A Survey On Graph Matching In Computer Vision

2020 ◽  
Author(s):  
Hui Sun ◽  
Wenju Zhou ◽  
Minrui Fei
Keyword(s):  
Computer Vision ◽  
Graph Matching

Graph Matching Techniques for Computer Vision

2013 ◽  
pp. 381-421 ◽  
Author(s):  
Mario Vento ◽  
Pasquale Foggia
Keyword(s):  
Computer Vision ◽  
Graph Matching ◽  
Reference Model ◽  
Computer Vision Applications ◽  
Matching Techniques

Many computer vision applications require a comparison between two objects, or between an object and a reference model. When the objects or the scenes are represented by graphs, this comparison can be performed using some form of graph matching. The aim of this chapter is to introduce the main graph matching techniques that have been used for computer vision, and to relate each application with the techniques that are most suited to it.

Graph Matching Techniques for Computer Vision

2012 ◽  
pp. 1-41 ◽  
Author(s):  
Mario Vento ◽  
Pasquale Foggia
Keyword(s):  
Computer Vision ◽  
Graph Matching ◽  
Reference Model ◽  
Computer Vision Applications ◽  
Matching Techniques

Many computer vision applications require a comparison between two objects, or between an object and a reference model. When the objects or the scenes are represented by graphs, this comparison can be performed using some form of graph matching. The aim of this chapter is to introduce the main graph matching techniques that have been used for computer vision, and to relate each application with the techniques that are most suited to it.

scholarly journals POOR TEXTURAL IMAGE MATCHING BASED ON GRAPH THEORY

2016 ◽  
Vol XLI-B3 ◽  
pp. 741-747 ◽  
Author(s):  
Shiyu Chen ◽  
Xiuxiao Yuan ◽  
Wei Yuan ◽  
Yang Cai
Keyword(s):  
Computer Vision ◽  
Image Matching ◽  
Urban Areas ◽  
Graph Matching ◽  
Recall Rate ◽  
Edge Weight ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Low Contrast ◽  
Series Of Experiments

Image matching lies at the heart of photogrammetry and computer vision. For poor textural images, the matching result is affected by low contrast, repetitive patterns, discontinuity or occlusion, few or homogeneous textures. Recently, graph matching became popular for its integration of geometric and radiometric information. Focused on poor textural image matching problem, it is proposed an edge-weight strategy to improve graph matching algorithm. A series of experiments have been conducted including 4 typical landscapes: Forest, desert, farmland, and urban areas. And it is experimentally found that our new algorithm achieves better performance. Compared to SIFT, doubled corresponding points were acquired, and the overall recall rate reached up to 68%, which verifies the feasibility and effectiveness of the algorithm.

Virtual multi-fracture craniofacial reconstruction using computer vision and graph matching

2009 ◽  
Vol 33 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Ananda S. Chowdhury ◽  
Suchendra M. Bhandarkar ◽  
Robert W. Robinson ◽  
Jack C. Yu
Keyword(s):  
Computer Vision ◽  
Graph Matching ◽  
Craniofacial Reconstruction

scholarly journals POOR TEXTURAL IMAGE MATCHING BASED ON GRAPH THEORY

2016 ◽  
Vol XLI-B3 ◽  
pp. 741-747
Author(s):  
Shiyu Chen ◽  
Xiuxiao Yuan ◽  
Wei Yuan ◽  
Yang Cai
Keyword(s):  
Computer Vision ◽  
Image Matching ◽  
Urban Areas ◽  
Graph Matching ◽  
Recall Rate ◽  
Edge Weight ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Low Contrast ◽  
Series Of Experiments

Image matching lies at the heart of photogrammetry and computer vision. For poor textural images, the matching result is affected by low contrast, repetitive patterns, discontinuity or occlusion, few or homogeneous textures. Recently, graph matching became popular for its integration of geometric and radiometric information. Focused on poor textural image matching problem, it is proposed an edge-weight strategy to improve graph matching algorithm. A series of experiments have been conducted including 4 typical landscapes: Forest, desert, farmland, and urban areas. And it is experimentally found that our new algorithm achieves better performance. Compared to SIFT, doubled corresponding points were acquired, and the overall recall rate reached up to 68%, which verifies the feasibility and effectiveness of the algorithm.

Human Vision and Computer Vision

1985 ◽  
Vol 30 (1) ◽  
pp. 47-47
Author(s):  
Herman Bouma
Keyword(s):  
Computer Vision ◽  
Human Vision

Computer vision for industrial applications

1983 ◽  
Vol 2 (5) ◽  
pp. 130
Author(s):  
J.A. Losty ◽  
P.R. Watkins
Keyword(s):  
Computer Vision ◽  
Industrial Applications

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

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