Date of Award


Document Type

Thesis (Undergraduate)


Department of Computer Science

First Advisor

Daniel N. Rockmore


The problem of fitting one image into another is commonly called "registration." Finding the best possible translation and rotation necessary to align two images is one approach to solving this problem. Registration is a crucial component of many remote sensing and medical image interpretation applications. Image alignment techniques aid in volumetric estimations of complicated structures and allow radiologists to accurately identify changes between sequential images. Radiologists require image alignment capabilities to correct for patient motion and/or content displacement between images. Numerous image registration techniques exist for correcting the alignment problems mentioned above. Unfortunately, most of these techniques, such as Correlation, fail to find a good alignment when dealing with images that differ in contrast. The Mutual Information method is able to align images independently of contrast, but it is computationally intensive. We explore a hybrid technique that utilizes both Correlation and Mutual Information. The Hybrid technique hopes to gain greater contrast independence than Correlation alone while achieving a lower running time than a pure Mutual Information technique.


Originally posted in the Dartmouth College Computer Science Technical Report Series, number TR2000-369.