This paper introduces a new concept for the establishment of human-robot symbiotic relationship. The
system is based on the implementation of knowledge-based image processing methodologies for model
based vision and intelligent task scheduling for an autonomous social robot. This paper aims to develop an
automatic translation of static gestures of alphabets and signs in American Sign Language (ASL), using
neural network with backpropagation algorithm. System deals with images of bare hands to achieve the
recognition task. For each individual sign 10 sample images have been considered, which means in
total300 samples have been processed. In order to compare between the training set of signs and the
considered sample images, are converted into feature vectors. Experimental results reveal that this can
recognize selected ASL signs (accuracy of 92.00%). Finally, the system has been implemented issuing hand
gesture commands for ASL to a robot car, named “Moto-robo”.
This paper introduces a new concept for the establishment of human-robot symbiotic relationship. The
system is based on the implementation of knowledge-based image processing methodologies for model
based vision and intelligent task scheduling for an autonomous social robot. This paper aims to develop an
automatic translation of static gestures of alphabets and signs in American Sign Language (ASL), using
neural network with backpropagation algorithm. System deals with images of bare hands to achieve the
recognition task. For each individual sign 10 sample images have been considered, which means in
total300 samples have been processed. In order to compare between the training set of signs and the
considered sample images, are converted into feature vectors. Experimental results reveal that this can
recognize selected ASL signs (accuracy of 92.00%). Finally, the system has been implemented issuing hand
gesture commands for ASL to a robot car, named “Moto-robo”.
HSV Brightness Factor Matching for Gesture Recognition SystemCSCJournals
The main goal of gesture recognition research is to establish a system which can identify specific human gestures and use these identified gestures to be carried out by the machine, In this paper, we introduce a new method for gesture recognition that based on computing the local brightness for each block of the gesture image, the gesture image is divided into 25x25 blocks each of 5x5 block size, and we calculated the local brightness of each block, so, each gesture produces 25x25 features value, our experimental shows that more that %60 of these features are zero value which leads to minimum storage space, this brightness value is calculated from the HSV (Hue, Saturation and Value) color model that used for segmentation operation, the recognition rate achieved is %91 using 36 training gestures and 24 different testing gestures. This Paper focuses on the hand gesture instead of the whole body movement since hands are the most flexible part of the body and can transfer the most meaning, we build a gesture recognition system that can communicate with the machine in natural way without any mechanical devices and without using the normal input devices which are the keyboard and mouse and the mathematical equations will be the translator between the gestures and the telerobotic.
A mediator person is required for communication between deaf person and a second person. But a
mediator should know the sign language used by deaf person. But this is also not possible always since there are
multiple sign languages for multiple languages. It is difficult for a deaf person to understand what a second
person speaks. And therefore deaf person should keep track of lip movements of second person in order to know
what he is speaking. But the lip movements do not give proper efficiency and accuracy since the facial
expressions and speech might not match. To overcome the above problems we have proposed a system, an
Android Application for recognizing sign language using hand gesture with the facility for user to define and
upload their own sign language into the system. The features of this system are the real time conversion of
gesture to text and speech. For two-way communication between deaf person and second person, the speech of
second person is converted into text. The processing steps include: gesture extraction, gesture matching and
conversion of text to speech and vice-versa. The system is not only useful for deaf community but can also be
used by common people who migrate to different regions and do not know local language.
PERFORMANCE EVALUATION OF STATISTICAL CLASSIFIERS USING INDIAN SIGN LANGUAGE ...IJCSEA Journal
Sign language is the key for communication between deaf people. The significance of sign language is accentuated by various research activities and the technical aspects will definitely improve the communication needs. General view based sign language recognition systems extract manual parameters by a single camera view because it seems to be user friendly and hardware complexity; however it needs a high accuracy classifier for classification and recognition purpose. The decision making of the system in this work employs Indian sign language datasets and the performance evaluation of the system is compared by deploying the K-NN, Naïve Bayes and PNN classifiers. Classification using an instance-based classifiercan be a simple matter of locating the instance space and labelling the unknown instance with the same class label as that of the located (known) neighbour. Classifier always tries to improve the classification rate by pushing classifiers into an optimised structure. In each hand posture, a measure of properties like area, mean intensity, centroid, perimeter and diameter are taken; the classifier then uses these properties to determine the sign in different angles. They estimate the probability that a sign belongs to each of the target classes that is fixed. The impact of such study may reflect the exploration for using such algorithms
in other similar applications such as text classification and the development of automated systems.
