Computer Vision Techniques for Hand Gesture Recognition: Survey

Fadel, Noor; Kareem, Emad I Abdul

doi:10.1007/978-3-031-35442-7_4

Cited by 3 publications

(2 citation statements)

References 77 publications

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“…Hand gesture recognition is often divided into two categories [4,[15][16][17][18][19][20][21][22][23]: (1) Rule-based techniques, which include a set of rules that are manually configured among feature inputs. The rule that balances the input is output as the gesture after a set of features are retrieved and matched to the encoded rules.…”

Section: State Of the Artmentioning

confidence: 99%

“…Recognizers primarily function to classify testing gestures into their respective training classes. In order for the recognizer to find the corresponding trained gesture class, it must first rank the classes according to the available testing gesture [17,66,67]. The NHMMbased gesture recognition paradigm's final parameters are derived using the neutrosophic Baum-Welch recursive approach.…”

Section: B Recognition Phasementioning

confidence: 99%

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Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Al-Saidi,

Ballagi,

Hassen

et al. 2024

Computers

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In recent years, Sign Language Recognition (SLR) has become an additional topic of discussion in the human–computer interface (HCI) field. The most significant difficulty confronting SLR recognition is finding algorithms that will scale effectively with a growing vocabulary size and a limited supply of training data for signer-independent applications. Due to its sensitivity to shape information, automated SLR based on hidden Markov models (HMMs) cannot characterize the confusing distributions of the observations in gesture features with sufficiently precise parameters. In order to simulate uncertainty in hypothesis spaces, many scholars provide an extension of the HMMs, utilizing higher-order fuzzy sets to generate interval-type-2 fuzzy HMMs. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic sets are used in this work to deal with indeterminacy in a practical SLR setting. Existing interval-type-2 fuzzy HMMs cannot consider uncertain information that includes indeterminacy. However, the neutrosophic hidden Markov model successfully identifies the best route between states when there is vagueness. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic three membership functions (truth, indeterminate, and falsity grades) provide more layers of autonomy for assessing HMM’s uncertainty. This approach could be helpful for an extensive vocabulary and hence seeks to solve the scalability issue. In addition, it may function independently of the signer, without needing data gloves or any other input devices. The experimental results demonstrate that the neutrosophic HMM is nearly as computationally difficult as the fuzzy HMM but has a similar performance and is more robust to gesture variations.

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Section: State Of the Artmentioning

confidence: 99%

Section: B Recognition Phasementioning

confidence: 99%

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Al-Saidi,

Ballagi,

Hassen

et al. 2024

Computers

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Validation of a Nondestructive Measurement System for Evaluating Surface Charge Distribution of Nonwoven Electret Fabrics

Eguchi,

Yien Lee,

Sato

et al. 2024

IEEE Access

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Nonwoven electret fabrics significantly improve the performance of filters used in various commercial products. In this study, we employed a no-contact, high-spatial-resolution surface potential distribution measurement system to verify the accuracy of evaluation methods for the surface charge distribution of nonwoven electret fabrics. Nonwoven electret fabric samples were fabricated through corona discharging, and their surface potential distributions were measured nondestructively. Thereafter, positively and negatively charged toners were dispersed to visualize the surface charge polarity of the sample. The results obtained using two measurement methods were analyzed through pixel-based analysis and higherorder local autocorrelation (HLAC) feature extraction. The results of the pixel-based analysis aligned with the charge distribution by 70-80 %, and the HLAC feature-based analysis indicated a high correlation between the overall surface charge distribution and the gray values of the images obtained using both measurement methods. These findings indicate that the proposed surface potential measurement system can effectively evaluate the surface charge distribution of nonwoven electret fabrics.

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Uncertainty-Aware Hand Gesture Recognition Based on Fmcw Mimo Radar and Convolutional Neural Network

Trinh,

2024

Preprint

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Computer Vision Techniques for Hand Gesture Recognition: Survey

Cited by 3 publications

References 77 publications

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Validation of a Nondestructive Measurement System for Evaluating Surface Charge Distribution of Nonwoven Electret Fabrics

Uncertainty-Aware Hand Gesture Recognition Based on Fmcw Mimo Radar and Convolutional Neural Network

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