The authors develop an advanced hand motion recognition system for virtual reality applications u... more The authors develop an advanced hand motion recognition system for virtual reality applications using a well defined stochastic mathematical approach. Hand gesture is a natural way of interaction with a computer by interpreting the primitive characteristics of gesture movement to the system. This concerns three basic issues: (1) there is no physical contact between the user and the system, (2) the rotation of the hand gesture can be determined by the geometric features, and the model parameter must be optimized to improve measurement of performance. A comparative analysis of other classification techniques used in hand gesture recognition is carried out on the proposed work hybrid with the bio-inspired metaheuristic approach, namely the cuckoo search algorithm, for reducing the complex trajectory in the hidden Markov model (HMM) model. An experimental result is as to how to validate the HMM model, based on the cost value of the optimizer, in order to improve the performance measures of the system.
Nowadays, Fingerprint recognition is the one of the authentication used for security applications... more Nowadays, Fingerprint recognition is the one of the authentication used for security applications. It provides hope for the society in reliable authentic biometric systems. Fingerprint technology emerges in various sectors such as government, organizations, libraries, universities, banks etc. It is widely used for biometric systems other than Iris, Face, Hand, Voice and Signature because of its uniqueness and distinctness. Traditional methods are not effectively used for analyzing the texture feature of finger print than neural network classifier. Fingerprint recognition will be identified and classified with the help of Euclidean distance and NN classifier for better accuracy has proposed in this paper. It uses certain techniques in preprocessing the image such as Histogram equalization and Fast Fourier transform. The performance of the proposed approach has significant result than the existing techniques used in the finger print recognition system.
Hand gestures and Deep Learning Strategies can be used to control a virtual robotic arm for real-... more Hand gestures and Deep Learning Strategies can be used to control a virtual robotic arm for real-time applications. A robotic arm which is portable to carry various places and which can be easily programmed to do any work of a hand and is controlled by using deep learning techniques. Deep hand is a combination of both virtual reality and deep learning techniques. It estimated the active spatio-temporal feature and the corresponding pose parameter for various hand movements, to determine the unknown pose parameter of hand gestures by using various deep learning algorithms. A novel framework for hand gestures has been made to estimate by using a deep convolution neural network (CNN) and a deep belief network (DBN). A comparison in terms of accuracy and recognition rate has been drawn. This helps in analyzing the movement of a hand and its fingers which can be made to control a robotic arm with high recognition rate and less error rate.
Motion recognition is a topic in software engineering and dialect innovation with a goal of inter... more Motion recognition is a topic in software engineering and dialect innovation with a goal of interpreting human signals through mathematical algorithm. Hand gesture is a strategy for nonverbal communication for individuals as it expresses more liberally than body parts. Hand gesture acknowledgment has more prominent significance in planning a proficient human computer interaction framework, utilizing signals as a characteristic interface favorable to circumstance of movements. Regardless, the distinguishing proof and acknowledgment of posture, gait, proxemics and human behaviors is furthermore the subject of motion to appreciate human nonverbal communication, thus building a richer bridge between machines and humans than primitive text user interfaces or even graphical user interfaces, which still limits the majority of input to electronics gadget. In this paper, a study on various motion recognition methodologies is given specific accentuation on available motions. A survey on hand posture and gesture is clarified with a detailed comparative analysis of hidden Markov model approach with other classifier techniques. Difficulties and future investigation bearing are also examined.
