Hand Shape and 3D Pose Estimation Using Depth Data from a Single Cluttered Frame

Doliotis, Paul; Athitsos, Vassilis; Kosmopoulos, Dimitrios; Perantonis, Stavros

doi:10.1007/978-3-642-33179-4_15

Cited by 34 publications

(19 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An alternative is to use techniques to estimate the pose of the hand directly from appearance. These approaches however often require some form of 3D input data [21], [22], [23], [24], [25]. While 3D sensing technologies such as IR projection-based RGBD cameras or multi-camera systems can aid in estimating hand pose, the footprint of multi-camera setups or the power consumption of IR projectors can be inhibiting for a wearable sensing scenario.…”

Section: B Sensing Techniquesmentioning

confidence: 99%

Hand parsing for fine-grained recognition of human grasps in monocular images

Saran

Teney

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

We propose a novel method for performing finegrained recognition of human hand grasp types using a single monocular image to allow computational systems to better understand human hand use. In particular, we focus on recognizing challenging grasp categories which differ only by subtle variations in finger configurations. While much of the prior work on understanding human hand grasps has been based on manual detection of grasps in video, this is the first work to automate the analysis process for fine-grained grasp classification. Instead of attempting to utilize a parametric model of the hand, we propose a hand parsing framework which leverages a data-driven learning to generate a pixelwise segmentation of a hand into finger and palm regions. The proposed approach makes use of appearance-based cues such as finger texture and hand shape to accurately determine hand parts. We then build on the hand parsing result to compute high-level grasp features to learn a supervised fine-grained grasp classifier. To validate our approach, we introduce a grasp dataset recorded with a wearable camera, where the hand and its parts have been manually segmented with pixel-wise accuracy. Our results show that our proposed automatic hand parsing technique can improve grasp classification accuracy by over 30 percentage points over a state-of-the-art grasp recognition technique.

show abstract

Section: B Sensing Techniquesmentioning

confidence: 99%

Hand parsing for fine-grained recognition of human grasps in monocular images

Saran

Teney

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

show abstract

“…As a basic task in hand research, hand segmentation contributes to many hand applications, such as pose estimation [8,9], gesture recognition [10,11] and so on.…”

Section: Related Workmentioning

confidence: 99%

DenseAttentionSeg: Segment hands from interacted objects using depth input

Zhang

Yong

et al. 2020

Applied Soft Computing

View full text Add to dashboard Cite

We propose a real-time DNN-based technique to segment hand and object of interacting motions from depth inputs. Our model is called DenseAttentionSeg, which contains a dense attention mechanism to fuse information in different scales and improves the results quality with skip-connections. Besides, we introduce a contour loss in model training, which helps to generate accurate hand and object boundaries. Finally, we propose and release our InterSegHands dataset 1 , a fine-scale hand segmentation dataset containing about 52k depth maps of hand-object interactions. Our experiments evaluate the effectiveness of our techniques and datasets, and indicate that our method outperforms the current state-of-the-art deep segmentation methods on interaction segmentation.

show abstract

“…This method presented both holistic and hierarchical propagation of joint regression. Image retrieval can also be used to resolve the joint positions, inferring the position of joints [31].…”

Section: Accepted Manuscriptmentioning

confidence: 99%