Convolutional Neural Networks for Pose Recognition in Binary Omni-directional Images
| Autor: | Vassilis P. Plagianakos, Ilias Maglogiannis, Kostas Delibasis, Spiros V. Georgakopoulos, Konstantina Kottari |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Computational complexity theory
Computer science business.industry Calibration (statistics) Zernike polynomials Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Omni directional Binary number 020207 software engineering Neocognitron 02 engineering and technology Convolutional neural network symbols.namesake 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
| Zdroj: | IFIP Advances in Information and Communication Technology ISBN: 9783319449432 AIAI |
| Popis: | In this work, we present a methodology for pose classification of silhouettes using convolutional neural networks. The training set consists exclusively from the synthetic images that are generated from three-dimensional (3D) human models, using the calibration of an omni-directional camera (fish-eye). Thus, we are able to generate a large volume of training set that is usually required for Convolutional Neural Networks (CNNs). Testing is performed using synthetically generated silhouettes, as well as real silhouettes. This work is in the same realm with previous work utilizing Zernike image descriptors designed specifically for a calibrated fish-eye camera. Results show that the proposed method improves pose classification accuracy for synthetic images, but it is outperformed by our previously proposed Zernike descriptors in real silhouettes. The computational complexity of the proposed methodology is also examined and the corresponding results are provided. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|