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

We deal with the probiem 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 otained when very noisy images are segmented demonstrate the strengths of this architecture; they also suggest extensions of the approach to other computer vision problem

Temporal precedence in asynchronous visual indexing

A recent trend in model-based object recognition is to build efficient systems for primary hypotheses generation. These systems, also called visual indexing, rely on the assumption that object identification can be performed by recovering local invariants. However, reliable and significant local features are difficult to retrieve when the image background is not uniform.Our solution to this problem takes advantage of a new concept called temporal precedence. The originality of the approach consists in transforming the initial static input image into a dynamic flow of data. Primitives extracted from the image are temporally ranked so as to favor most relevant features for recognition. Most importantly, ranks of features of different kinds may be compared.The architecture of our indexing system is composed of a set of knowledge sources which update in parallel a blackboard structure. The asynchronism produced by the flow of input primitives is used in the activation strategy of knowledge sources