A Region of Interest Based Image Segmentation Method using a Biologically Motivated Selective Attention Model
Seung Hyun Lee, Jaekyoung Moon, Minho Lee · The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006
We propose a new method for a region of interest (ROI) based image segmentation that uses biologically motivated selective attention model. One of the most important issues in image segmentation based on a region of interest (ROI) is how to decide upon a semantic object region according to a specific purpose. The proposed saliency map model in conjunction with a top-down Fuzzy adaptive resonance theory (ART) model for human interaction can generate a scan path that contains a plausible area in a natural scene. In order to extract an interesting region generated by the saliency map model, we propose a new region of interest (ROI) extraction algorithm using scale salient information and multiple features such as a intensity, edge, R+G-, and B+Y-color to reflect more exact salient regions. Computer experimental results show that the proposed model can successfully segment an ROI boundary in natural scenes and computer graphics.