Satoh Shunji
Department of Information Science for Human Welfare, Tohoku Fukushi University, Sendai 981-8522, Japan.
Biol Cybern. 2006 Sep;95(3):259-70. doi: 10.1007/s00422-006-0088-2. Epub 2006 Jul 28.
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.
提出了一种用于目标检测的视觉模型。为了使检测能力与现有的目标检测技术方法相媲美，该模型中神经元的演化方程是从活动轮廓的计算原理推导出来的。模型的层次结构从演化方程中自然出现。通过引入并公式化凸性（一种视觉属性），减轻了与活动轮廓初始值相关的一个缺点。数值实验表明，所提出的模型能够检测具有复杂拓扑结构的目标，并且对噪声具有容忍性。在所提出的模型中引入了视觉注意模型。其他模拟表明，该模型的视觉属性与揭示图形-背景反转和视觉注意之间关系的心理实验结果一致。我们还证明，该模型倾向于将较小的区域感知为图形，这是人类视觉感知中观察到的一个特征。
