Shape recovery by a generalized topology preserving SOM

This paper proposes a new deformable model, i.e., gTPSOM, for object shape recovery. Inspired by visual induced self-organizing map (ViSOM) and region-aided active contour, the proposed model is formulated as generalized chain SOM with an adaptive force field. The adaptive force field is adjusted during the evolvement of the neuron chain according to local consistency of the image edge map. With the topology preserving property inherited from the data mapping model, i.e., ViSOM, the proposed model is suitable for both the precise edge detection and the complex shape recovery with boundary strength variations. Detailed formulation and analysis of the proposed model are given. Experiments on both synthesis and real images are carried out to demonstrate the performances.