scholarly journals The Dynamic Model Embed in Augmented Graph Cuts for Robust Hand Tracking and Segmentation in Videos

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jun Wan ◽  
Qiuqi Ruan ◽  
Gaoyun An ◽  
Wei Li ◽  
Yanyan Liang ◽  
...  
Keyword(s):  
Dynamic Model ◽  
Spatial Information ◽  
Graph Cuts ◽  
Hand Tracking ◽  
Human Hand ◽  
Dynamic Background ◽  
Hand Motion ◽  
Hand Segmentation ◽  
New Energy ◽  
Fast Motion

Segmenting human hand is important in computer vision applications, for example, sign language interpretation, human computer interaction, and gesture recognition. However, some serious bottlenecks still exist in hand localization systems such as fast hand motion capture, hand over face, and hand occlusions on which we focus in this paper. We present a novel method for hand tracking and segmentation based on augmented graph cuts and dynamic model. First, an effective dynamic model for state estimation is generated, which correctly predicts the location of hands probably having fast motion or shape deformations. Second, new energy terms are brought into the energy function to develop augmented graph cuts based on some cues, namely, spatial information, hand motion, and chamfer distance. The proposed method successfully achieves hand segmentation even though the hand passes over other skin-colored objects. Some challenging videos are provided in the case of hand over face, hand occlusions, dynamic background, and fast motion. Experimental results demonstrate that the proposed method is much more accurate than other graph cuts-based methods for hand tracking and segmentation.

scholarly journals HAND SEGMENTATION AND TRACKING OF CONTINUOUS HAND POSTURE USING MORPHOLOGICAL PROCESSING

2014 ◽  
pp. 247-251
Author(s):  
MADHURJYA KUMAR NAYAK ◽  
ANJAN KUMAR TALUKDAR ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Recognition System ◽  
Morphological Processing ◽  
Hand Tracking ◽  
Hand Posture ◽  
Hand Detection ◽  
Dynamic Background ◽  
Lighting Condition ◽  
Background Removal ◽  
Hand Segmentation ◽  
Detection Schemes

This work reports the design of a continuous hand posture recognition system. Hand tracking and segmentation are the primary steps for any hand gesture recognition system. The aim of this paper is to report a noise resistant and efficient hand segmentation algorithm where a new method for hand segmentation using different hand detection schemes with required morphological processing are utilized. Problems such as skin colour detection, complex background removal and variable lighting condition are found to be efficiently handled with this system. Noise present in the segmented image due to dynamic background can be removed with the help of this technique. The proposed approach is found to be effective for a range of conditions.

scholarly journals A Systematic Review of EMG Applications for the Characterization of Forearm and Hand Muscle Activity during Activities of Daily Living: Results, Challenges, and Open Issues

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3035
Author(s):  
Néstor J. Jarque-Bou ◽  
Joaquín L. Sancho-Bru ◽  
Margarita Vergara
Keyword(s):  
Activities Of Daily Living ◽  
Musculoskeletal System ◽  
Muscle Activity ◽  
Daily Living ◽  
Muscle Activation ◽  
Human Hand ◽  
Hand Motion ◽  
Biomechanical Models ◽  
Rehabilitation Protocols ◽  
Open Issues

The role of the hand is crucial for the performance of activities of daily living, thereby ensuring a full and autonomous life. Its motion is controlled by a complex musculoskeletal system of approximately 38 muscles. Therefore, measuring and interpreting the muscle activation signals that drive hand motion is of great importance in many scientific domains, such as neuroscience, rehabilitation, physiotherapy, robotics, prosthetics, and biomechanics. Electromyography (EMG) can be used to carry out the neuromuscular characterization, but it is cumbersome because of the complexity of the musculoskeletal system of the forearm and hand. This paper reviews the main studies in which EMG has been applied to characterize the muscle activity of the forearm and hand during activities of daily living, with special attention to muscle synergies, which are thought to be used by the nervous system to simplify the control of the numerous muscles by actuating them in task-relevant subgroups. The state of the art of the current results are presented, which may help to guide and foster progress in many scientific domains. Furthermore, the most important challenges and open issues are identified in order to achieve a better understanding of human hand behavior, improve rehabilitation protocols, more intuitive control of prostheses, and more realistic biomechanical models.

