Minimising the energy of active contour model using a Hopfield network

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Minimising the energy of active contour model using a Hopfield network

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© The Institution of Electrical Engineers
Published

Active contour models (snakes) are commonly used for locating the boundary of an object in computer vision applications. The minimisation procedure is the key problem to solve in the technique of active contour models. In this paper, a minimisation method for an active contour model using Hopfield networks is proposed. Due to its network structure, it lends itself admirably to parallel implementation and is potentially faster than conventional methods. In addition, it retains the stability of the snake model and the possibility for inclusion of hard constraints. Experimental results are given to demonstrate the feasibility of the proposed method in applications of industrial pattern recognition and medical image processing.

Inspec keywords: Hopfield neural nets; minimisation; computer vision; image recognition; parallel algorithms; image segmentation

Other keywords: snake model; medical image processing; constrained energy minimisation; parallel implementation; Hopfield network; network structure; active contour model; industrial pattern recognition; computer vision applications; parallel image processing; minimisation procedure

Subjects: Optimisation techniques; Computer vision and image processing techniques; Parallel programming and algorithm theory; Neural computing techniques

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