Introduction to mathematical techniques in pattern recognition
Introduction to mathematical techniques in pattern recognition
Multichannel Texture Analysis Using Localized Spatial Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Designs for Efficient Multiresolution Edge Detection and Orientation Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Generating Kernels for Image Pyramids by Piecewise Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Segmentation Using Fractal Dimension
IEEE Transactions on Pattern Analysis and Machine Intelligence
Strategies for image segmentation combining region and boundary information
Pattern Recognition Letters
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Region-Boundary Cooperative Image Segmentation Based on Active Regions
CCIA '02 Proceedings of the 5th Catalonian Conference on AI: Topics in Artificial Intelligence
Recognition by Symmetry Derivatives and the Generalized Structure Tensor
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multichannel texture segmentation using bamberger pyramids
Journal on Image and Video Processing
Orientation fields filtering by derivates of a Gaussian
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Image scale-space from the heat kernel
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Evaluation of color image segmentation algorithms based on histogram thresholding
VLBV'05 Proceedings of the 9th international conference on Visual Content Processing and Representation
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The problem of uncertainty in image feature description is discussed, and it is shown how finite prolate spheroidal sequences can be used in the construction of feature descriptions that combine spatial and frequency-domain locality in an optimal way. Methods of constructing such optimal feature sets, which are suitable for graphical implementation, are described, and some generalizations of the quadtree concept are presented. These methods are illustrated by examples from image processing applications, including feature extraction and texture description. The problem of image segmentation is discussed, and the importance of scale invariance in overcoming the limitations imposed by uncertainty is demonstrated. A novel texture segmentation algorithm that is based on a combination of the new feature description and multiresolution techniques is described and shown to give accurate segmentations on a range of synthetic and natural textures.