An adaptive image segmentation method with visual nonlinearity characteristics

Abstract

This correspondence is concerned with a method for image segmentation on the visual principle. The inconsistency between the conventional discriminating criterion and the human vision mechanism in perceiving an object and its background is analyzed and an improved discriminating criterion with visual nonlinearity is defined. A new model and an algorithm for image segmentation calculation are proposed based on the spatially adaptive principle of human vision and the relevant hypotheses about object recognition. This is a two-stage process of image segmentation. First, initial segmentation is realized with the bottom-up segmenting algorithm, followed by the goal-driven segmenting algorithm to improve the segmentation results concerning certain regions of interest. Experimental results show that, compared with some conventional and gradient-based segmenting methods, the new method has the excellent performance of extracting small objects from the images of natural scenes with a complicated background.