Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Intensity Transformations and Their Invariants in the Context of Color Pattern Recognition
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Illumination Invariant Recognition of Color Texture Using Correlation and Covariance Functions
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Moment invariants for recognition under changing viewpoint and illumination
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Illumination invariant recognition of three-dimensional texture in color images
Journal of Computer Science and Technology
Extension of Moment Features' Invariance to Blur
Journal of Mathematical Imaging and Vision
Textile Recognition Using Tchebichef Moments of Co-occurrence Matrices
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
International Journal of Computer Vision
Using tchebichef moment for fast and efficient image compression
Pattern Recognition and Image Analysis
Illumination invariant color texture analysis based on sum- and difference-histograms
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
A comparison of 2-d moment-based description techniques
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
A robust texture feature extraction using the localized angular phase
Multimedia Tools and Applications
| Hi-index | 0.01 |
We develop a method for recognizing color texture independent of rotation, scale, and illumination. Color texture is modeled using spatial correlation functions defined within and between sensor bands. Using a linear model for surface spectral reflectance with the same number of parameters as the number of sensor classes, we show that illumination and geometry changes in the scene correspond to a linear transformation of the correlation functions and a linear transformation of their coordinates. A several step algorithm that includes scale estimation and correlation moment computation is used to achieve the invariance. The key to the method is the new result that illumination, rotation, and scale changes in the scene correspond to a specific transformation of correlation function Zernike moment matrices. These matrices can be estimated from a color image. This relationship is used to derive an efficient algorithm for recognition. The algorithm is substantiated using classification results on over 200 images of color textures obtained under various illumination conditions and geometric configurations