An Architectural Model of Visual Motion Understanding
Abstract
The past few years have seen an explosion of interest in the recovery and use of visual motion information by biological and machine vision systems. In the area of computer vision, a variety of algorithms have been developed for extracting various types of motion information from images. The central claim of this thesis is that many puzzling aspects of motion perception can be understood by assuming a particular architecture for the human motion processing system. The architecture consists of three functional units or subsystems. The first or low level subsystem computes simple mathematical properties of the visual signal. It is entirely bottom-up, and prone to error when its implicit assumptions are violated. The intermediate-level subsystem combines the low-level system's output with world knowledge, segmentation information and other inputs to construct a representation of the world in terms of primitive forms and their trajectories. In order to compute the trajectories of primitive shapes it is necessary to design mechanisms for handling time and Gestalt grouping effects in connectionist networks. Solutions to these problems are developed and used to construct a network that interprets continuous and apparent motion stimuli in a limited domain. Simulation results show that its interpretations are in qualitative agreement with human perception. Keywords: Vision; Motion perception; Apparent motion; Connectionist models. (kt)
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 1989
- Accession Number
- ADA214327
Entities
People
- Thomas J. Olson
Organizations
- University of Rochester