Using the A* algorithm, we propose a new heuristic search strategy to find object boundaries. We show that a cost function with a Gaussian curvature is more appropriate to develop only the best paths. An application to the detection of cells boundaries is presented.

We address the problem of identifying Posidonia oceanica areas in multispectral aerial seacoast images. P. oceanica is a marine phanerogain endemic to the Mediterranean. Several diving have been performed in order to get information at specific locations. At each point (context point), a measure of depth has been made and the presence or absence of P. oceanica has been noticed. The first difficulty is to separate the sea from the coast. We propose a least square technique to distinguish sea points from the others. Then, the major problem is that the color of P. oceanica in shallow water is the same as the color of the sand in deeper water. We proceed in two steps: for each point of the sea, the three closer context points are used to. compute a depth map. Then, the sea points are split in three categories, with respect to the depth. A classification is learned from the context points, and the final segmentation is obtained by generalizing to all points of the sea.