12/14/2023 0 Comments Virtual piano app![]() The posterior distribution of the joints is formulated as a weighted product of experts model based on the individual pixel predictions, which is maximized via the expectation–maximization algorithm on a learned low-dimensional space of the hand joint parameters. In addition, we further enforce the hand motion constraints to fuse the per-pixel prediction results. The hand parts are utilized as the additional cue to resolve the multimodal predictions produced by the previous regression-based method without increasing the computational cost significantly. We present a novel method to predict the 3-D joint positions from the depth images and the parsed hand parts obtained with a pretrained classifier. The positions of the hand joints are important high-level features for hand-based human-computer interaction. We evaluate our approach on a real-world annotated dataset and propose a novel annotation technique for accurate 3D hand labelling even in case of partial occlusions. We also exploit the depth features for a sparser and faster detection. We propose to use task and viewpoint specific synthetic training exemplars in a discriminative detection framework. ![]() The problem is exacerbated when considering a wearable sensor and a first-person camera viewpoint: the occlusions inherent to the particular camera view and the limitations in terms of field of view make the problem even more difficult. ![]() Despite the recent advances in full-body pose estimation using Kinect-like sensors, reliable monocular hand pose estimation in RGB-D images is still an unsolved problem. For this problem specification, we show that depth sensors are particularly informative for extracting near-field interactions of the camera wearer with his/her environment. We focus on the task of hand pose estimation from egocentric viewpoints.
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