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An Intelligent Heuristic Manta-Ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image Recognition

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Big Data Mining and Analytics 2023年第3期6卷 321-335页

作者： Seetharam Khetavath Navalpur Chinnappan Sendhilkumar Pandurangan Mukunthan Selvaganesan Jana Lakshmanan Malliga Subburayalu Gopalakrishnan Sankuru Ravi Chand Yousef Farhaoui Department of Electronics and Communication Engineering Chaitanya(Deemed to be University)Warangal 506001India. Department of Electronics and Communication Engineering Sri Indu College of Engineering&TechnologySherigudaHyderabad 501510India. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India. Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women(Autonomous)Telangana 500100India. Department of Electronics and Communication Engineering Nalla Narasimha Reddy Education Society’s Group of Institutions-Integrated CampusHyderabad 500088India. STI Laboratory the IDMS TeamFaculty of Sciences and TechniquesMoulay Ismail University of MeknèsErrachidia 52000Morocco.

The development of hand gesture recognition systems has gained more attention in recent days,due to its support of modern human-computer ***,sign language recognition is mainly developed for enabling communication between deaf and dumb *** conventional works,various image processing techniques like segmentation,optimization,and classification are deployed for hand gesture ***,it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption,increased false positives,error rate,and misclassification ***,this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing *** image segmentation,skin color detection and morphological operations are performed for accurately segmenting the hand gesture ***,the Heuristic Manta-ray Foraging Optimization(HMFO)technique is employed for optimally selecting the features by computing the best fitness ***,the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error ***,an Adaptive Extreme Learning Machine(AELM)based classification technique is employed for predicting the recognition *** results validation,various evaluation measures have been used to compare the proposed model’s performance with other classification approaches.

关键词： hand gesture recognition skin color detection morphological operations Multifaceted Feature Extraction(MFE)model Heuristic Manta-ray Foraging Optimization(HMFO) Adaptive Extreme Learning Machine(AELM)

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A Naturalness-Preserved Low-Light Enhancement Algorithm for Intelligent Analysis

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Chinese Journal of Electronics 2019年第2期28卷 316-324页

作者： ZHUO Li HU Xiaochen LI Jiafeng ZHANG Jing LI Xiaoguang Signal & Information Processing Laboratory Beijing University of Technology Collaborative Innovation Center of Electric Vehicles

Images/videos captured in low-light conditions often present low luminance and *** the existing low-light enhancement algorithms can improve the subjective perception, color distortion and over-enhancement are extremely obvious, which will disturb the subsequent intelligent analysis. Therefore,a naturalness-preserved low-light enhancement algorithm for intelligent analysis is proposed in this paper. An enhancement model is established in RGB color *** of colorChecker color chart are captured under a series of light conditions. To preserve the naturalness,the factors of the proposed enhancement model are estimated by the images captured in practical illumination environment. Experimental results demonstrate that the proposed algorithm can produce natural enhanced results and improve the performance of vehicle license plate localization and skin color detection compared to the existing algorithms. Furthermore, the proposed algorithm can process the 720p videos at the speed of 28.3 fps on average.

关键词： Low-light enhancement Intelligent analysis Naturalness-preserved Vehicle license plate localizations skin color detection

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