2001 Fiscal Year Final Research Report Summary
Development of statistical object discrimination system using image characteristics
| Project/Area Number |
11558027
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 展開研究 |
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| Research Field |
Statistical science
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| Research Institution | HIROSHIMA UNIVERSITY |
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Principal Investigator |
ASANO Akira Hiroshima University, Faculty of Integrated Arts and Sciences, 総合科学部, 助教授 (60243987)
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| Co-Investigator(Kenkyū-buntansha) |
ASANO Chie (MURAKI Chie) Okayama University of Science, Faculty of Informatics, Assistant Professor, 総合情報学部, 講師 (00299174)
NISHII Ryuei Hiroshima University, Faculty of Integrated Arts and Sciences, Professor, 総合科学部, 教授 (40127684)
OHTAKI Megu Hiroshima University, Research Institute for Radiological Biology and Medicine, Professor, 原爆放射能医学研究所, 教授 (20110463)
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| Project Period (FY) |
1999 – 2001
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| Keywords | image analysis / object identification / statistical model / logistic discrimination analysis / neural network / image characteristics / kansei engineering |
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| Research Abstract |
We developed in this research a discrimination system of image objects by applying the data identification, which had been already developed the statistical research, to object identification in images, and by evaluating the probability that two given images are derived from an identical object. We achieved that (1) development of an object identification system using the logistic discrimination analysis, and (2) introduction of the layered artificial neural network into the system developed in (1). We also carried out related researches as follows : (3) investigation of relationship between image characteristics and human kansei on textiles, and (4) fundamental research on statistical discrimination. We explain (1) and (2) in the following, and explanations of (3) and (4) are omitted here.
(1) We developed a system that discriminates with the degree of confidence whether the object in an image and that in another image are in the same category or in the different categories. This system measures the differences between each known pair of images subject to several image characteristics, and derives using the maximum likelihood method the logistic function that optimally discriminates pairs belonging to the same category and those belonging to the different categories. A new pair of images is discriminated with the confidence degree by this logistic discrimination function. We applied this system to the discrimination of images of leaves.
(2) We unified the system developed in (1) and the 3-layer neural network. We applied the neural network only for the cases that the error ratio is high, and developed a discrimination system that has the clear discrimination model of the logistic function as well as the high discrimination ability of the neural network. The new hybrid system achieved almost the same discrimination ability as the original neural network and reduced the computation time to one-fifth.
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