Northeast Artificial Intelligence Consortium Annual Report for 1987. Volume 7. Parallel, Structural, and Optimal Techniques in Vision

摘要

Investigation is made of various aspects of parallel computer vision, with the goal of building behaving, real-time systems that perform multi-sensory integration. A commitment is made to the idea that an intimate coupling of sensory and motor capabilities is a way to make progress on the vision problem. Behaving animals have such a coupling, and its benefits have been demonstrated analytically. A special-purpose parallel pipelined hardware was acquired for low-level vision, and have upgraded our 16-processor Butterfly Parallel Processor with faster CPUs and floating point hardware. The Butterfly is now connected with the rest of the vision hardware (the pipelined device and a fast Sun/3) via the VME bus. This year's work broadened its focus from the optimal selection of feature detectors in a Bayesian framework. The results were used to apply to a working system that applies a Markov Random Field formulation of the segmentation (objecthood recognition, figure-ground separation) problem. Further work moved in the direction of acquiring and commissioning real-time vision hardware and using the hardware in applications. Theoretical work continues to be important, and this year we made progress on the theory of principal views and convergence properties of two sorts of parallel networks: connectionist nets for recognition and Markov Random Field models of segmentation. (JHD)