Using multiscale top points in image matching

In this paper we discuss the feasibility of using singular points in a scale space representation (referred to as top points) for image matching purposes. These points are easily extracted from the scale space of an image and they form a compact description of the image. The image matching problem thus becomes a point cloud matching problem. This is related to the transportation problem known from linear optimization and we solve it by using an earth movers distance algorithm. To match points in scale space a distance measure is needed as Euclidean distance no longer applies. In this article we suggest a metric that can be used in scale space and show that it indeed performs better than a Euclidean distance measure. To distinguish between stable and unstable top points we derive a stability norm based on the total variation norm which only depends on the second order derivatives at the top point. To further improve matching results we show that other features at the top points can als...

• Type: Conference
• Year: 2004
• Where: ICIP
• Authors: Bram Platel, Frans Kanters, Luc Florack, Evguenia Balmachnova
• Keywords: Cloud Matching Problem, Euclidean Distance Measure, ICIP 2004, Image Matching Problem, Image Matching Purposes, Image Processing, Scale Space