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Proceedings Paper

Markov field model-based approach to image segmentation for target recognition
Author(s): Philippe Hervy
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Paper Abstract

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.

Paper Details

Date Published: 28 July 1997
PDF: 8 pages
Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997); doi: 10.1117/12.280790
Show Author Affiliations
Philippe Hervy, Thomson-CSF (France)


Published in SPIE Proceedings Vol. 3068:
Signal Processing, Sensor Fusion, and Target Recognition VI
Ivan Kadar, Editor(s)

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