Novel Approach to Use HU Moments with Image Processing Techniques for Real Ti...CSCJournals
Sign language is the fundamental communication method among people who suffer from speech and hearing defects. The rest of the world doesn’t have a clear idea of sign language. “Sign Language Communicator” (SLC) is designed to solve the language barrier between the sign language users and the rest of the world. The main objective of this research is to provide a low cost affordable method of sign language interpretation. This system will also be very useful to the sign language learners as they can practice the sign language. During the research available human computer interaction techniques in posture recognition was tested and evaluated. A series of image processing techniques with Hu-moment classification was identified as the best approach. To improve the accuracy of the system, a new approach; height to width ratio filtration was implemented along with Hu-moments. System is able to recognize selected Sign Language signs with the accuracy of 84% without a controlled background with small light adjustments.
Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...IJERA Editor
Gesture recognition system received great attention in the recent few years because of its manifoldness applications and the ability to interact with machine efficiently through human computer interaction. Gesture is one of human body languages which are popularly used in our daily life. It is a communication system that consists of hand movements and facial expressions via communication by actions and sights. This research mainly focuses on the research of gesture extraction and finger segmentation in the gesture recognition. In this paper, we have used image analysis technologies to create an application by encoding in MATLAB program. We will use this application to segment and extract the finger from one specific gesture. This paper is aimed to give gesture recognition in different natural conditions like dark and glare condition, different distances condition and similar object condition then collect the results to calculate the successful extraction rate.
Hand gesture classification is popularly used in
wide applications like Human-Machine Interface, Virtual
Reality, Sign Language Recognition, Animations etc. The
classification accuracy of static gestures depends on the
technique used to extract the features as well as the classifier
used in the system. To achieve the invariance to illumination
against complex background, experimentation has been
carried out to generate a feature vector based on skin color
detection by fusing the Fourier descriptors of the image with
its geometrical features. Such feature vectors are then used in
Neural Network environment implementing Back
Propagation algorithm to classify the hand gestures. The set
of images for the hand gestures used in the proposed research
work are collected from the standard databases viz.
Sebastien Marcel Database, Cambridge Hand Gesture Data
set and NUS Hand Posture dataset. An average classification
accuracy of 95.25% has been observed which is on par with
that reported in the literature by the earlier researchers.
This paper introduces a new concept for the establishment of human-robot symbiotic relationship. The
system is based on the implementation of knowledge-based image processing methodologies for model
based vision and intelligent task scheduling for an autonomous social robot. This paper aims to develop an
automatic translation of static gestures of alphabets and signs in American Sign Language (ASL), using
neural network with backpropagation algorithm. System deals with images of bare hands to achieve the
recognition task. For each individual sign 10 sample images have been considered, which means in
total300 samples have been processed. In order to compare between the training set of signs and the
considered sample images, are converted into feature vectors. Experimental results reveal that this can
recognize selected ASL signs (accuracy of 92.00%). Finally, the system has been implemented issuing hand
gesture commands for ASL to a robot car, named “Moto-robo”.
HSV Brightness Factor Matching for Gesture Recognition SystemCSCJournals
The main goal of gesture recognition research is to establish a system which can identify specific human gestures and use these identified gestures to be carried out by the machine, In this paper, we introduce a new method for gesture recognition that based on computing the local brightness for each block of the gesture image, the gesture image is divided into 25x25 blocks each of 5x5 block size, and we calculated the local brightness of each block, so, each gesture produces 25x25 features value, our experimental shows that more that %60 of these features are zero value which leads to minimum storage space, this brightness value is calculated from the HSV (Hue, Saturation and Value) color model that used for segmentation operation, the recognition rate achieved is %91 using 36 training gestures and 24 different testing gestures. This Paper focuses on the hand gesture instead of the whole body movement since hands are the most flexible part of the body and can transfer the most meaning, we build a gesture recognition system that can communicate with the machine in natural way without any mechanical devices and without using the normal input devices which are the keyboard and mouse and the mathematical equations will be the translator between the gestures and the telerobotic.
A mediator person is required for communication between deaf person and a second person. But a
mediator should know the sign language used by deaf person. But this is also not possible always since there are
multiple sign languages for multiple languages. It is difficult for a deaf person to understand what a second
person speaks. And therefore deaf person should keep track of lip movements of second person in order to know
what he is speaking. But the lip movements do not give proper efficiency and accuracy since the facial
expressions and speech might not match. To overcome the above problems we have proposed a system, an
Android Application for recognizing sign language using hand gesture with the facility for user to define and
upload their own sign language into the system. The features of this system are the real time conversion of
gesture to text and speech. For two-way communication between deaf person and second person, the speech of
second person is converted into text. The processing steps include: gesture extraction, gesture matching and
conversion of text to speech and vice-versa. The system is not only useful for deaf community but can also be
used by common people who migrate to different regions and do not know local language.
