Pattern Recognition, volume 27, issue 2, pages 185-204
Variations on the evidence-based object recognition theme
Publication type: Journal Article
Publication date: 1994-02-01
Journal:
Pattern Recognition
Q1
Q1
SJR: 2.732
CiteScore: 14.4
Impact factor: 7.5
ISSN: 00313203, 18735142
Artificial Intelligence
Software
Signal Processing
Computer Vision and Pattern Recognition
Abstract
The Evidence-Based Object Recognition System of Jain and Hoffman ( IEEE Trans. Pattern Analysis Mach. Intell. 10 (6), 783–802 (1988)) has been extended to include some new view-independent features, a new optimized rule generation procedure based upon Minimum Entropy Clustering and a Neural Network which estimates optimal evidence weights and provides an associated matching procedure. This approach provides an objective definition of the difficulty of an object recognition problem and the procedures and performance of the system are evaluated with four sets of CAD (range) models.
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GOST
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CAELLI T., Dreier A. Variations on the evidence-based object recognition theme // Pattern Recognition. 1994. Vol. 27. No. 2. pp. 185-204.
GOST all authors (up to 50)
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CAELLI T., Dreier A. Variations on the evidence-based object recognition theme // Pattern Recognition. 1994. Vol. 27. No. 2. pp. 185-204.
Cite this
RIS
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TY - JOUR
DO - 10.1016/0031-3203(94)90053-1
UR - https://doi.org/10.1016/0031-3203(94)90053-1
TI - Variations on the evidence-based object recognition theme
T2 - Pattern Recognition
AU - CAELLI, TERRY
AU - Dreier, Ashley
PY - 1994
DA - 1994/02/01
PB - Elsevier
SP - 185-204
IS - 2
VL - 27
SN - 0031-3203
SN - 1873-5142
ER -
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BibTex (up to 50 authors)
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@article{1994_CAELLI,
author = {TERRY CAELLI and Ashley Dreier},
title = {Variations on the evidence-based object recognition theme},
journal = {Pattern Recognition},
year = {1994},
volume = {27},
publisher = {Elsevier},
month = {feb},
url = {https://doi.org/10.1016/0031-3203(94)90053-1},
number = {2},
pages = {185--204},
doi = {10.1016/0031-3203(94)90053-1}
}
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MLA
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CAELLI, TERRY, and Ashley Dreier. “Variations on the evidence-based object recognition theme.” Pattern Recognition, vol. 27, no. 2, Feb. 1994, pp. 185-204. https://doi.org/10.1016/0031-3203(94)90053-1.