This paper investigates two registration methods for ultrasound image relying on the intensity-based similarity measure. In the first method intensity information is provided by feature points which have been extracted using Harris corner detector. The registration similarity measure is then defined as a cost-function-error cost function.
The method integrates image hierarchical structure,multi-resolution wavelet decomposition technique and Harris corner detector to create a feature point pyramid. And the improved Hausdorff distance is used as the similarity measure for realizing remote sensing image registration.
In this article, the author proposes method to improve the utilization of marked GCPs by storing and reusing them using GCP Image Database , which is a great save of hard-work and method to automatically detect new GCPs by importing some algorithms in computer vision like corner detector.
A VLSI architecture for a half-edge-based corner detector
The Harris corner detector is used for benchmarking.
It consists of two schemes: (1)?extracting robust point and line features from sonar data and (2)?recognizing planar visual objects using a multi-scale Harris corner detector and its SIFT descriptor from a pre-constructed object database.
Axiom 4 The corner detector over a set of constant eigenenergy points attains its maximum value at a point of isotropy.
Axiom 4 in this example requires the corner detector along this constant energy curve to be maximized at the point where 1 = 2 = c/2.