Identify Finger Rotation Angles With ArUco Markers and Action Cameras

2021 ◽  
Author(s):  
Tianyun Yuan ◽  
Yu (Wolf) Song ◽  
Gerald A. Kraan ◽  
Richard H. M. Goossens
Keyword(s):  
Dynamic Time Warping ◽  
Tracking System ◽  
Low Cost ◽  
Finger Joint ◽  
Hand Tracking ◽  
Human Hand ◽  
Time Warping ◽  
Joint Rotation ◽  
Dynamic Time ◽  
The Right

Abstract Measuring the motion of human hand joints is a challenging task due to the high number of DOFs. In this study, we proposed a low-cost hand tracking system built on action cameras and ArUco markers to measure finger joint rotation angles. The lens distortion of each camera was corrected first via intra-calibration and the videos of different cameras were aligned to the reference camera using a dynamic time warping based method. Two methods were proposed and implemented for extracting the rotation angles of finger joints: one is based on the 3D positions of the markers via inter-calibration between cameras, named pos-based method; the other one is based on the relative marker orientation information from individual cameras, named rot-based method. An experiment was conducted to evaluate the effectiveness of the proposed system. The right hand of a volunteer was included in this practical study, where the movement of the fingers was recorded and the finger rotation angles were calculated with the two proposed methods, respectively. The results indicated that although using the rot-based method may collect less data than using the pos-based method, it was more stable and reliable. Therefore, the rot-based method is recommended for measuring finger joint rotation in practical setups.

Gesture Recognition

2017 ◽  
pp. 191-209
Author(s):  
Nandhini Kesavan ◽  
Raajan N. R.
Keyword(s):  
Gesture Recognition ◽  
Degrees Of Freedom ◽  
Research Work ◽  
Hand Tracking ◽  
Multidimensional Data ◽  
Human Hand ◽  
Challenging Problem ◽  
Hand Gestures ◽  
The Commons ◽  
Video Input

The main objective of gesture recognition is to promote the technology behind the automation of registered gesture with a fusion of multidimensional data in a versatile manner. To achieve this goal, computers should be able to visually recognize hand gestures from video input. However, vision-based hand tracking and gesture recognition is an extremely challenging problem due to the complexity of hand gestures, which are rich in diversities due to high degrees of freedom involved by the human hand. This would make the world a better place with for the commons not only to live in, but also to communicate with ease. This research work would serve as a pharos to researchers in the field of smart vision and would immensely help the society in a versatile manner.

Human Hand Motion Parametrization for Dactilemes Modeling

2011 ◽  
Vol 43 (12) ◽  
pp. 1-11 ◽  
Author(s):  
Yuriy G. Krivonos ◽  
Yuriy V. Krak ◽  
Yulia V. Barchukova ◽  
Bogdan A. Trotsenko
Keyword(s):  
Human Hand ◽  
Hand Motion

scholarly journals Comparison of Kinect and Leap Motion for Intraoperative Image Interaction

2020 ◽  
pp. 155335062094720
Author(s):  
Yuanyuan Feng ◽  
Uchenna A. Uchidiuno ◽  
Hamid R. Zahiri ◽  
Ivan George ◽  
Adrian E. Park ◽  
...  
Keyword(s):  
Intraoperative Imaging ◽  
Microsoft Kinect ◽  
Body Motion ◽  
Hand Tracking ◽  
Task Completion ◽  
Leap Motion ◽  
Hand Motion ◽  
Interaction Mechanisms ◽  
Touchless Interaction ◽  
Similar Accuracy

Background. Touchless interaction devices have increasingly garnered attention for intraoperative imaging interaction, but there are limited recommendations on which touchless interaction mechanisms should be implemented in the operating room. The objective of this study was to evaluate the efficiency, accuracy, and satisfaction of 2 current touchless interaction mechanisms—hand motion and body motion for intraoperative image interaction. Methods. We used the TedCas plugin for ClearCanvas DICOM viewer to display and manipulate CT images. Ten surgeons performed 5 image interaction tasks—step-through, pan, zoom, circle measure, and line measure—on the 3 input interaction devices—the Microsoft Kinect, the Leap Motion, and a mouse. Results. The Kinect shared similar accuracy with the Leap Motion for most of the tasks. But it had an increased error rate in the step-through task. The Leap Motion led to shorter task completion time than the Kinect and was preferred by the surgeons, especially for the measure tasks. Discussion. Our study suggests that hand tracking devices, such as the Leap Motion, should be used for intraoperative imagining manipulation tasks that require high precision.

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