HCI Virtual reality 1-D HMM Baum-Welch algorithm Viterbi algorithm ABC algorithm Cambridge hand g... more HCI Virtual reality 1-D HMM Baum-Welch algorithm Viterbi algorithm ABC algorithm Cambridge hand gesture A B S T R A C T Hand gestures are extensively used to communicate based on non-verbal interaction with computers. This mode of communication is made possible by implementing machine learning algorithms for pattern recognition. A stochastic mathematical approach is used to interpret the hand gesture pattern for classification. In this work, a predominant method is used by 1-D hidden Markov model (1-D HMM) for classifying the patterns and to measure its performance. During training phase, 1-D HMM is used to predict its next state sequence of hand gestures using dynamic programming methods such as Baum-Welch algorithm and Viterbi algorithm. However, dynamic programming based prediction methodologies are complex. To enhance the performance of 1-D HMM model, its parameter and observation state sequence must be optimized using bio-inspired heuristic approaches. In this work, Artificial Bee Colony (ABC) algorithm is used for optimization. A hybrid 1-D HMM model with ABC optimization has been proposed which has yielded a better performance metrics like recognition rate and error rate for Cambridge hand gesture dataset.
The role of gesture recognition is significant in areas like human-computer interaction, sign lan... more The role of gesture recognition is significant in areas like human-computer interaction, sign language, virtual reality, machine vision, etc. Among various gestures of the human body, hand gestures play a major role to communicate nonverbally with the computer. As the hand gesture is a continuous pattern with respect to time, the hidden Markov model (HMM) is found to be the most suitable pattern recognition tool, which can be modeled using the hand gesture parameters. The HMM considers the speeded up robust feature features of hand gesture and uses them to train and test the system. Conventionally, the Viterbi algorithm has been used for training process in HMM by discovering the shortest decoded path in the state diagram. The recursiveness of the Viterbi algorithm leads to computational complexity during the execution process. In order to reduce the complexity, the state sequence analysis approach is proposed for training the hand gesture model, which provides a better recognition rate and accuracy than that of the Viterbi algorithm. The performance of the proposed approach is explored in the context of pattern recognition with the Cambridge hand gesture data set.
Over the past few decades due to the advancement of technology, biometrics has evolved into a key... more Over the past few decades due to the advancement of technology, biometrics has evolved into a key factor of security for societal needs. Biometrics started as a basic ID verification system and has evolved into a major factor of authentication by taking several biological parameters as references. There are certain issues that researchers have yet been facing against hack or overriding. This chapter covers a comparative study of different biometrics and its methods used for safe authentication that improves security management for complicated scenarios.
Hand gesture recognition is the temporal pattern analysis with mathematical interpretation. It pr... more Hand gesture recognition is the temporal pattern analysis with mathematical interpretation. It provides the means for the non-verbal communication among the people, more natural and powerful means of human-computer interaction (HCI) for the virtual reality application. The development of human-computer stochastic processes has led to a 1-D hidden Markov models (1DHMMs) and training algorithms to find the high recognition rate and low computational complexity. Due to their dimensionality and computational efficiency, Pseudo 2-D HMMs (P2DHMMs) are often favored for a flexible way of presenting events with temporal and dynamic variations. Both 1-D HMM and 2-D HMM are present in hand gestures, which are of increasing interest in the research of hand gesture recognition (HGR). The main issue of 1-D HMM is the fact that the recursiveness in the forward and backward procedures typically multiply probability values between themselves. Hence, this product quickly tends to zero and goes beyond any machine storage capabilities. This work presents an application of Pseudo 2-D HMM to classify the hand gestures from measured values of an accelerating image. Comparing an experimental result between 1-D HMM and Pseudo 2-D HMM with respect to recognition rate and accuracy, it shows a prominent result for the proposed approach.
Hand motion recognition is an interesting field in the development of virtual reality application... more Hand motion recognition is an interesting field in the development of virtual reality applications through the human-computer interface. The stochastic mathematical model hidden Markov model (HMM) is used in this work. There are numerous parametric efforts in HMM for temporal pattern recognition. To overcome the recursiveness in the forward and backward procedures, dimensionality and storage problem in Markov model, 2-D HMM has been used. The experimental results show the comparison of 2-D HMM with 1-D HMM in terms of performance measures.
