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SIGMA pp 99-151 | Cite as

Algorithms for Evidence Accumulation

  • Takashi Matsuyama
  • Vincent Shang-Shouq Hwang
Part of the Advances in Computer Vision and Machine Intelligence book series (ACVM)

Abstract

As we have described in Chaper 2, the SIGMA image understanding system consists of three cooperating reasoning modules plus the Question and Answer Module (QAM). The Geometric Reasoning Expert (GRE) performs evidence accumulation for spatial reasoning and constructs the interpretation of the scene. Often, GRE generates hypotheses about undiscovered objects and initiates the top- down verification analysis. A hypothesis is passed to the Model Selection Expert (MSE), which reasons about the most likely appearance of the object. Then, the description of the expected appearance is given to the Low-Level Vision Expert (LLVE), which verifies/refutes its existence in the image.

Keywords

Object Class Situation Lattice Spatial Reasoning Object Instance Composite Hypothesis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1990

Authors and Affiliations

  • Takashi Matsuyama
    • 1
  • Vincent Shang-Shouq Hwang
    • 2
  1. 1.Okayama UniversityTsushima, OkayamaJapan
  2. 2.Mitre CorporationMcLeanUSA

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