Chapter 6: Feature detection in scale-space

Chapter 6 in Scale-Space Theory in Computer Vision shows how the methodology for discrete derivative approximations proposed in chapter 5 leads to a conceptually simple scheme of computations for multi-scale low-level feature extraction, consisting of four basic steps;

• large support convolution smoothing,
• small support difference computations,
• point operations for computing differential geometric entities, and
• nearest neighbour operations for feature detection.

Applications are given demonstrating how the proposed scheme can be used for edge detection and junction detection based on derivatives up to order three.