Optimization of a HMM-Based Hand Gesture Recognition System Using a Hybrid Cuckoo Search Algorithm

Sagayam, K. Martin; Hemanth, D. Jude; Vasanth, X. Ajay; Henesy, Lawerence E.; Ho, Chiung Ching

doi:10.1007/978-3-319-77625-5_4

Cited by 10 publications

(4 citation statements)

References 34 publications

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“…In ref. [26], the authors used a well-defined stochastic mathematical technique to build a sophisticated hand motion detection system for use in VR applications. First, the user and system do not make physical contact; second, geometric factors may dictate the hand gesture's rotation; and third, to increase measurement performance, the model parameter must be adjusted.…”

Section: State Of the Artmentioning

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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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%

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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“…Cambridge Hand Gesture datasets [23,24] 2007 A dataset with 9 gestures, containing 900 videos of both static and dynamic gestures.…”

Section: Ms-asl [22] 2018mentioning

confidence: 99%

“…We studied three common classification methods suitable for the MediaPipe framework [18,23,24]: (1) the BP neural network approach, with the structure of the neural network model shown in Table 2; (2) the LSTM network, with the structure of the LSTM network model shown in Table 3; and (3) by calculating the distance between the sampling sequence and the template sequence. The commonly used method for calculating the time series distance is dynamic time warping (DTW).…”

Section: In-air Gesture Recognition Frameworkmentioning

confidence: 99%

Impact of In-Air Gestures on In-Car Task’s Diver Distraction

et al. 2023

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As in-vehicle information systems (IVIS) grow increasingly complex, the demand for innovative artificial intelligence-based interaction methods that enhance cybersecurity becomes more crucial. In-air gestures offer a promising solution due to their intuitiveness and individual uniqueness, potentially improving security in human–computer interactions. However, the impact of in-air gestures on driver distraction during in-vehicle tasks and the scarcity of skeleton-based in-air gesture recognition methods in IVIS remain largely unexplored. To address these challenges, we developed a skeleton-based framework specifically tailored for IVIS that recognizes in-air gestures, classifying them as static or dynamic. Our gesture model, tested on the large-scale AUTSL dataset, demonstrates accuracy comparable to state-of-the-art methods and increased efficiency on mobile devices. In comparative experiments between in-air gestures and touch interactions within a driving simulation environment, we established an evaluation system to assess the driver’s attention level during driving. Our findings indicate that in-air gestures provide a more efficient and less distracting interaction solution for IVIS in multi-goal driving environments, significantly improving driving performance by 65%. The proposed framework can serve as a valuable tool for designing future in-air gesture-based interfaces for IVIS, contributing to enhanced cybersecurity.

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“…Sathish and Elango also used a radiated source neural network with exponential CSA for the instinctive cataloging in MRI images [35]. Cuckoo search algorithm (CSA) has been also used along with other NIOAs known as the Hybrid Cuckoo search algorithm [36]. Recently in 2016, Choubey et al proposed a novel idea of developing a Hybrid CS-BFO algorithm for getting the optimized multilevel image threshold value for edge magnitude information [37].…”

Section: Related Workmentioning

confidence: 99%

Cuckoo Search Constrained Gamma Masking for MRI Image Detail Enhancement

Ojha¹,

Rai²,

Prakash³

et al. 2022

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Nature-inspired algorithms are widely applied in the arena of image enhancement for various optimization purposes. To address the optimization complexities in various image enhancement approaches, nature-inspired optimization algorithms play a vital role. Cuckoo search is one of the prominent nature-inspired performance algorithms that we employed in this work for the enhancement of magnetic resonance imaging (MRI). We proposed a wavelet-based masking technique employing a cuckoo search algorithm whose masking value is corrected by gamma function for the contrast enhancement of MRI images. The cuckoo search algorithm can inevitably fine-tune the relation of nest building using genetic operatives like adaptive cusp and alteration. The proposed contrast enhancement scheme is examined quantitatively for different types of MRI images. Extensive simulation results compared with quantitative values have revealed that the traditional nest building of cuckoo search optimization is improved by adaptive gamma correction. Comparative analysis with the existing works establishes the usefulness of the proposed methodology over the other standard approaches.

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Optimization of a HMM-Based Hand Gesture Recognition System Using a Hybrid Cuckoo Search Algorithm

Cited by 10 publications

References 34 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

Impact of In-Air Gestures on In-Car Task’s Diver Distraction

Cuckoo Search Constrained Gamma Masking for MRI Image Detail Enhancement

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