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- W2313305498 abstract "This paper used a light field camera to capture two types of 3D hand posture depth images, contour hand posture and solid hand posture, without a complicated setup and superfluous preprocess. The images were captured under two recognition conditions: in-plane and out-of-plane rotations. The posture recognition features two methods: the first method is the principal component analysis (PCA), which is used to obtain the required feature vectors, associated with the k-nearest neighbor (k-NN) algorithm as a classifier; the second method involves using 2D optimal-PCA (2DOPCA) combined with a genetic algorithm (GA) for feature selection, and the Mahalanobis distance is then used for classification. The variations in the test images include in-plane rotation, out-of-plane rotation, and Gaussian noise added to simulate the lighting interference in a real situation. The results showed that the PCA combined with the k-NN yields high recognition rates for grayscale contour images with in-plane and out-of-plane rotations and color solid posture images with in-plane rotation. For color solid posture images with out-of-plane rotation, the projection color space was combined with the PCA and k-NN methods to obtain high recognition rates. Moreover, the contour and color solid posture images with noise more than 5% require the 2DOPCA combined with the GA to obtain a satisfactory result and maintain recognition rate stability." @default.
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- W2313305498 date "2016-06-01" @default.
- W2313305498 modified "2023-09-24" @default.
- W2313305498 title "Hand Posture Recognition Using a Three-Dimensional Light Field Camera" @default.
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- W2313305498 doi "https://doi.org/10.1109/jsen.2016.2546556" @default.
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