Shape Context Stereo Matching Method Based on Texture Features

2019 ◽  
Author(s):  
Pan Rui ◽  
Hua Chun-jian ◽  
Xiong Xue-mei
Keyword(s):  
Stereo Matching ◽  
Texture Features ◽  
Shape Context ◽  
Matching Method

Stereo Matching Method Based on Combination Characteristic Cost Computing and Unstable Tree Reconstruction Optimization and Its Application in Medical Images

2020 ◽  
Vol 10 (3) ◽  
pp. 646-653
Author(s):  
Shuchun Yu ◽  
Yupeng He ◽  
Zhifeng Chen ◽  
Changhai Ru ◽  
Ming Pang
Keyword(s):  
Spanning Tree ◽  
Stereo Matching ◽  
Minimum Spanning Tree ◽  
Texture Features ◽  
Cost Calculation ◽  
Depth Information ◽  
Matching Method ◽  
Stability Parameters ◽  
Reconstruction Process ◽  
The Stability

This study aimed to propose a stereo matching method based on the cost calculation of combination feature and reconstruction optimization of an unstable tree. For cost calculation, the improved Census transform was used to calculate the illumination characteristics, the color and gradient operators were used to calculate the color features, and the LBP (Local Binary Pattern) operator was used to calculate the texture features. Then, the initial matching cost was calculated by combining all three features. For cost aggregation, the minimum spanning tree algorithm was improved and the tree aggregation window was reset. For disparity optimization, the stability parameters were constructed to judge the stability of the tree aggregation window, and unstable trees were disassembled, reconstructed, incorporated into the surrounding stable trees. In the reconstruction process, the effects of error disparity ratio difference, brightness difference, and disparity difference were considered comprehensively. The experimental results showed that the performance of the proposed method was obviously better than that of the stereo matching method based on the minimum spanning tree, and was close to that of the two globally optimized stereo matching methods. Further, the method was applied to stereo matching of uterine images, and the depth information was rich in the disparity images, showing that this method could provide a basis for medical diagnosis.

Generation and Implementation of Grasping Rectangle Based on Hierarchical Shape Context and Kernel Density Estimation towards Robotic Grasping Unknown Objects

2013 ◽  
Vol 433-435 ◽  
pp. 537-544
Author(s):  
Guo Liang Kang ◽  
Shi Yin Qin
Keyword(s):  
Density Estimation ◽  
Random Forests ◽  
Kernel Density Estimation ◽  
Stereo Matching ◽  
Vision System ◽  
Kernel Density ◽  
Robotic Grasping ◽  
Shape Context ◽  
3D Space ◽  
Unknown Objects

This paper focuses on the perception step of robotic grasping unknown objects in order to get a stable grasping hypothesis. At first, hierarchical shape context feature is proposed to depict the local and global shape character of a sample point along the edges of the object. Moreover a kind of random forests classifier is adopted to recognize the grasping candidates in the image from vision system so that a 2D grasping rectangle can be generated through kernel density estimation. Finally, by means of stereo matching, the grasping rectangle can be mapped into the 3D space. Thus, the center of the grasping rectangle can be applied as the center of the gripper. The approaching vector and the grasping rectangle direction can be employed to determine the pose of the gripper. Simulated experiments showed that a reasonable and stable grasping rectangle can be generated for various unknown objects.

scholarly journals An Efficient Dense Stereo Matching Method for Planetary Rover

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 48551-48564 ◽  
Author(s):  
Haichao Li ◽  
Liang Chen ◽  
Feng Li
Keyword(s):  
Stereo Matching ◽  
Matching Method ◽  
Planetary Rover ◽  
Dense Stereo Matching

scholarly journals Stereo Matching Algorithm Based on 2D Delaunay Triangulation

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Xue-he Zhang ◽  
Ge Li ◽  
Chang-le Li ◽  
He Zhang ◽  
Jie Zhao ◽  
...  
Keyword(s):  
Delaunay Triangulation ◽  
Stereo Matching ◽  
Robot Vision ◽  
Depth Estimation ◽  
Planning System ◽  
Disparity Map ◽  
Matching Method ◽  
Running Speed ◽  
Edge Operator ◽  
Edge Points

To fulfill the applications on robot vision, the commonly used stereo matching method for depth estimation is supposed to be efficient in terms of running speed and disparity accuracy. Based on this requirement, Delaunay-based stereo matching method is proposed to achieve the aforementioned standards in this paper. First, a Canny edge operator is used to detect the edge points of an image as supporting points. Those points are then processed using a Delaunay triangulation algorithm to divide the whole image into a series of linked triangular facets. A proposed module composed of these facets performs a rude estimation of image disparity. According to the triangular property of shared vertices, the estimated disparity is then refined to generate the disparity map. The method is tested on Middlebury stereo pairs. The running time of the proposed method is about 1 s and the matching accuracy is 93%. Experimental results show that the proposed method improves both running speed and disparity accuracy, which forms a steady foundation and good application prospect for a robot’s path planning system with stereo camera devices.

Simulation of Fast Retrieval Method for Large-Scale Image Database

2014 ◽  
Vol 556-562 ◽  
pp. 4959-4962
Author(s):  
Sai Qiao
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Great Increase ◽  
Texture Features ◽  
Image Database ◽  
Image Databases ◽  
Matching Method ◽  
Retrieval Method ◽  
Database Retrieval ◽  
Database Constraints

The traditional database information retrieval method is achieved by retrieving simple corresponding association of the attributes, which has the necessary requirement that image only have a single characteristic, with increasing complexity of image, it is difficult to process further feature extraction for the image, resulting in great increase of time consumed by large-scale image database retrieval. A fast retrieval method for large-scale image databases is proposed. Texture features are extracted in the database to support retrieval in database. Constraints matching method is introduced, in large-scale image database, referring to the texture features of image in the database to complete the target retrieval. The experimental results show that the proposed algorithm applied in the large-scale image database retrieval, augments retrieval speed, thereby improves the performance of large-scale image database.

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