| Presentation | 2009/1/5 FIGURE ALPHABET HYPOTHESIS INSPIRED NEURAL NETWORK RECOGNITION MODEL(International Workshop on Advanced Image Technology 2009) Ryoji Ohira, Kenji Saiki, Tomoharu Nagao, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The object recognition mechanism of human being is not well understood yet. On research of animal experiment using an ape, however, neurons that respond to simple shape (e.g. circle, triangle, square and so on) were found. And Hypothesis has been set up as human being may recognize object as combination of such simple shapes. That mechanism is called Figure Alphabet Hypothesis, and those simple shapes are called Figure Alphabet. As one way to research object recognition algorithm, we focused attention to this Figure Alphabet Hypothesis. Getting idea from it, we proposed the feature extraction algorithm for object recognition. In this paper, we described recognition of binarized images of multifont alphabet characters by the recognition model which combined three-layered neural network in the feature extraction algorithm. First of all, we calculated the difference between the learning image data set and the template by the feature extraction algorithm. The computed finite difference is a feature quantity of the feature extraction algorithm. We had it input the feature quantity to the neural network model and learn by backpropagation(BP method). We had the recognition model recognize the unknown image data set and found the correct answer rate. To estimate the performance of the contriving recognition model, we had the unknown image data set recognized by a conventional neural network. As a result, the contriving recognition model showed a higher correct answer rate than a conventional neural network model. Therefore the validity of the contriving recognition model could be proved. We'll plan the research a recognition of natural image by the contriving recognition model in the future. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | figure alphabet hypothesis / neural network / feature extraction algorithm |
| Paper # | IE2008-136 |
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| Conference Information | |
| Committee | IE |
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| Conference Date | 2009/1/5(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 | Image Engineering (IE) |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | FIGURE ALPHABET HYPOTHESIS INSPIRED NEURAL NETWORK RECOGNITION MODEL(International Workshop on Advanced Image Technology 2009) |
| Sub Title (in English) | |
| Keyword(1) | figure alphabet hypothesis |
| Keyword(2) | neural network |
| Keyword(3) | feature extraction algorithm |
| 1st Author's Name | Ryoji Ohira |
| 1st Author's Affiliation | Department of Information Media and Environment Graduate School of Environment and Information Sciences Yokohama National University:Mechanics Platform Systems Development Department Systems Hardware Development Division Systems Hardware Company Oki Electric Industry Co., Ltd.() |
| 2nd Author's Name | Kenji Saiki |
| 2nd Author's Affiliation | Department of Information Media and Environment Graduate School of Environment and Information Sciences Yokohama National University |
| 3rd Author's Name | Tomoharu Nagao |
| 3rd Author's Affiliation | Department of Information Media and Environment Graduate School of Environment and Information Sciences Yokohama National University |
| Date | 2009/1/5 |
| Paper # | IE2008-136 |
| Volume (vol) | vol.108 |
| Number (no) | 373 |
| Page | pp.pp.- |
| #Pages | 4 |
| Date of Issue | |