Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Multi-view face identification and pose estimation using B-spline interpolation
Information Sciences—Informatics and Computer Science: An International Journal
A SIFT Descriptor with Global Context
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric and photometric invariant distinctive regions detection
Information Sciences: an International Journal
Information Sciences: an International Journal
Elastic shape-texture matching for human face recognition
Pattern Recognition
Journal of Systems and Software
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CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 3 - Volume 03
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IITA '08 Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application - Volume 01
Hybrid intelligent vision-based car-like vehicle backing systems design
Expert Systems with Applications: An International Journal
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Image Processing
Content-based facial image retrieval using constrained independent component analysis
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Shape classification by manifold learning in multiple observation spaces
Information Sciences: an International Journal
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This paper describes a visual shape descriptor based on the sectors and shape context of contour lines to represent the image local features used for image matching. The proposed descriptor consists of two-component feature vectors. First, the local region is separated into sectors and their gradient magnitude and orientation values are extracted; a feature vector is then constructed from these values. Second, local shape features are obtained using the shape context of contour lines. Another feature vector is then constructed from these contour lines. The proposed approach calculates the local shape feature without needing to consider the edges. This can overcome the difficulty associated with textured images and images with ill-defined edges. The combination of two-component feature vectors makes the proposed descriptor more robust to image scale changes, illumination variations and noise. The proposed visual shape descriptor outperformed other descriptors in terms of the matching accuracy: 14.525% better than SIFT, 21% better than PCA-SIFT, 11.86% better than GLOH, and 25.66% better than the shape context.