images is one of the critical issues because these images contain different types of random noise... more images is one of the critical issues because these images contain different types of random noises and attenuation artifacts. This paper proposes an automatic morphological segmentation and region growing method to change the representation of an image into something that is more meaningful and easier to analyze. There are several methods that intend to perform segmentation, but it is difficult to adapt easily and diagnose accurately. To resolve this problem, this paper aims to present an adaptable automatic morphological segmentation and region growing method that can be applied to any type of medical image which is exactly diagnosed even with the small changes that occur in the image. This proposed method is based in a model of morph function which applies the morphological operator to a gray scale image. Morphological segment technique is used to segment the image and selecting the specific image objects, thinning the object to diagnose the region. After using a morphological operation to expose the basic elements within an image, it is often useful to extract and analyze specific information about those image elements. The region grow function performs region growing for a given region within an N-dimensional array by finding all pixels within the array that are connected to neighboring region pixels and that fall within provided constraints. This technique was applied to segment the medical image, diagnosing the interested object from the grown region in all medical images.
The paper discussed here deals with inducing cartoon like features to an input video using DSP pr... more The paper discussed here deals with inducing cartoon like features to an input video using DSP processor (Texas Instrument product, TMS3206416). The basic process involved is taking input, a color video which is to be converted into a cartoon like video in DSP board can be displayed on VM3224K2 Daughter kit (LCD). VM3224Daughter Kit is embedded with DSK6416 Kit to display the output video. The conversion process is carried out in the YUV colour format. Converting in the YUV format is advantageous because applying thresholding technique to the intensity component (Y') of the video can render a cartoon like appearance to the video as the YUV colour model is based on human perception. Only the intensity component of the video inYUV format is modified as the YUV colour model allows apparent changes in the colour perception without actually altering the chrominance component of the video. Suitable thresholding techniques; single level or multi level thresholding technique can be used to get the desired effect. YUV colour model has three components namely Y, U and V. 'Y' is the intensity component whereas 'U' and 'V' are chrominance components. As this colour model facilitates apparent change in colour by simply varying the intensity component it is a very simple and intuitive technique to convert an input video to a cartoon like video. In order to observe the quality and suppleness of the algorithm various simulations in image level using different techniques has been compared with the proposed algorithm in MATLAB software. The proposed algorithm namely thresholding technique in YUV format were well implemented practically on our hardware (TMS6416 kit). Implementation of algorithm has been carried out in Cprogramming language using CCstudio v3.1 and various simulations have been carried out in MATLAB.
The paper discussed here deals with inducing cartoon like features to an input video using DSP pr... more The paper discussed here deals with inducing cartoon like features to an input video using DSP processor (Texas Instrument product, TMS3206416). The basic process involved is taking input, a color video which is to be converted into a cartoon like video in DSP board can be displayed on VM3224K2 Daughter kit (LCD). VM3224Daughter Kit is embedded with DSK6416 Kit to display the output video. The conversion process is carried out in the YUV colour format. Converting in the YUV format is advantageous because applying thresholding technique to the intensity component (Y') of the video can render a cartoon like appearance to the video as the YUV colour model is based on human perception. Only the intensity component of the video inYUV format is modified as the YUV colour model allows apparent changes in the colour perception without actually altering the chrominance component of the video. Suitable thresholding techniques; single level or multi level thresholding technique can be used to get the desired effect. YUV colour model has three components namely Y, U and V. 'Y' is the intensity component whereas 'U' and 'V' are chrominance components. As this colour model facilitates apparent change in colour by simply varying the intensity component it is a very simple and intuitive technique to convert an input video to a cartoon like video. In order to observe the quality and suppleness of the algorithm various simulations in image level using different techniques has been compared with the proposed algorithm in MATLAB software. The proposed algorithm namely thresholding technique in YUV format were well implemented practically on our hardware (TMS6416 kit). Implementation of algorithm has been carried out in C-programming language using CCstudio v3.1 and various simulations have been carried out in MATLAB.
