Titlebook: Computer Vision and Machine Learning with RGB-D Sensors; Ling Shao,Jungong Han,Zhengyou Zhang Book 2014 Springer International Publishing

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Hand Parsing and Gesture Recognition with a Commodity Depth Cameraers from the lack of discriminative features to differentiate and track hand parts. In this chapter, we present a robust hand parsing scheme to obtain a high-level and discriminative representation of the hand from raw depth image. A novel distance-adaptive feature selection method is proposed to ge

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Learning Fast Hand Pose Recognitionchitecture, the . (.), for addressing this challenge. The classifier architecture optimizes both classification speed and accuracy when a large training set is available. Speed is obtained using simple binary features and direct indexing into a set of tables, and accuracy by using a large capacity m

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Real-Time Hand Gesture Recognition Using RGB-D Sensor hand motion capture procedure for establishing the real gesture data set. A hand partition scheme is designed for color-based semi-automatic labeling. This method is integrated into a vision-based hand gesture recognition framework for developing desktop applications. We use the Kinect sensor to ac

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The Definitive Guide to Windows Installerlished to help improve calibration accuracy. Uncertainty in depth values has been taken into account systematically. The proposed algorithm is reliable and accurate, as demonstrated by extensive experimental results on simulated and real-world examples.

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https://doi.org/10.1007/978-1-4302-0176-2The matching process is transformed to the problem of Maximum Weight Subgraph search. Excellent retrieval results achieved in a comprehensive setup of challenging experiments show the benefits of our method comparing to the state-of-the-art.

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Technical Considerations When Using db4o with simple contour model and thus supports complex real-time interactions. The experimental evaluations and a real-world demo of hand gesture interaction demonstrate the effectiveness of this framework.

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Calibration Between Depth and Color Sensors for Commodity Depth Cameraslished to help improve calibration accuracy. Uncertainty in depth values has been taken into account systematically. The proposed algorithm is reliable and accurate, as demonstrated by extensive experimental results on simulated and real-world examples.

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Matching of 3D Objects Based on 3D CurvesThe matching process is transformed to the problem of Maximum Weight Subgraph search. Excellent retrieval results achieved in a comprehensive setup of challenging experiments show the benefits of our method comparing to the state-of-the-art.