Texture discrimination by Gabor functions
Biological Cybernetics
The cortex transform: rapid computation of simulated neural images
Computer Vision, Graphics, and Image Processing
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Handbook of pattern recognition & computer vision
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-channel filtering techniques for texture segmentation and surface quality inspection
Multi-channel filtering techniques for texture segmentation and surface quality inspection
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Structure of Locally Orderless Images
International Journal of Computer Vision
Texture Classification by Wavelet Packet Signatures
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Optimal Gabor filters for texture segmentation
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
Pattern Recognition Letters
Automated diagnosis of sewer pipe defects based on machine learning approaches
Expert Systems with Applications: An International Journal
Wavelet-based principal component analysis applied to automated surface defect detection
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
Computer-Aided Vision System for Surface Blemish Detection of LED Chips
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
A Wavelet-Based Neural Network Applied to Surface Defect Detection of LED Chips
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Expert Systems with Applications: An International Journal
WSEAS Transactions on Computer Research
A classifier ensemble for face recognition using gabor wavelet features
CISIS'11 Proceedings of the 4th international conference on Computational intelligence in security for information systems
Image retrieval by local contrast patterns and color
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Local feature saliency for texture representation
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Image retrieval by categorization using LVQ network with wavelet domain perceptual features
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
Hi-index | 0.10 |
Texture analysis is an important issue in many areas like object recognition, image retrieval study, medical imaging, robotics, and remote sensing. Despite the development of a family of techniques over the last couple of decades, there are only a few reliable methods. Multiresolution techniques seem to be attractive for many applications. In this study, we present an approach based on the discrete wavelet transform and scale space concept. We integrate the framework of locally orderless images (LOIs) with the transform coefficients to obtain a flexible method for texture segmentation. Compared to intensity (spatial domain), the wavelet coefficients appear to be more reliable with respect to noise immunity and the ease of feature formation. Hence, we represent each discrete coefficient value with a probability density function to form isophote images. Each isophote image is then convolved with a Gaussian to form LOIs, which specify a local histogram in each transform point. These LOIs, or statistical moments computed from LOIs, can be regarded as texture features. An experiment with the standard Brodatz's and VisTex texture databases demonstrates the superior performance of the wavelet-based LOIs compared to conventional LOI-based moments or wavelet and Gabor energy features. The elegance of the approach is in the relatively greater flexibility in producing segmentation results. A simple minimum distance classifier and confusion matrix analyses confirm the above attributes.