Presentation | 2012-12-12 Estimate the gradients of skew symmetric shapes using asymmetry of the shapes on a neural network model Takashi Sugiura, Kazushi Murakoshi, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | It is possible for humans to estimate the gradients of the depth direction of the object in monocular. Some researchers proposed methods to estimate gradients. However, these methods were just based on image processing. In this paper, we propose a method to estimate the gradients close to human visual processing. For verification of method, we estimated the gradients of skew symmetric shapes (symmetric shape tilted in the direction of depth). This model extract the symmetry axis by neural network model. It regards the degree of asymmetry of the outline of shape as features. It calculated the features on square which rotated around the horizontal axis and vertical axis. Then, it learned the features by multilayer perceptron using error back propagation method. Thereafter, it estimated the gradients of rectangle after learning square. As a result, this model could estimate the gradients at $8.75$ degrees average error. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | estimate the gradients / skew symmetric shapes / asymmetry of the outline |
Paper # | NC2012-84 |
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Committee | NC |
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Conference Date | 2012/12/5(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Estimate the gradients of skew symmetric shapes using asymmetry of the shapes on a neural network model |
Sub Title (in English) | |
Keyword(1) | estimate the gradients |
Keyword(2) | skew symmetric shapes |
Keyword(3) | asymmetry of the outline |
1st Author's Name | Takashi Sugiura |
1st Author's Affiliation | Toyohashi University of Technology() |
2nd Author's Name | Kazushi Murakoshi |
2nd Author's Affiliation | Toyohashi University of Technology |
Date | 2012-12-12 |
Paper # | NC2012-84 |
Volume (vol) | vol.112 |
Number (no) | 345 |
Page | pp.pp.- |
#Pages | 4 |
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