Hand Segmentation
10 papers with code • 0 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Hand Segmentation
EgoHands
HandNet
EYTH
ObMan-Ego
Latest papers with no code
Residual Attention based Network for Hand Bone Age Assessment
The hierarchical attention components of the residual attention subnet force our network to focus on the key components of the X-ray images and generate the final predictions as well as the associated visual supports, which is similar to the assessment procedure of clinicians.
Learning to Infer the Depth Map of a Hand from its Color Image
We propose the first approach to the problem of inferring the depth map of a human hand based on a single RGB image.
Beyond One Glance: Gated Recurrent Architecture for Hand Segmentation
As evidenced by our results on standard hand segmentation benchmarks and on our own dataset, our approach outperforms these other, simpler recurrent segmentation techniques, as well as the state-of-the-art hand segmentation one.
Fingertip Detection and Tracking for Recognition of Air-Writing in Videos
Moreover, the initialization and termination of mid-air finger writing is also challenging due to the absence of any standard delimiting criterion.
HandSeg: An Automatically Labeled Dataset for Hand Segmentation from Depth Images
We propose an automatic method for generating high-quality annotations for depth-based hand segmentation, and introduce a large-scale hand segmentation dataset.
Gesture-based Bootstrapping for Egocentric Hand Segmentation
Concretely, our approach uses two convolutional neural networks: (1) a gesture network that uses pre-defined motion information to detect the hand region; and (2) an appearance network that learns a person specific model of the hand region based on the output of the gesture network.
3D Hand Pose Tracking and Estimation Using Stereo Matching
This paper demonstrates that the performance of the state-of-the art tracking/estimation algorithms can be maintained with most stereo matching algorithms on the proposed benchmark, as long as the hand segmentation is correct.
Fine Hand Segmentation using Convolutional Neural Networks
We propose a method for extracting very accurate masks of hands in egocentric views.
Left/Right Hand Segmentation in Egocentric Videos
Due to their favorable location, wearable cameras frequently capture the hands of the user, and may thus represent a promising user-machine interaction tool for different applications.
Cascaded Interactional Targeting Network for Egocentric Video Analysis
Firstly, a novel EM-like learning framework is proposed to train the pixel-level deep convolutional neural network (DCNN) by seamlessly integrating weakly supervised data (i. e., massive bounding box annotations) with a small set of strongly supervised data (i. e., fully annotated hand segmentation maps) to achieve state-of-the-art hand segmentation performance.
