28 March 1995 Image segmentation with constraint satisfaction synergetic potential network
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By the interpretation of the segmentation process as an assignment of labels to objects dependent on spatial constraints, image segmentation can be described as a constraint satisfaction problem (CSP). Starting from this model, a new technique for the segmentation of medical images is presented: the constraint satisfaction synergetic potential network (CSSPN). In CSSPN the actually possible labels of an object are represented by singular points of synergetic potential systems. The fuzzy-algorithmic initialization model of the CSSPN allows a label-number-independent dimensioning of the network with n2 nodes. The parallel relaxation dynamics of the CSSPN controlled by interactions of the potential systems will bring selection or evolution of the input image by complete deterministic or stochastically perturbed equations of motion in the potential systems. Constraint functions are significant to the relaxation dynamics and to the result of segmentation within an object adjacency, information of the image model like the image semantics or the optimization strategy of network parameters are mapped onto the CSP with them. Experimental comparative analyses of the segmentation results demonstrate the efficiency of the technique and confirm that the CSSPN is a very promising method for image segmentation.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Joerg Peter, Joerg Peter, Richard Freyer, Richard Freyer, } "Image segmentation with constraint satisfaction synergetic potential network", Proc. SPIE 2424, Nonlinear Image Processing VI, (28 March 1995); doi: 10.1117/12.205240; https://doi.org/10.1117/12.205240

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