One shot learning gesture recognition from RGBD images

Wu, Di, Zhu, Fan and Shao, Ling (2012) One shot learning gesture recognition from RGBD images. In: CVPRW 2012 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 16th - 21st June 2012, Providence, USA.

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We present a system to classify the gesture from only one learning example. The inputs are duo-modality, i.e. RGB and depth sensor from Kinect. Our system performs morphological denoising on depth images and automatically segments the temporal boundaries. Features are extracted based on Extended-Motion-History-Image (Extended-MHI) and the Multi-view Spectral Embedding (MSE) algorithm is used to fuse duo modalities in a physically meaningful manner. Our approach achieves less than 0.3 in Levenshtein distance in CHALEARN Gesture Challenge validation batches.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: feature extraction, gesture recognition, image classification, image colour analysis, image denoising, image segmentation, artificial intelligence, spatial variables measurement
Subjects: G400 Computer Science
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 16 Jun 2015 14:36
Last Modified: 13 Oct 2019 00:32

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