Presentation | 1994/5/19 Learning Model Structures from Binary Images Andreas Held, Keiichi Abe, |
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PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Based on a newly proposed notion of relational network,a novel learning mechanism for image model acquisition is developed. Starting from a decomposition of two-dimensional binary objects into meaningful parts,first a description of the decomposition in terms of relational networks is obtained.Based on the description of two or more instances of the same concept,generalizations are obtained by first finding matchings between instances.Generalizing itself proceeds on two levels:the topological and the predicate level.After generalizing,the system attempts to find an explanation of the result in terms of natural language.An example underlines the validity of relational networks and illustrates the performance of the proposed system. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Shape recognition / Model acquisition / Learning / Relational network |
Paper # | PRU94-2 |
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Conference Information | |
Committee | PRU |
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Conference Date | 1994/5/19(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Pattern Recognition and Understanding (PRU) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Learning Model Structures from Binary Images |
Sub Title (in English) | |
Keyword(1) | Shape recognition |
Keyword(2) | Model acquisition |
Keyword(3) | Learning |
Keyword(4) | Relational network |
1st Author's Name | Andreas Held |
1st Author's Affiliation | Graduate School of Electronic Science and Technology,Shizuoka University() |
2nd Author's Name | Keiichi Abe |
2nd Author's Affiliation | Department of Computer Science,Faculty of Engineering,Shizuoka University |
Date | 1994/5/19 |
Paper # | PRU94-2 |
Volume (vol) | vol.94 |
Number (no) | 50 |
Page | pp.pp.- |
#Pages | 8 |
Date of Issue |