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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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Shape Matching and Object Recognition Using Shape Contexts
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Affine/ Photometric Invariants for Planar Intensity Patterns
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ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
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ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
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International Journal of Computer Vision
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International Journal of Computer Vision
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International Journal of Computer Vision
A Sparse Texture Representation Using Local Affine Regions
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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
Multi-scale phase-based local features
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Description of interest regions with center-symmetric local binary patterns
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Local binary patterns variants as texture descriptors for medical image analysis
Artificial Intelligence in Medicine
Feature detector and descriptor evaluation in human action recognition
Proceedings of the ACM International Conference on Image and Video Retrieval
Fast algorithms for the computation of Ranklets
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Texture classification by modeling joint distributions of local patterns with Gaussian mixtures
IEEE Transactions on Image Processing
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Two novel real-time local visual features for omnidirectional vision
Pattern Recognition
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ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
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Binary Histogrammed Intensity Patches for Efficient and Robust Matching
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Pattern Recognition Letters
Appearance-based smile intensity estimation by cascaded support vector machines
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Image and Vision Computing
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Pattern Recognition Letters
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Applied Soft Computing
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Pattern Recognition
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Image and Vision Computing
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Pattern Recognition
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Pattern Recognition
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
Local descriptors in application to the aging problem in face recognition
Pattern Recognition
Markov Chain Monte Carlo Modular Ensemble Tracking
Image and Vision Computing
WaveLBP based hierarchical features for image classification
Pattern Recognition Letters
Human nonverbal behavior multi-sourced ontological annotation
Proceedings of the International Workshop on Video and Image Ground Truth in Computer Vision Applications
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Pattern Recognition
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Neurocomputing
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ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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Computers in Industry
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Computer Vision and Image Understanding
Rapid multimodality registration based on MM-SURF
Neurocomputing
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The Visual Computer: International Journal of Computer Graphics
Uniform Local Derivative Patterns and Their Application in Face Recognition
Journal of Signal Processing Systems
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This paper presents a novel method for interest region description. We adopted the idea that the appearance of an interest region can be well characterized by the distribution of its local features. The most well-known descriptor built on this idea is the SIFT descriptor that uses gradient as the local feature. Thus far, existing texture features are not widely utilized in the context of region description. In this paper, we introduce a new texture feature called center-symmetric local binary pattern (CS-LBP) that is a modified version of the well-known local binary pattern (LBP) feature. To combine the strengths of the SIFT and LBP, we use the CS-LBP as the local feature in the SIFT algorithm. The resulting descriptor is called the CS-LBP descriptor. In the matching and object category classification experiments, our descriptor performs favorably compared to the SIFT. Furthermore, the CS-LBP descriptor is computationally simpler than the SIFT.