Fuzzy model for scene decomposition based on preattentive visual features
Ross Andrew Brown, Binh Pham, Anthony John Maeder · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1999
Feature detectors in the early preattentive stage of the Human Visual System (HVS) are believed to cause region of the viewing field to be identified as perceptually salient, attracting the attention of the viewer. It is anticipated that this characteristic of the HVS can be incorporated into a feature based fuzzy scene decomposition model, which will assist an image rendering system in the allocation of the highest levels of detail to the most conspicuous objects. Efficiency gains should occur, with minimal loss of perceptual image quality. This paper describes the early stages of the development of this fuzzy model, for a small subset of commonly accepted visual features: color, size, location, edges and depth cues. Previous researchers have used arbitrary feature relationship models in image processing systems, with some success. Our aim is to improve on these models by integrating present knowledge of visual feature relationships, with experimental results of our own, and to apply this model to the area of image synthesis. Preliminary results from experiments with size features are presented, along with planned experimentation for other visual features. This work will have applications in the areas of scientific visualization, vision simulation and entertainment.