching ◽  
Mean Shift ◽  
Three Dimensional ◽  
Image Synthesis ◽  
Learning Approaches ◽  
Median Filtering ◽  
Stereo Images ◽  
Disparity Map ◽  
3D Vision ◽  
2D Images

Three-dimensional (3D) vision extracted from the stereo images or reconstructed from the two-dimensional (2D) images is the most effective topic in computer vision and video surveillance. Three-dimensional scene is constructed through two stereo images which existing disparity map by Stereo vision. Many methods of Stereo matching which contains median filtering, mean-shift segmentation, guided filter and joint trilateral filters [1] are used in many algorithms to construct the precise disparity map. These methods committed to figure out the image synthesis range in different Stereo matching fields and among these techniques cannot perform perfectly every turn. The paper focuses on 3D vision, introduce the background and process of 3D vision, reviews several classical datasets in the field of 3D vision, based on which the learning approaches and several types of applications of 3D vision were evaluated and analyzed.

scholarly journals A Line-Based Adaptive-Weight Matching Algorithm Using Loopy Belief Propagation

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Hui Li ◽  
Xiao-Guang Zhang ◽  
Zheng Sun
Keyword(s):  
Stereo Matching ◽  
Belief Propagation ◽  
Mean Shift ◽  
Spatial Proximity ◽  
Consistency Checking ◽  
Disparity Map ◽  
Matching Effect ◽  
Matching Algorithm ◽  
Adaptive Weight ◽  
Loopy Belief Propagation

In traditional adaptive-weight stereo matching, the rectangular shaped support region requires excess memory consumption and time. We propose a novel line-based stereo matching algorithm for obtaining a more accurate disparity map with low computation complexity. This algorithm can be divided into two steps: disparity map initialization and disparity map refinement. In the initialization step, a new adaptive-weight model based on the linear support region is put forward for cost aggregation. In this model, the neural network is used to evaluate the spatial proximity, and the mean-shift segmentation method is used to improve the accuracy of color similarity; the Birchfield pixel dissimilarity function and the census transform are adopted to establish the dissimilarity measurement function. Then the initial disparity map is obtained by loopy belief propagation. In the refinement step, the disparity map is optimized by iterative left-right consistency checking method and segmentation voting method. The parameter values involved in this algorithm are determined with many simulation experiments to further improve the matching effect. Simulation results indicate that this new matching method performs well on standard stereo benchmarks and running time of our algorithm is remarkably lower than that of algorithm with rectangle-shaped support region.

scholarly journals A Window-Based Adaptive Correspondence Search Algorithm Using Mean Shift and Disparity Estimation

2011 ◽  
Vol 10 (3) ◽  
pp. 65-72
Author(s):  
Shujun Zhang ◽  
Jianbo Zhang ◽  
Yun Liu
Keyword(s):  
Stereo Matching ◽  
Search Algorithm ◽  
Mean Shift ◽  
Depth Map ◽  
Disparity Estimation ◽  
Matching Process ◽  
Window Method ◽  
Binocular Stereo ◽  
Correspondence Search ◽  
Better Than

Current methods to solve the problem of binocular stereo matching can be divided into two categories: sparse points based methods and dense points based methods. However, both of them have different shortcomings and limitations. There is no perfect method to solve the disparity problem. Dense points based techniques relatively obtain more accurate results but with higher computation. A large number of window-based adaptive corres-pondence techniques have emerged in recent years. In order to solve the problem of high time complexity and large amount of calculation in matching process, we propose a new window-based correspondence search algorithm using mean shift and disparity estimation. Mean shift can aggregate the same or similar colors so it can be applied to pre-process the source images to reduce their dynamic color range. Disparity estimation is conducted on the pre-processed two images to compute disparities of uniform texture regions. Adaptive window matching through similarity computation and window-based support aggregation is finally executed and exact depth map is obtained. Experimental results show that our algorithm is more efficient and keeps smooth dis-parity better than the prior window method

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