Fast marching method and modified features fusion in enhanced dynamic hand gesture segmentation and detection method under complicated background

Thabet, Eman; Khalid, Fatimah; Sulaiman, Puteri Suhaiza; Yaakob, Razali

doi:10.1007/s12652-017-0512-6

Cited by 18 publications

(3 citation statements)

References 28 publications

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“…Typically, a combination of various features such as skin color and motion features is applied to enhance segmentation accuracy. In 2018, Thabet et al [8] proposed a gesture segmentation method that fused four features: mixed skin color, hand motion, skin motion, and contour characteristics. However, the integration of multiple features to enhance segmentation accuracy compromised realtime performance.…”

Section: Dynamic Gesture Segmentationmentioning

confidence: 99%

Review of vision-based gesture recognition technology

bao,

dai,

2024

International Conference on Optics and Machine Vision (ICOMV 2024)

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With the emergence of deep learning methodologies, vision-based gesture recognition technology has continuously advanced. This paper primarily delves into four main stages of vision-based gesture recognition: gesture segmentation, gesture tracking, feature extraction, and gesture classification. It sequentially introduces pertinent techniques from representative literature spanning from 2018 to 2023. Based on this analysis, the current status of vision-based gesture recognition technology is examined, paving the way for predicting its future trends and developments.

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Section: Dynamic Gesture Segmentationmentioning

confidence: 99%

Review of vision-based gesture recognition technology

bao,

dai,

2024

International Conference on Optics and Machine Vision (ICOMV 2024)

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“…The system was trained using deep learning techniques that helped recognise designed gestures (Hu & Wang, 2020). Another detection method was introduced for segmenting the complete hand using a combination of four modified visual features segmentation procedures, including skin, motion, contour features, and the fast marching method (Thabet et al, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning based spatio‐temporal hand gesture recognition system in complex environment

2023

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Gesture recognition nowadays has grabbed the attention of researchers as they represent human behaviour in multiple practical ways. Amongst a variety of gestures available, hand gestures play an essential role in the field of human‐computer interaction when recognised efficiently in complex and dynamic environments. In this paper, we propose a dynamic hand gesture recognition system to recognise hand gestures appearing in different indoor and outdoor environments. Hand detection and tracking uses a two‐level system resulting in the formation of gesture trajectory in challenging conditions in which existing detection and tracking algorithms could not do so. A set of 45 features is provided as input to the various classification techniques. The redundancy problem has been reduced by selecting a set of optimum features using the analysis of variance method, which ranks the list of features. An incremental feature selection technique calculates recognition accuracy by selecting features according to rankings. This system provides an accuracy of 96.32% when used with machine learning and 97.5% when used with deep learning techniques. Recognition accuracy is calculated for various environments, including an extra hand, multiple persons in the video frame, and outdoor environment. All machine‐learning classifiers are combined using classifier combination to calculate the accuracy according to the majority‐voting rule. Based on the experimental results, it has been observed that deep learning provides better results compared to machine learning.

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“…By using red, green, and blue (RGB) vision cameras, contour features, skin, motion, even skin movement are collected to obtain a tri-dimensional model by designing a fast-marching method (Thabet et al , 2018). The growing neural gas algorithm constructing a graph is introduced to process depth RGB data based on topological features (Malgireddy et al , 2010).…”

Section: Related Workmentioning

confidence: 99%

Adaptive sensor fusion labeling framework for hand pose recognition in robot teleoperation

Liu

Zhang

et al. 2021

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Purpose The purpose of this paper is to mainly center on the touchless interaction between humans and robots in the real world. The accuracy of hand pose identification and stable operation in a non-stationary environment is the main challenge, especially in multiple sensors conditions. To guarantee the human-machine interaction system’s performance with a high recognition rate and lower computational time, an adaptive sensor fusion labeling framework should be considered in surgery robot teleoperation. Design/methodology/approach In this paper, a hand pose estimation model is proposed consisting of automatic labeling and classified based on a deep convolutional neural networks (DCNN) structure. Subsequently, an adaptive sensor fusion methodology is proposed for hand pose estimation with two leap motions. The sensor fusion system is implemented to process depth data and electromyography signals capturing from Myo Armband and leap motion, respectively. The developed adaptive methodology can perform stable and continuous hand position estimation even when a single sensor is unable to detect a hand. Findings The proposed adaptive sensor fusion method is verified with various experiments in six degrees of freedom in space. The results showed that the clustering model acquires the highest clustering accuracy (96.31%) than other methods, which can be regarded as real gestures. Moreover, the DCNN classifier gets the highest performance (88.47% accuracy and lowest computational time) than other methods. Originality/value This study can provide theoretical and engineering guidance for hand pose recognition in surgery robot teleoperation and design a new deep learning model for accuracy enhancement.

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Fast marching method and modified features fusion in enhanced dynamic hand gesture segmentation and detection method under complicated background

Cited by 18 publications

References 28 publications

Review of vision-based gesture recognition technology

Review of vision-based gesture recognition technology

Deep learning based spatio‐temporal hand gesture recognition system in complex environment

Adaptive sensor fusion labeling framework for hand pose recognition in robot teleoperation

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