Active shape models—their training and application
Computer Vision and Image Understanding
Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
The effect of texture representations on AAM performance
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Fusing Gabor and LBP feature sets for kernel-based face recognition
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
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
Automated Marsh-like classification of celiac disease in children using local texture operators
Computers in Biology and Medicine
MCBR-CDS'12 Proceedings of the Third MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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Active appearance model (AAM) has been widely used for modeling the shape and the texture of deformable objects and matching new ones effectively. The traditional AAM consists of two parts, shape model and texture model. In the texture model, for the sake of simplicity, the image intensity is usually employed to represent the texture information. However, the intensity is easy to be interfered by the external environment change, e.g. illumination variations, which results in an unsatisfied model fitting. To this purpose, we present a new texture representation in AAM, which combines Gabor wavelet and Local Binary Patterns (LBP) operator. On the one hand, Gabor wavelet can encode multi-scale and multi-direction information of an image. On the other hand, LBP is able to efficiently encode local information and compress the redundancy in the Gabor filtered images. Since the new texture representation can express an object more sophisticatedly, it will improve the accuracy of the model fitting. The Experimental results on various datasets demonstrate the effectiveness of the proposed texture representation, which results in a more accurate and reliable matching between the model and new images.