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

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.

Computer vision guided virtual craniofacial reconstruction

2007 ◽  
Vol 31 (6) ◽  
pp. 418-427 ◽  
Author(s):  
Suchendra M. Bhandarkar ◽  
Ananda S. Chowdhury ◽  
Yarong Tang ◽  
Jack C. Yu ◽  
Ernest W. Tollner
Keyword(s):  
Computer Vision ◽  
Craniofacial Reconstruction

Virtual craniofacial reconstruction using computer vision, graph theory and geometric constraints

2009 ◽  
Vol 30 (10) ◽  
pp. 931-938 ◽  
Author(s):  
Ananda S. Chowdhury ◽  
Suchendra M. Bhandarkar ◽  
Robert W. Robinson ◽  
Jack C. Yu
Keyword(s):  
Computer Vision ◽  
Graph Theory ◽  
Geometric Constraints ◽  
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
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