Markov field model-based approach to image segmentation for target recognition
Philippe Hervy · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
To achieve robust and efficient model-based object recognition, particularly from real outdoor images, we must extract salient information of the objects. Unfortunately the low-level processing procedures most of the time erroneous or incomplete primitives. Towards this end, we present an original technique to extract salient segments taking into account the geometrical specificity of the model: parallelism, T-junctions, main directions for example. Our method uses a markovian model defined on the spatial adjacency formed by structured edge primitives, on extracted features measurements and on domain knowledge. In this paper, we describe the Gibbs distribution associated to the proposed model (sites and its components and cliques representing the domain knowledge). We use a deterministic algorithm ICM (Iterated Conditional Mode) to generate a sub- optimal configuration. We also describe the energy function to minimize and how we initialize the Markov field. We specify the automatic convergence criteria depending on the model. Experimental results on real world images for different model-based recognition will be presented. Finally, we will touch on the implementation aspects and the computational time for real time applications.