11/12/2022 0 Comments Fisheye software![]() Liwei Chan, Yi-Ling Chen, Chi-Hao Hsieh, Rong-Hao Liang, and Bing-Yu Chen.In The European Conference on Computer Vision (ECCV). Weakly-supervised 3D Hand Pose Estimation from Monocular RGB Images. Yujun Cai, Liuhao Ge, Jianfei Cai, and Junsong Yuan.Association for Computing Machinery, New York, NY, USA, 93-100. In Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces (ITS '09). Enhancing Input on and above the Interactive Surface with Muscle Sensing. Scott Saponas, Dan Morris, and Desney Tan. In The IEEE International Conference on Computer Vision (ICCV). Lending A Hand: Detecting Hands and Recognizing Activities in Complex Egocentric Interactions. Association for Computing Machinery, New York, NY, USA, 1239-1248. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '12). ShoeSense: A New Perspective on Gestural Interaction and Wearable Applications. Gilles Bailly, Jörg Müller, Michael Rohs, Daniel Wigdor, and Sven Kratz.In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Pushing the Envelope for RGB-Based Dense 3D Hand Pose Estimation via Neural Rendering. Seungryul Baek, Kwang In Kim, and Tae-Kyun Kim.Augmented Skeleton Space Transfer for Depth-Based Hand Pose Estimation. Spatio-Temporal Hough Forest for Efficient Detection-Localisation-Recognition of Fingerwriting in Egocentric Camera. The contact finger and hand posture classifiers showed accuracy of approximately 83 and 90%, respectively, across the device sizes. Additionally, we created simple rule-based classifiers that estimate the contact finger and hand posture from DeepFisheye's output. DeepFisheye showed average errors with approximate value of 20 mm for fingertip tracking across the different device sizes. We evaluated DeepFisheye's performance for three device sizes. We created two new hand pose datasets comprising fisheye images, on which our network was trained. DeepFisheye acquires the image of an interacting hand positioned above the touchscreen using the camera and employs deep learning to estimate the 3D position of each fingertip. We present DeepFisheye, a practical NMFT solution for mobile devices, that utilizes a fisheye camera attached at the bottom of a touchscreen. Near-surface multi-finger tracking (NMFT) technology expands the input space of touchscreens by enabling novel interactions such as mid-air and finger-aware interactions. ![]()
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