A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchy in Picture Segmentation: A Stepwise Optimization Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multiresolution Hierarchical Approach to Image Segmentation Based on Intensity Extrema
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Resolution Segmentation of Textured Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Filtering Using Multiresolution Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
Image Segmentation by Unifying Region and Boundary Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
An adaptive clustering algorithm for image segmentation
IEEE Transactions on Signal Processing
Zero-crossings of a wavelet transform
IEEE Transactions on Information Theory
A sampling theorem for wavelet subspaces
IEEE Transactions on Information Theory - Part 2
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper, the problem of segmentation of a smooth image has been studied using multiresolution analysis. The analysis has been carried out with the aid of a new orthonormal wavelet basis introduced in this paper. A procedure has been developed to approximate an image at a coarse resolution by dropping its components at finer resolutions. The proposed wavelet basis has been used to represent the fine resolution components of the image. An algorithm is proposed which performs an initial segmentation on the coarse approximation of the image, and a region information table is formed. The table is used and updated repeatedly via a 'region refinement procedure' with the introduction of the image information at finer resolution. The procedure progresses until all the information has been taken into account. The proposed algorithm has been tested on a variety of real images such as human faces, natural scenes, and medical images.