Performance improvement of deep learning based gesture recognition using spatiotemporal demosaicing technique
| Autor: | Baek Hwan Cho, Chang-Woo Shin, Qiang Wang, Jin Man Park, Hyun Lee, Lee Won Jo, Kyoobin Lee, Jooyeon Woo, Hayoung Kim, Kang Hyo A, Paul K. J. Park, Yohan Roh, Hyunsurk Ryu |
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| Rok vydání: | 2016 |
| Předmět: |
Demosaicing
Pixel Computer science business.industry Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology Convolutional neural network Gesture recognition Computer Science::Computer Vision and Pattern Recognition 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence Performance improvement business Image resolution Interpolation |
| Zdroj: | ICIP |
| DOI: | 10.1109/icip.2016.7532633 |
| Popis: | We propose a novel method for the demosaicing of event-based images that offers substantial performance improvement of far-distance gesture recognition based on deep Convolutional Neural Network. Unlike the conventional demosaicing technique using the spatial color interpolation of Bayer patterns, our new approach utilizes spatiotemporal correlation between pixel arrays, whereby timestamps of high-resolution pixels are efficiently generated in real-time from the event data. In this paper, we describe this new method and evaluate its performance with a hand motion recognition task. |
| Databáze: | OpenAIRE |
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