V4 shape features for contour representation and object detection.

Cortical area V4 lies in the middle of the visual pathway involved with object recognition. Neurons in V4 selectively respond to different curve fragments along the object contour. In this paper, we propose a computational model that captures the shape features extracted by V4 neurons. The computational model emulated the information processing mechanism in the visual cortex. It extracted curve segments that V4 neurons respond to and quantitatively represented features of the curve segments. The proposed V4 shape features could describe object contours accurately and efficiently. With quantitative evaluation using the MPEG7 shape dataset, we showed that complex shapes could be represented with a very limited number of V4 shape features. Based on V4 features, we further developed a self-organizing map neural network to learn object shape models. The shape model was defined by a group of V4 features with constraints on their spatial relationships. The model was evaluated in object detection experiments using ETHZ objects and INRIA horses datasets. The proposed model could learn to recognize objects by shapes and accurately outline the object contour in the images. Thus, this model provides insight into the neural mechanisms of shape-based object recognition.