On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Illumination-Invariant Recognition of 3D Objects Using Local Color Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
Radiometric CCD camera calibration and noise estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Color texture measurement and segmentation
Signal Processing - Special section on content-based image and video retrieval
Comparative experiment with colour texture classifiers using the CCR feature space
Pattern Recognition Letters
Using phase information for symmetry detection
Pattern Recognition Letters
Fourier fractal descriptors for colored texture analysis
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
An appearance-based approach to assistive identity inference using LBP and colour histograms
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Illuminant invariant descriptors for color texture classification
CCIW'13 Proceedings of the 4th international conference on Computational Color Imaging
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A new class of multiscale symmetry features provides a useful high-level representation for color texture. These symmetry features are defined within and between the bands of a color image using complex moments computed from the output of a bank of orientation and scale selective filters. We show that these features not only represent symmetry information but are also invariant to rotation, scale, and illumination conditions. The features computed between color bands are motivated by opponent process mechanisms in human vision. Experimental results are provided to show the performance of this set of features for texture classification and retrieval.