Accession Number : ADA299525

Title :   Alignment by Maximization of Mutual Information

Descriptive Note : Technical rept.,

Corporate Author : MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB

Personal Author(s) : Viola, Paul A.

PDF Url : ADA299525

Report Date : JUN 1995

Pagination or Media Count : 156

Abstract : A new information-theoretic approach is presented for finding the pose of an object in an image. The technique does not require information about the surface properties of the object, besides its shape and is robust with respect to variations of illumination. In our derivation, few assumptions are made about the nature of the imaging process. As a result the algorithms are quite general and can foresceably be used in a wide variety of imaging situations. Experiments are presented that demonstrate the approach registering magnetic resonance (MR) images with computed tomography (CT) images, aligning a complex 3D object model to real scenes including clutter and occlusion, tracking a human head in a video sequence and aligning a view-based 2D object model to real images. The method is based on a formulation of the mutual information between the model and the image called lEMMA. As applied here the technique is intensity-based rather than feature-based. It works well in domains where edge or gradient-magnitude based methods have difficulty, yet it is more robust than traditional correlation. Additionally, it has an efficient implementation that is based on stochastic approximation. Finally, we will describe a number of additional real-world applications that can be solved efficiently and reliably using EMMA. EMMA can be used in machine learning to find maximally informative projections of high%dimensional data. EMMA can also be used to detect and correct corruption in magnetic resonance images (MRI).

Descriptors :   *IMAGE PROCESSING, *SURFACE PROPERTIES, *INFORMATION THEORY, ALGORITHMS, STOCHASTIC PROCESSES, HUMANS, TRACKING, LEARNING MACHINES, MAGNETIC RESONANCE, SEQUENCES, IMAGES, ARTIFICIAL INTELLIGENCE, VIDEO SIGNALS, LEARNING, COMPUTERIZED TOMOGRAPHY.

Subject Categories : Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE