A framework for sign gesture recognition using improved genetic algorithm and adaptive filter

Kaluri, Rajesh; Reddy, Chandra

doi:10.1080/23311916.2016.1251730

Cited by 20 publications

(24 citation statements)

References 17 publications

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“…Our Proposed method is well suitable for gesture recognition compared to the conventional methods like neural networks [28] and support vector machine [8]. Previous frameworks [8,28] were not enough in removing noise from the input images, hence we have achieved better results for noise removal using Wiener Filter.…”

Section: Resultsmentioning

confidence: 88%

“…Previous frameworks [8,28] were not enough in removing noise from the input images, hence we have achieved better results for noise removal using Wiener Filter. We have used Adaptive Histogram technique for image segmentation and completed segmentation process in very less time.…”

Section: Resultsmentioning

confidence: 99%

“…We have used Adaptive Histogram technique for image segmentation and completed segmentation process in very less time. Our enhanced framework mainly attains the better recognition rate compared to the previous methods in [8,28].…”

Section: Resultsmentioning

confidence: 99%

“…The expanding enthusiasm for motion examination is halfway because of the advancement of minimal effort sensors, for which there are Software Development Kits that give the way to make applications with various levels of multifaceted nature [7,8]. Signal investigation is critical for various application fields, since it is worried with the communications amongst people, and amongst people and nature or machines [9].…”

Section: Introductionmentioning

confidence: 99%

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An Enhanced Framework for Sign Gesture Recognition using Hidden Markov Model and Adaptive Histogram Technique

Kaluri¹,

Reddy²

2017

IJIES

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Gesture based communication is the fundamental method for correspondence for those with hearing and vocal incapacities. Communication via gestures comprises of making shapes or developments with human hands as for the head or other body parts. In this paper, we propose a new framework for recognizing sign gestures by using Hidden Markov Model (HMM) and Histogram based methods. Initially, the noise of an image will be eliminated by Wiener Filter and the image will be segmented with the help of Histogram oriented methods -Adaptive Histogram technique and then features will be extracted. The extracted features will be given to the HMM for training and recognition of gestures. Our experimental results show a better performance in terms of recognizing gestures from a blurred image compared to the existing segmentation methods.

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Section: Resultsmentioning

confidence: 88%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Enhanced Framework for Sign Gesture Recognition using Hidden Markov Model and Adaptive Histogram Technique

Kaluri¹,

Reddy²

2017

IJIES

Self Cite

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“…A 2D Adaptive Grey Wolf Approach is suggested in this paper for efficient and secure medical image protection [53,54]. To enhance the efficiency of the 2D chaotic system, we propose a permutation and substitution method based on standard chaotic system.…”

Section: Basic Theory Of Chaotic Systemsmentioning

confidence: 99%

2D Chaotic Map Based on 2D Adaptive Grey Wolf Algorithm for Ultra Sound Medical Image Security

Koppu¹,

Madhu²

2017

IJIES

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Abstract:In this paper, we have proposed a chaos-based visual encryption method that can be applied to Ultra Sound Medical Images. We used adaptive Grey Wolf Optimization (GWO) to archive Ultra Sound Medical Image encryption. The robustness of the proposed image encryption are measured by various security attacks such as key sensitivity, histogram analysis, adjacent pixel autocorrelation, chi-square test, etc. Moreover, analytical outcomes are compared with the conventional algorithms like Genetic Algorithm (GA) and GWO. The experimental result shows that the proposed method is faster with low complexity.

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A review on computational methods based automated sign language recognition system for hearing and speech impaired community

Robert

Hemanth

2023

Concurrency and Computation

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Summary The recent advancements in computer vision and deep learning have led to promising progress in various motion detection and gesture recognition methods. Thriving efforts in the field of sign language recognition (SLR) during recent years led to interaction between humans and computer systems. Contributing a real‐time automated Sign Language recognition system will be a remarkable treasure for hearing and speech‐impaired people that will break the barriers of interaction with the real world. Albeit several research works are accomplished in sign language recognition, there is still a demand for developing a real‐time automated sign language recognition system. Compared to other methodologies, the techniques adopted may have advantages and disadvantages, and they vary from researcher to researcher. There are still some issues with employing these SLR models and procedures regularly, even though numerous research studies have been conducted to determine the most acceptable methods and models for sign language recognition. It gets more expensive and difficult in terms of resources when the developed automated SLR system becomes available as a product. Hence, for the welfare of the hearing and speech‐impaired community, the researchers are still endeavoring way to find a cost‐effective method. This work brings forth the challenges faced by scientists in developing a cost‐effective commercial prototype for the hearing and speech‐impaired community. This paper also explores and analyses various deep learning techniques and methods used in developing a sign language recognition system. The objective behind this work is to identify the best method which produces high accuracy in developing a cost‐effective sign language recognition system aiding communication between signers and non‐signers so that they can be a part of growing technology.

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A framework for sign gesture recognition using improved genetic algorithm and adaptive filter

Cited by 20 publications

References 17 publications

An Enhanced Framework for Sign Gesture Recognition using Hidden Markov Model and Adaptive Histogram Technique

An Enhanced Framework for Sign Gesture Recognition using Hidden Markov Model and Adaptive Histogram Technique

2D Chaotic Map Based on 2D Adaptive Grey Wolf Algorithm for Ultra Sound Medical Image Security

A review on computational methods based automated sign language recognition system for hearing and speech impaired community

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