Digital image processing
Handbook of pattern recognition & computer vision
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
Degraded Image Analysis: An Invariant Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Outex - New Framework for Empirical Evaluation of Texture Analysis Algorithms
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of the blurred image by complex moment invariants
Pattern Recognition Letters
Evaluation of Features Detectors and Descriptors based on 3D Objects
International Journal of Computer Vision
Blur Insensitive Texture Classification Using Local Phase Quantization
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
WLD: A Robust Local Image Descriptor
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Color texture segmentation based on image pixel classification
Engineering Applications of Artificial Intelligence
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This paper proposes a novel descriptor, referred to as the localized angular phase (LAP), which is robust to illumination, scaling, and image blurring. LAP utilizes the phase information from the Fourier transform of the pixels in localized polar space with a fixed radius. The application examples of LAP are presented in terms of content-based image retrieval, classification, and feature extraction of real-world degraded images and computer-aided diagnosis using medical images. The experimental results show that the classification performance of LAP in terms of the latter application examples are better than those of local phase quantization (LPQ), local binary patterns (LBP), and local Fourier histogram (LFH). Specially, the capability of LAP to analyze degraded images and classify abnormal regions in medical images are superior to those of other methods since the best overall classification accuracy of LAP, LPQ, LBP, and LFH using degraded textures are 91.26, 61.23, 35.79, and 33.47%, respectively, while the sensitivity of LAP, LBP, and spatial gray level dependent method (SGLDM) in classifying abnormal lung regions in CT images are 100, 95.5, and 93.75%, respectively.