The authors develop an advanced hand motion recognition system for virtual reality applications u... more The authors develop an advanced hand motion recognition system for virtual reality applications using a well defined stochastic mathematical approach. Hand gesture is a natural way of interaction with a computer by interpreting the primitive characteristics of gesture movement to the system. This concerns three basic issues: (1) there is no physical contact between the user and the system, (2) the rotation of the hand gesture can be determined by the geometric features, and the model parameter must be optimized to improve measurement of performance. A comparative analysis of other classification techniques used in hand gesture recognition is carried out on the proposed work hybrid with the bio-inspired metaheuristic approach, namely the cuckoo search algorithm, for reducing the complex trajectory in the hidden Markov model (HMM) model. An experimental result is as to how to validate the HMM model, based on the cost value of the optimizer, in order to improve the performance measures of the system.
Nowadays, Fingerprint recognition is the one of the authentication used for security applications... more Nowadays, Fingerprint recognition is the one of the authentication used for security applications. It provides hope for the society in reliable authentic biometric systems. Fingerprint technology emerges in various sectors such as government, organizations, libraries, universities, banks etc. It is widely used for biometric systems other than Iris, Face, Hand, Voice and Signature because of its uniqueness and distinctness. Traditional methods are not effectively used for analyzing the texture feature of finger print than neural network classifier. Fingerprint recognition will be identified and classified with the help of Euclidean distance and NN classifier for better accuracy has proposed in this paper. It uses certain techniques in preprocessing the image such as Histogram equalization and Fast Fourier transform. The performance of the proposed approach has significant result than the existing techniques used in the finger print recognition system.
Hand gestures and Deep Learning Strategies can be used to control a virtual robotic arm for real-... more Hand gestures and Deep Learning Strategies can be used to control a virtual robotic arm for real-time applications. A robotic arm which is portable to carry various places and which can be easily programmed to do any work of a hand and is controlled by using deep learning techniques. Deep hand is a combination of both virtual reality and deep learning techniques. It estimated the active spatio-temporal feature and the corresponding pose parameter for various hand movements, to determine the unknown pose parameter of hand gestures by using various deep learning algorithms. A novel framework for hand gestures has been made to estimate by using a deep convolution neural network (CNN) and a deep belief network (DBN). A comparison in terms of accuracy and recognition rate has been drawn. This helps in analyzing the movement of a hand and its fingers which can be made to control a robotic arm with high recognition rate and less error rate.
Motion recognition is a topic in software engineering and dialect innovation with a goal of inter... more Motion recognition is a topic in software engineering and dialect innovation with a goal of interpreting human signals through mathematical algorithm. Hand gesture is a strategy for nonverbal communication for individuals as it expresses more liberally than body parts. Hand gesture acknowledgment has more prominent significance in planning a proficient human computer interaction framework, utilizing signals as a characteristic interface favorable to circumstance of movements. Regardless, the distinguishing proof and acknowledgment of posture, gait, proxemics and human behaviors is furthermore the subject of motion to appreciate human nonverbal communication, thus building a richer bridge between machines and humans than primitive text user interfaces or even graphical user interfaces, which still limits the majority of input to electronics gadget. In this paper, a study on various motion recognition methodologies is given specific accentuation on available motions. A survey on hand posture and gesture is clarified with a detailed comparative analysis of hidden Markov model approach with other classifier techniques. Difficulties and future investigation bearing are also examined.
HCI Virtual reality 1-D HMM Baum-Welch algorithm Viterbi algorithm ABC algorithm Cambridge hand g... more HCI Virtual reality 1-D HMM Baum-Welch algorithm Viterbi algorithm ABC algorithm Cambridge hand gesture A B S T R A C T Hand gestures are extensively used to communicate based on non-verbal interaction with computers. This mode of communication is made possible by implementing machine learning algorithms for pattern recognition. A stochastic mathematical approach is used to interpret the hand gesture pattern for classification. In this work, a predominant method is used by 1-D hidden Markov model (1-D HMM) for classifying the patterns and to measure its performance. During training phase, 1-D HMM is used to predict its next state sequence of hand gestures using dynamic programming methods such as Baum-Welch algorithm and Viterbi algorithm. However, dynamic programming based prediction methodologies are complex. To enhance the performance of 1-D HMM model, its parameter and observation state sequence must be optimized using bio-inspired heuristic approaches. In this work, Artificial Bee Colony (ABC) algorithm is used for optimization. A hybrid 1-D HMM model with ABC optimization has been proposed which has yielded a better performance metrics like recognition rate and error rate for Cambridge hand gesture dataset.
The role of gesture recognition is significant in areas like human-computer interaction, sign lan... more The role of gesture recognition is significant in areas like human-computer interaction, sign language, virtual reality, machine vision, etc. Among various gestures of the human body, hand gestures play a major role to communicate nonverbally with the computer. As the hand gesture is a continuous pattern with respect to time, the hidden Markov model (HMM) is found to be the most suitable pattern recognition tool, which can be modeled using the hand gesture parameters. The HMM considers the speeded up robust feature features of hand gesture and uses them to train and test the system. Conventionally, the Viterbi algorithm has been used for training process in HMM by discovering the shortest decoded path in the state diagram. The recursiveness of the Viterbi algorithm leads to computational complexity during the execution process. In order to reduce the complexity, the state sequence analysis approach is proposed for training the hand gesture model, which provides a better recognition rate and accuracy than that of the Viterbi algorithm. The performance of the proposed approach is explored in the context of pattern recognition with the Cambridge hand gesture data set.
Over the past few decades due to the advancement of technology, biometrics has evolved into a key... more Over the past few decades due to the advancement of technology, biometrics has evolved into a key factor of security for societal needs. Biometrics started as a basic ID verification system and has evolved into a major factor of authentication by taking several biological parameters as references. There are certain issues that researchers have yet been facing against hack or overriding. This chapter covers a comparative study of different biometrics and its methods used for safe authentication that improves security management for complicated scenarios.
Hand gesture recognition is the temporal pattern analysis with mathematical interpretation. It pr... more Hand gesture recognition is the temporal pattern analysis with mathematical interpretation. It provides the means for the non-verbal communication among the people, more natural and powerful means of human-computer interaction (HCI) for the virtual reality application. The development of human-computer stochastic processes has led to a 1-D hidden Markov models (1DHMMs) and training algorithms to find the high recognition rate and low computational complexity. Due to their dimensionality and computational efficiency, Pseudo 2-D HMMs (P2DHMMs) are often favored for a flexible way of presenting events with temporal and dynamic variations. Both 1-D HMM and 2-D HMM are present in hand gestures, which are of increasing interest in the research of hand gesture recognition (HGR). The main issue of 1-D HMM is the fact that the recursiveness in the forward and backward procedures typically multiply probability values between themselves. Hence, this product quickly tends to zero and goes beyond any machine storage capabilities. This work presents an application of Pseudo 2-D HMM to classify the hand gestures from measured values of an accelerating image. Comparing an experimental result between 1-D HMM and Pseudo 2-D HMM with respect to recognition rate and accuracy, it shows a prominent result for the proposed approach.
Hand motion recognition is an interesting field in the development of virtual reality application... more Hand motion recognition is an interesting field in the development of virtual reality applications through the human-computer interface. The stochastic mathematical model hidden Markov model (HMM) is used in this work. There are numerous parametric efforts in HMM for temporal pattern recognition. To overcome the recursiveness in the forward and backward procedures, dimensionality and storage problem in Markov model, 2-D HMM has been used. The experimental results show the comparison of 2-D HMM with 1-D HMM in terms of performance measures.
