A Novel Shape Matching Method Based on Feature Points

2014 ◽  
Vol 11 (3) ◽  
pp. 681-689
Author(s):  
Yuhua Li
Keyword(s):  
Shape Matching ◽  
Feature Points ◽  
Matching Method

scholarly journals Handwritten tifinagh character recognition using simple geometric shapes and graphs

2019 ◽  
Vol 13 (2) ◽  
pp. 598
Author(s):  
Youssef Ouadid ◽  
Abderrahmane Elbalaoui ◽  
Mehdi Boutaounte ◽  
Mohamed Fakir ◽  
Brahim Minaoui
Keyword(s):  
Character Recognition ◽  
Graph Matching ◽  
Recognition Rate ◽  
Recognition System ◽  
Features Extraction ◽  
Experimental Results ◽  
Feature Points ◽  
Matching Method ◽  
Extraction Algorithm ◽  
Novel Algorithm

<p>In this paper, a graph based handwritten Tifinagh character recognition system is presented. In preprocessing Zhang Suen algorithm is enhanced. In features extraction, a novel key point extraction algorithm is presented. Images are then represented by adjacency matrices defining graphs where nodes represent feature points extracted by a novel algorithm. These graphs are classified using a graph matching method. Experimental results are obtained using two databases to test the effectiveness. The system shows good results in terms of recognition rate.</p>

Fracture Surface Matching Method for Terracotta Based on Feature Points

2018 ◽  
Vol 55 (4) ◽  
pp. 041005
Author(s):  
赵夫群 Zhao Fuqun ◽  
耿国华 Geng Guohua
Keyword(s):  
Fracture Surface ◽  
Surface Matching ◽  
Feature Points ◽  
Matching Method

scholarly journals Extraction and Research of Crop Feature Points Based on Computer Vision

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2553 ◽  
Author(s):  
Jingwen Cui ◽  
Jianping Zhang ◽  
Guiling Sun ◽  
Bowen Zheng
Keyword(s):  
Computer Vision ◽  
Point Cloud ◽  
High Accuracy ◽  
Depth Camera ◽  
Positioning Error ◽  
Feature Points ◽  
Matching Method ◽  
Depth Images ◽  
Computer Vision Technology ◽  
Rgb Images

Based on computer vision technology, this paper proposes a method for identifying and locating crops in order to successfully capture crops in the process of automatic crop picking. This method innovatively combines the YOLOv3 algorithm under the DarkNet framework with the point cloud image coordinate matching method, and can achieve the goal of this paper very well. Firstly, RGB (RGB is the color representing the three channels of red, green and blue) images and depth images are obtained by using the Kinect v2 depth camera. Secondly, the YOLOv3 algorithm is used to identify the various types of target crops in the RGB images, and the feature points of the target crops are determined. Finally, the 3D coordinates of the feature points are displayed on the point cloud images. Compared with other methods, this method of crop identification has high accuracy and small positioning error, which lays a good foundation for the subsequent harvesting of crops using mechanical arms. In summary, the method used in this paper can be considered effective.

Matching of Affine Transformed Images by Using Similarity Based on Local Concentric Features

1997 ◽  
Vol 9 (2) ◽  
pp. 126-131 ◽  
Author(s):  
Atsushi Sakai ◽  
◽  
Seiji Ujifuku ◽  
Yoshihiko Nomura ◽  
Keyword(s):  
Fourier Transform ◽  
Pattern Matching ◽  
Affine Transformation ◽  
Feature Points ◽  
Matching Method ◽  
Corner Points ◽  
Local Areas

This paper proposes a pattern matching method based on concentric features calculated over local areas close to feature points. For such feature points, the corner points of images are used, and images generated by complex-log mapping and Fourier transform are used as the concentric features. The procedures are as follows: (1) Similarity is found based on the concentric features in neighborhoods of corner points; (2) in consideration of the uniqueness of correspondence and removal of pseudo-correspondence, correspondence is obtained from this similarity; (3) with correspondence as a weight, the parameters of the affine transformation are estimated. By conducting experiments, the robustness of the proposed technique against deformations and noises is shown.

A Multi-Gradation Stereo Matching Method Based on Edge Feature Points

2013 ◽  
Vol 748 ◽  
pp. 624-628
Author(s):  
Zhu Lin Li
Keyword(s):  
Object Recognition ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Feature Points ◽  
Image Pair ◽  
Three Dimensional Reconstruction ◽  
Matching Method ◽  
Matching Algorithm ◽  
Dimensional Reconstruction ◽  
Edge Feature

A gradation stereo matching algorithm based on edge feature points was proposed. Its basic idea is: firstly edge feature points of image pair were extracted; then, gradient invariability and singular eigenvalue invariability were analyzed, two-grade stereo matching method was build, foundation matrix was solved further, and three-grade stereo matching algorithm was finished by foundation matrix guidance. The result indicates that the algorithm can improve matching precision, from 58.3% to 73.2%, it is simple and utility, and it is important for object recognition, tracking, and three-dimensional reconstruction.

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