Asynchrony in image analysis: using the luminance-to-response-latency relationship to improve segmentation

Abstract

We deal with the problem of segmenting static images, a procedure known to be difficult in the case of very noisy patterns. The proposed approach rests on the transformation of a static image into a data flow in which the first image points to be processed are the brighter ones. This solution, inspired by human perception, in which strong luminances elicit reactions from the visual system before weaker ones, has led to the notion of asynchronous processing. The asynchronous processing of image points has required the design of a specific architecture that exploits time differences in the processing of information. The results obtained when very noisy images are segmented demonstrate the strengths of this architecture; they also suggest extensions of the approach to other computer vision problems.