Sonar image registration through symbolic matching: a fuzzy local transform approach using genetic algorithms

Depending on sonar characteristics (resolution, ...) and tow fish configuration (location, heading, altitude, ...), sonar images present different properties. An algorithm which registers directly all these images would be very complex and hardly evolutive. In order to overcome these problems, the authors propose a two-step registrating system: its first stage is acquisition-dependent and extracts information through processed tailored for each sonar type. Then, this symbolic knowledge feeds a generic system whose goal is to perform the matching task between current information and previously recorded data. Examples of such data range from coarse detected objects (low resolution sensor) to finely delimited homogeneous sea-bed regions (texture analysis relying on higher resolution sonar outputs). Thus, multiple symbolic information can be extracted from the same sea-bed region when various tracks overlap. Nevertheless, precision on tow-fish navigational data recorded with sonar images, do not allow one to directly match common information. Thus, the authors implement such a matching mechanism using genetic algorithms with variable-length chromosomes coding a field of transformations the size of which is not a priori known. This new flexibility allows the registration of different tracks covering a same sea-bed region with multiple distortions.