images is one of the critical issues because these images contain different types of random noise... more images is one of the critical issues because these images contain different types of random noises and attenuation artifacts. This paper proposes an automatic morphological segmentation and region growing method to change the representation of an image into something that is more meaningful and easier to analyze. There are several methods that intend to perform segmentation, but it is difficult to adapt easily and diagnose accurately. To resolve this problem, this paper aims to present an adaptable automatic morphological segmentation and region growing method that can be applied to any type of medical image which is exactly diagnosed even with the small changes that occur in the image. This proposed method is based in a model of morph function which applies the morphological operator to a gray scale image. Morphological segment technique is used to segment the image and selecting the specific image objects, thinning the object to diagnose the region. After using a morphological operation to expose the basic elements within an image, it is often useful to extract and analyze specific information about those image elements. The region grow function performs region growing for a given region within an N-dimensional array by finding all pixels within the array that are connected to neighboring region pixels and that fall within provided constraints. This technique was applied to segment the medical image, diagnosing the interested object from the grown region in all medical images.
The paper discussed here deals with inducing cartoon like features to an input video using DSP pr... more The paper discussed here deals with inducing cartoon like features to an input video using DSP processor (Texas Instrument product, TMS3206416). The basic process involved is taking input, a color video which is to be converted into a cartoon like video in DSP board can be displayed on VM3224K2 Daughter kit (LCD). VM3224Daughter Kit is embedded with DSK6416 Kit to display the output video. The conversion process is carried out in the YUV colour format. Converting in the YUV format is advantageous because applying thresholding technique to the intensity component (Y') of the video can render a cartoon like appearance to the video as the YUV colour model is based on human perception. Only the intensity component of the video inYUV format is modified as the YUV colour model allows apparent changes in the colour perception without actually altering the chrominance component of the video. Suitable thresholding techniques; single level or multi level thresholding technique can be used to get the desired effect. YUV colour model has three components namely Y, U and V. 'Y' is the intensity component whereas 'U' and 'V' are chrominance components. As this colour model facilitates apparent change in colour by simply varying the intensity component it is a very simple and intuitive technique to convert an input video to a cartoon like video. In order to observe the quality and suppleness of the algorithm various simulations in image level using different techniques has been compared with the proposed algorithm in MATLAB software. The proposed algorithm namely thresholding technique in YUV format were well implemented practically on our hardware (TMS6416 kit). Implementation of algorithm has been carried out in Cprogramming language using CCstudio v3.1 and various simulations have been carried out in MATLAB.
The paper discussed here deals with inducing cartoon like features to an input video using DSP pr... more The paper discussed here deals with inducing cartoon like features to an input video using DSP processor (Texas Instrument product, TMS3206416). The basic process involved is taking input, a color video which is to be converted into a cartoon like video in DSP board can be displayed on VM3224K2 Daughter kit (LCD). VM3224Daughter Kit is embedded with DSK6416 Kit to display the output video. The conversion process is carried out in the YUV colour format. Converting in the YUV format is advantageous because applying thresholding technique to the intensity component (Y') of the video can render a cartoon like appearance to the video as the YUV colour model is based on human perception. Only the intensity component of the video inYUV format is modified as the YUV colour model allows apparent changes in the colour perception without actually altering the chrominance component of the video. Suitable thresholding techniques; single level or multi level thresholding technique can be used to get the desired effect. YUV colour model has three components namely Y, U and V. 'Y' is the intensity component whereas 'U' and 'V' are chrominance components. As this colour model facilitates apparent change in colour by simply varying the intensity component it is a very simple and intuitive technique to convert an input video to a cartoon like video. In order to observe the quality and suppleness of the algorithm various simulations in image level using different techniques has been compared with the proposed algorithm in MATLAB software. The proposed algorithm namely thresholding technique in YUV format were well implemented practically on our hardware (TMS6416 kit). Implementation of algorithm has been carried out in C-programming language using CCstudio v3.1 and various simulations have been carried out in MATLAB.
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Papers by Sagayam Martin