Markov random field modeling in computer vision
Markov random field modeling in computer vision
Face Recognition System Using Local Autocorrelations and Multiscale Integration
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Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
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ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
The BANCA database and evaluation protocol
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Learning object detection from a small number of examples: the importance of good features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A graphical model framework for coupling MRFs and deformable models
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A discriminative feature space for detecting and recognizing faces
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Image Processing
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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Currently, most of the feature extraction methods based on micro-patterns are application oriented. The micro-patterns are intuitively user-designed based on experience. Few works have built models of micro-patterns for feature extraction. In this paper, we propose a model-based feature extraction approach, which uses micro-structure modeling to design adaptive micro-patterns. We first model the micro-structure of the image by Markov random field. Then we give the generalized definition of micro-pattern based on the model. After that, we define the fitness function and compute the fitness index to encode the image’s local fitness to micro-patterns. Theoretical analysis and experimental results show that the new algorithm is both flexible and effective in extracting good features.