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.
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© 1990 Springer Science+Business Media New York
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Matsuyama, T., Hwang, V.SS. (1990). Algorithms for Evidence Accumulation. In: SIGMA. Advances in Computer Vision and Machine Intelligence. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-0867-4_3
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DOI: https://doi.org/10.1007/978-1-4899-0867-4_3
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4899-0869-8
Online ISBN: 978-1-4899-0867-4
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