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Stereo-disparity perception for a monochromatic flat slope based on a neural network dynamic model

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Abstract

Stereo-matching is one of the most active research topics in computer vision. In this article, the stereo-correspondence problem for a stereo-image pair on a monochromatic surface is considered. Even if some hints exist, it is not easy to reconstruct the correct 3-D scene from two images because it is an ill-posed problem. We have modified our previous competitive and cooperative neural network model so that we can efficiently perceive a monochromatic surface which is enclosed by two vertical stripes. The modification consists of two factors: (1) combining the parameterized multiple inputs (similarities); (2) extending the cooperative terms of the neural network equation. The effect of the proposed model is examined by experiments with both synthetic and real stereo-image pairs. For the real images, a segmentation method is proposed to deal with the similarity maps.

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Authors and Affiliations

  1. Department of Computer Science and Systems Engineering, Faculty of Engineering, Miyazaki University, Gakuen Kibanadai Nishi 1-1, 889-2155, Miyazaki, Japan

    X. Hua, Y. Tang, M. Yokomichi & T. Kitazoe

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  1. X. Hua
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  2. Y. Tang
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  3. M. Yokomichi
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  4. T. Kitazoe
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Corresponding author

Correspondence to X. Hua.

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Hua, X., Tang, Y., Yokomichi, M. et al. Stereo-disparity perception for a monochromatic flat slope based on a neural network dynamic model. Artif Life Robotics 7, 63–68 (2003). https://doi.org/10.1007/BF02480887

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  • DOI: https://doi.org/10.1007/BF02480887

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