On the Sequential Accumulation of Evidence

  • Authors:
  • Tal Arbel;Frank P. Ferrie

  • Affiliations:
  • Centre for Intelligent Machines, McGill University, 3480 University St., Montréal, Québec, Canada H3A 2A7. taly@cim.mcgill.edu;Centre for Intelligent Machines, McGill University, 3480 University St., Montréal, Québec, Canada H3A 2A7. ferrie@cim.mcgill.ca

  • Venue:
  • International Journal of Computer Vision - Special issue: Research at McGill University
  • Year:
  • 2001

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Abstract

In this paper, we introduce a method for sequentially accumulating evidence as it pertains to an active observer seeking to identify an object in a known environment. We develop a probabilistic framework, based on a generalized inverse theory, where assertions are represented by conditional probability density functions. This leads to a sequential recognition strategy in which evidence is accumulated over successive viewpoints using Bayesian chaining until a definitive assertion can be made. To illustrate the theory we show how the characteristics of belief distributions can be exploited in a model-based recognition problem, where the task is to identify an unknown model from a database of known objects on the basis of parameter estimates. We illustrate the robustness of the algorithm through recognition experiments in two very different contexts: (1) a highly structured recognition context where 3-D parametric models can be estimated directly from range data, (2) a complex environment, where the relationship between the data and the model is learned through an appearance-based strategy. Specifically, the flow fields computed through the object's motion are used as structural signatures for recognition.