A Trainable Low-level Feature Detector

We introduce a trainable system that simultaneously filters and classifies low-level features into types specified by the user. The system operates over full colour images, and outputs a vector at each pixel indicating the probability that the pixel belongs to each feature type. We explain how common features such as edge, corner, and ridge can all be detected within a single framework, and how we combine these detectors using simple probability theory. We show its efficacy, using stereo-matching as an example.

• Type: Conference
• Year: 2004
• Where: ICPR
• Authors: John P. Collomosse, Martin Owen, Peter M. Hall
• Keywords: Classifies Low-level Features, Computer Vision, ICPR 2004, Simple Probability Theory, Single Framework