A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
International Journal of Computer Vision
Context-free attentional operators: the generalized symmetry transform
International Journal of Computer Vision - Special issue on qualitative vision
Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Image processing and data analysis: the multiscale approach
Image processing and data analysis: the multiscale approach
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Maximum-Likelihood Strategy for Directing Attention during Visual Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of Local Structural Features in Images by Using a Multi-scale Relevance Function
MLDM '99 Proceedings of the First International Workshop on Machine Learning and Data Mining in Pattern Recognition
Multi-scale morphological modeling of a class of structural texture
Machine Graphics & Vision International Journal
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The goal of the image analysis approach presented in this paper was two-fold. Firstly, it is the development of a computational model for visual attention in humans and animals, which is consistent with the known psychophysical experiments and neurology findings in early vision mechanisms. Secondly, it is a model-based design of an attention operator in computer vision, which is capable to detect, locate, and trace objects of interest in images in a fast way. The proposed attention operator, named image relevance function, is an image local operator that has local maximums at the centers of locations of supposed objects of interest or their relevant parts. This approach has several advantageous features in detecting objects in images due to the model-based design of the relevance function and the utilization of the maximum likelihood decision.