Statistical model and genetic optimization: application to pattern detection in sonar images

M. Mignotte, C. Collet, P. Perez, P. Bouthemy
Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181)  
We present a new classi cation method using deformable template model to separate natural objects from man made objects in an image given by a high resolution sonar. A prior knowledge of the manufactured object shadow shape is described by a prototype template and a set of admissible linear transformations to take i n to account the shape variability. Then, the classi cation problem is de ned as a two step process; rstly the detection problem of a region of interest in the input image is stated
more » ... put image is stated in a Bayesian framework and is posed as an equivalent energy minimization problem of an objective function: in this paper, this energy minimization problem is solved by using a hybrid Genetic Algorithm GA. Secondly, the value of this function at convergence allows to determine the presence of the desired object in the sonar image. This method has been successfully tested on real and synthetic sonar images 1 , yielding very promissing results.
doi:10.1109/icassp.1998.678090 dblp:conf/icassp/MignotteCPB98 fatcat:4vyjazvw5jg6bdoe75jwyyzirm