How do we see the motion of objects as well as their shapes? The Gaussian Derivative (GD) spatial model is extended to time to help answer this question. The GD spatio-temporal model requires only two numbers to describe the complete three-dimensional space-time shapes of individual receptive fields in primate visual cortex. These two numbers are the derivative numbers along the respective spatial and temporal principal axes of a given receptive field. Nine transformation parameters allow for a standard geometric association of these intrinsic axes with the extrinsic environment. The GD spatio-temporal model describes in one framework the following properties of primate simple cell fields: motion properties, number of lobes in space-time, spatial orientation. location, and size. A discrete difference-of-offset-Gaussians (DOOG) model provides a plausible physiological mechanism to form GD-like model fields in both space and time. The GD model hypothesizes that receptive fields at the first stage of processing in the visual cortex approximate 'derivative analyzers' that estimate local spatial and temporal derivatives of the intensity profile in the visual environment. The receptive fields as modeled provide operators that can allow later stages of processing in either a biological or machine vision system to estimate the motion as well as the shapes of objects in the environment.

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Abstract. Dimensional management is a form of quality assurance for the manufacture of mechanical structures, such as vehicle bodies. Establishing and maintaining dimensional control is a process of adjusting complex machinery for environmental and material changes to manufacture product to specifications within very small tolerances. It involves constant monitoring of the process as well as responding to crises. A good deal of undocumented "folk wisdom" is built up by the dimensional management teams on how to diagnosis and cure problems, but this knowledge tends to be lost over time (people can't remember, people move on) and is rarely shared from shop to shop. Our project involves establishing a case-based diagnostic system for dimensionalmanagement problems, which can also serve as a system for systematically documenting solved dimensional-control problems. It is intended that this documentation should be meaningful over time and be shareable between plants. The project includes defining a workable case structure and matching ontology, especially to establish the context and generic language to accomplish this. A prototype system has been launched in a vehicle assembly plant.

The General Motors Variation-Reduction Adviser is a knowledge system built on case-based reasoning principles that is currently in use in a dozen General Motors Assembly Centers. This paper reviews the overall characteristics of the system and then focuses on various AI elements critical to support its deployment to a production system. A key AI enabler is ontology-guided search using domain-specific ontologies.

This paper presents the methodology of Digital Panel Assembly (DPA), a computer aided approach in automotive body panel assembly. Core to the methodology is special-purpose finite element software, EAVS, that can simulate the panel assembly processes and predict assembly dimensions by taking into considerations of the compliant nature of panels and sub-assemblies. To validate the methodology, a non-contact EOIS optical scanning procedure for panel measurement is established. The validation study shows that the reported methodology can accurately predict the 1st level panel sub-assemblies. Finally, the resource requirements, functional capabilities, and scalability of the digital panel assembly methodology towards a complete Body-in-White implementation are discussed.