Object segmentation and binding within a biologically-based neural network model of depth-from-occlusion

Abstract

The problems of object segmentation and binding are addressed within a biologically based network model capable of determining depth from occlusion. In particular, the authors discuss two subprocesses most relevant to segmentation and binding: contour binding and figure direction. They propose that these two subprocesses have intrinsic constraints that allow several underdetermined problems in occlusion processing and object segmentation to be uniquely solved. Simulations that demonstrate the role these subprocesses play in discriminating objects and stratifying them in depth are reported. The network is tested on illusory stimuli, with the network's response indicating the existence of robust psychological properties in the system.<>