Detecting Rotational Symmetries Using Normalized Convolution

Perceptual experiments indicate that corners and curvature are very important features in the process of recognition. This paper presents a new method to detect rotational symmetries, which describes complex curvature such as corners, circles, star, and spiral patterns. It works in two steps; first extract local orientation from a gray-scale or color image, second apply normalized convolution on the orientation image with rotational symmetry filters as basis functions. These symmetries can serve as feature points at a high ion level for use in hierarchical matching structures for 3D estimation, object recognition, image database retrieval etc.

• Type: Conference
• Year: 2000
• Where: ICPR
• Authors: Björn Johansson, Gösta H. Granlund, Hans Knutsson
• Keywords: Apply Normalized Convolution, Computer Vision, ICPR 2000, Rotational Symmetries, Rotational Symmetry Filters