Junctions: detection, classification, and reconstruction

A unified approach to detecting, classifying, and reconstructing junctions in images by using a template deformation framework along with a gradient criterium to detect radial partitions of the template and the minimum description length principle to obtain the optimal number of partitions.

Abstract

Junctions are important features for image analysis and form a critical aspect of image understanding tasks such as object recognition. We present a unified approach to detecting, classifying, and reconstructing junctions in images. Our main contribution is a modeling of the junction which is complex enough to handle all these issues and yet simple enough to admit an effective dynamic programming solution. We use a template deformation framework along with a gradient criterium to detect radial partitions of the template. We use the minimum description length principle to obtain the optimal number of partitions that best describes the junction. The Kona detector presented by Parida et al. (1997) is an implementation of this model. We demonstrate the stability and robustness of the detector by analyzing its behavior in the presence of noise, using synthetic/controlled apparatus. We also present a qualitative study of its behavior on real images.