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A visual model for object detection based on active contours and level-set method

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Abstract

A visual model for object detection is proposed. In order to make the detection ability comparable with existing technical methods for object detection, an evolution equation of neurons in the model is derived from the computational principle of active contours. The hierarchical structure of the model emerges naturally from the evolution equation. One drawback involved with initial values of active contours is alleviated by introducing and formulating convexity, which is a visual property. Numerical experiments show that the proposed model detects objects with complex topologies and that it is tolerant of noise. A visual attention model is introduced into the proposed model. Other simulations show that the visual properties of the model are consistent with the results of psychological experiments that disclose the relation between figure–ground reversal and visual attention. We also demonstrate that the model tends to perceive smaller regions as figures, which is a characteristic observed in human visual perception.

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Authors and Affiliations

  1. Department of Information Science for Human Welfare, Tohoku Fukushi University, Sendai, Japan, 981-8522

    Shunji Satoh

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  1. Shunji Satoh
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Correspondence to Shunji Satoh.

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This work was partially supported by Grants-in-Aid for Scientific Research (#14780254) from Japan Society of Promotion of Science.

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Satoh, S. A visual model for object detection based on active contours and level-set method. Biol Cybern 95, 259–270 (2006). https://doi.org/10.1007/s00422-006-0088-2

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