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Hand Pose Estimation Using Deep Stereovision and Markov-chain Monte Carlo

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Academic year: 2019

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Figure

Figure 1: Using an inexpensive stereo camera RGB images of the hand from two perspectives are captured to regress for hand point 3D position (a)
Figure 2: (D(c) Illustrates a conventional approach to estimating pose from stereo capture, where the optimum depth is first resolvedidentifying the joint maximum point (illustrated with the red dot) with enough depth proposals
Figure 4: Structure of two CNN used. (a)  a Siamese Network used as a similarity measure between two potentially matchingsquare patches of pixels, (b) illustrates the structure used for discriminatively regressing for pose given a depth image
Figure 5:are more consistent with the stereo capture and that yields Evaluating the significance of the window size and the number of components used for spanning the prior posespace
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