Hand Posture Recognition Using a Three-Dimensional Light Field Camera
| Autor: | Chou-Chen Wang, Jiann-Shu Lee, Ngoc Tuyen Le, Jing-Wein Wang |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Computer science
Feature vector Feature selection 02 engineering and technology Color space 01 natural sciences Grayscale law.invention symbols.namesake law 0202 electrical engineering electronic engineering information engineering Computer vision Electrical and Electronic Engineering Instrumentation Mahalanobis distance Light-field camera business.industry 010401 analytical chemistry Color solid 0104 chemical sciences Gaussian noise Computer Science::Computer Vision and Pattern Recognition Principal component analysis symbols 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | IEEE Sensors Journal. 16:4389-4396 |
| ISSN: | 2379-9153 1530-437X |
| DOI: | 10.1109/jsen.2016.2546556 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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