Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface curvature and shape reconstruction from unknown multiple illumination and integrability
Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Effect of Illuminant Rotation on Texture Filters: Lissajous's Ellipses
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Comparing Images under Variable Illumination
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A comparison of wavelet transform features for texture image annotation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Capture and Synthesis of 3D Surface Texture
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Journal of Mathematical Imaging and Vision
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Self-Similarity Based Classification of 3D Surface Textures
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 2 - Volume 02
Illumination-Invariant Texture Classification Based on Self-Similarity and Gabor Wavelet
IITA '08 Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application - Volume 01
A self-training approach to cost sensitive uncertainty sampling
Machine Learning
Associating visual textures with human perceptions using genetic algorithms
Information Sciences: an International Journal
Capture and fusion of 3d surface texture
Multimedia Tools and Applications
Textures and covering based rough sets
Information Sciences: an International Journal
Information Sciences: an International Journal
Comparison of texture features based on Gabor filters
IEEE Transactions on Image Processing
Texture analysis and classification: A complex network-based approach
Information Sciences: an International Journal
Computerized facial diagnosis using both color and texture features
Information Sciences: an International Journal
Real-time background modeling based on a multi-level texture description
Information Sciences: an International Journal
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As the appearance of a 3D surface texture is strongly dependent on the illumination direction, 3D surface-texture classification methods need to employ multiple training images captured under a variety of illumination conditions for each class. Texture images under different illumination conditions and directions still present a challenge for texture-image retrieval and classification. This paper proposes an efficient method for illumination-insensitive texture discrimination based on illumination compensation and enhancement. Features extracted from an illumination-compensated or -enhanced texture are insensitive to illumination variation; this can improve the performance for texture classification. The proposed scheme learns the average illumination-effect matrix for image representation under changing illumination so as to compensate or enhance images and to eliminate the effect of different and uneven illuminations while retaining the intrinsic properties of the surfaces. The advantage of our method is that the assumption of a single-point light source is not required, so it circumvents and overcomes the limitations of the Lambertian model and is also suitable for outdoor settings. We use a wide range of textures in the PhoTex database in our experiments to evaluate the performance of the proposed method. Experimental results demonstrate the effectiveness of our proposed methods.