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MOTION IS AN IMPORTANT AND FUNDAMENTAL SOURCE OF VISUAL INFORMATION. IT IS WELL KNOWN THAT THE PATTERN OF IMAGE MOTION CONTAINS INFORMATION USEFUL FOR THE DETERMINATION OF THE 3-DIMENSIONAL STRUCTURE OF THE ENVIRONMENT AND THE RELATIVE MOTION BETWEEN THE CAMERA AND THE OBJECTS IN THE SCENE. HOWEVER, THE ACCURATE MEASUREMENT OF IMAGE MOTION FROM A SEQUENCE OF REAL IMAGES HAS PROVEN TO BE DIFFICULT. IN THIS THESIS, A HIERARCHICAL FRAMEWORK FOR THE COMPUTATION OF DENSE DISPLACEMENT FIELDS FROM PAIRS OF IMAGES, AND AN INTEGRATED SYSTEM CONSIS- TENT WITH THAT FRAMEWORK ARE DESCRIBED. EACH INPUT INTENSITY IMAGE IS FIRST DECOMPOSED USING A SET OF SPATIAL-FREQUENCY TUNED CHANNELS. THE INFORMATION IN THE LOW-FREQUENCY CHANNELS IS USED TO PROVIDE ROUGH DISPLACEMENTS OVER A LARGE RANGE, WHICH ARE THEN SUCCESSIVELY REFINED BY USING THE INFORMATION IN THE HIGHER-FREQUENCY CHANNELS. WITHIN EACH CHANNEL, A DIRECTION- DEPENDENT CONFIDENCE MEASURE IS COMPUTED FOR EACH DISPLACEMENT VECTOR, AND A SMOOTHNESS CONSTRAINT IS USED TO PROPOGATE RELIABLE DISPLACEMENT VECTORS TO THEIR NEIGHBORING AREAS WITH LESS RELIABLE VECTORS. FOR OUR INTEGRATED SYSTEM, BURT''S LAPLACIAN PYRAMID TRANSFORM IS USED FOR THE SPATIAL-FREQUENCY DECOMPOSITION, AND THE MINIMIZATION OF THE SUM OF SQUARED DIFFERENCES MEASURE (SSD) IS USED AS THE MATCH CRITERION. THE CON- FIDENCE MEASURE IS DERIVED FROM THE SHAPE OF THE SSD SURFACE, AND THE SMOOT