A Visual Attention Operator Based on Morphological Models of Images and Maximum Likelihood Decision

Abstract

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.KeywordsVisual AttentionObject DetectionHuman Visual SystemRelevance FunctionPlanar ShapeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.