VEIL: Research in Knowledge Representation for Computer Vision — Final Report

Abstract

The VEIL (Vision Environment Integrating Loom) project focused on integrating advanced knowledge representation (KR) technology with image understanding technology. VEIL to developed a more declarative approach to the construction of vision systems and produced a tool that incorporates that methodology. Systems were constructed in a more principled fashion that made it possible to share and reuse software across systems. Experiments in two main areas were carried out. We first demonstrated the utility of using Loom as a software engineering tool for a specific vision application (runway detection). We also demonstrated the benefits Loom provides for image understanding itself (event detetion).

The major innovations in this work are as follows:

  1. applied a methodology that maximizes use of declarative knowledge (as opposed to procedural knowledge) in vision systems, thereby enabling us to apply modern software development techniques. The criteria for recognizing objects was stated explicitly in a formal language (instead of being buried in code) making it easier to understand and maintain an application and keep it consistent. Extending the recognition capabilities of the software was made easier.
  2. use of this declarative system construction methodology to facilitate the process of integrating high-level vision routines (such as for recognizing sequences of scenes) with low-level routines that recognize picture elements.
  3. enabling interaction with the system at a level of abstraction appropriate to the domain task. This includes associating collateral information with the objects recognized by low-level image understanding programs.
  4. development of a foundation for a vision ontology.

This work leveraged off the Loom Knowledge Representation system. Loom captures the best features of object-oriented programming, data-driven programming, problem solving, and constraint programming, through the use of an underlying logic-based representation scheme. This system is a powerful tool that incorporates very strong, frame-based representation capabilities, explicit term subsumption, and a number of powerful reasoning paradigms (including logical deduction, object-oriented methods, and production rules). Loom also provides knowledge representation integrity through consistency checking, and provides truth maintenance. Infusing these facilities into the vision problem area, where strong KR capabilities have not yet been developed will significantly alter and improve the methodology for the construction of vision systems. We also developed spatial and temporal reasoning capacities (critical for vision), along with mechanisms to exercise flexible control strategies and incremental scene processing. Finally, Loom was interfaced to a variety of vision processing elements to provide a new tool of extended capabilities. The net result is a powerful software environment for the development of vision systems.

The full paper is available in PDF. (68pp, 2.9MB)

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