PERFORMANCE EVALUATION OF STATISTICAL CLASSIFIERS USING INDIAN SIGN LANGUAGE ...IJCSEA Journal
Sign language is the key for communication between deaf people. The significance of sign language is accentuated by various research activities and the technical aspects will definitely improve the communication needs. General view based sign language recognition systems extract manual parameters by a single camera view because it seems to be user friendly and hardware complexity; however it needs a high accuracy classifier for classification and recognition purpose. The decision making of the system in this work employs Indian sign language datasets and the performance evaluation of the system is compared by deploying the K-NN, Naïve Bayes and PNN classifiers. Classification using an instance-based classifiercan be a simple matter of locating the instance space and labelling the unknown instance with the same class label as that of the located (known) neighbour. Classifier always tries to improve the classification rate by pushing classifiers into an optimised structure. In each hand posture, a measure of properties like area, mean intensity, centroid, perimeter and diameter are taken; the classifier then uses these properties to determine the sign in different angles. They estimate the probability that a sign belongs to each of the target classes that is fixed. The impact of such study may reflect the exploration for using such algorithms
in other similar applications such as text classification and the development of automated systems.
Novel Approach to Use HU Moments with Image Processing Techniques for Real Ti...CSCJournals
Sign language is the fundamental communication method among people who suffer from speech and hearing defects. The rest of the world doesn’t have a clear idea of sign language. “Sign Language Communicator” (SLC) is designed to solve the language barrier between the sign language users and the rest of the world. The main objective of this research is to provide a low cost affordable method of sign language interpretation. This system will also be very useful to the sign language learners as they can practice the sign language. During the research available human computer interaction techniques in posture recognition was tested and evaluated. A series of image processing techniques with Hu-moment classification was identified as the best approach. To improve the accuracy of the system, a new approach; height to width ratio filtration was implemented along with Hu-moments. System is able to recognize selected Sign Language signs with the accuracy of 84% without a controlled background with small light adjustments.
Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...IJERA Editor
Gesture recognition system received great attention in the recent few years because of its manifoldness applications and the ability to interact with machine efficiently through human computer interaction. Gesture is one of human body languages which are popularly used in our daily life. It is a communication system that consists of hand movements and facial expressions via communication by actions and sights. This research mainly focuses on the research of gesture extraction and finger segmentation in the gesture recognition. In this paper, we have used image analysis technologies to create an application by encoding in MATLAB program. We will use this application to segment and extract the finger from one specific gesture. This paper is aimed to give gesture recognition in different natural conditions like dark and glare condition, different distances condition and similar object condition then collect the results to calculate the successful extraction rate.
Hand gesture classification is popularly used in
wide applications like Human-Machine Interface, Virtual
Reality, Sign Language Recognition, Animations etc. The
classification accuracy of static gestures depends on the
technique used to extract the features as well as the classifier
used in the system. To achieve the invariance to illumination
against complex background, experimentation has been
carried out to generate a feature vector based on skin color
detection by fusing the Fourier descriptors of the image with
its geometrical features. Such feature vectors are then used in
Neural Network environment implementing Back
Propagation algorithm to classify the hand gestures. The set
of images for the hand gestures used in the proposed research
work are collected from the standard databases viz.
Sebastien Marcel Database, Cambridge Hand Gesture Data
set and NUS Hand Posture dataset. An average classification
accuracy of 95.25% has been observed which is on par with
that reported in the literature by the earlier researchers.
50Combining Color Spaces for Human Skin Detection in Color Images using Skin ...idescitation
Skin detection remains a challenging task over
several decades in spite of many techniques evolved. It is the
elementary step of most of the computer vision applications
like face recognition, human computer interaction, etc. It
depends on the suitability of color space chosen, skin modeling
and classification of skin and non-skin pixels under varying
illumination conditions. This paper presents a symbolic
interpretation on the performance of the color spaces using
piecewise linear decision boundary classifier in color images
to find the winning color space (s). The whole task is divided
into three processes: analysis of color spaces individually;
analysis of the combination of two color spaces; and finally
making a comparative analysis among the results obtained by
the above two processes. For performing the fair evaluation,
the whole experiment is tested over commonly used databases.
Based on the success rate, false positive and false negative of
each color spaces, the winner(s) has been chosen among single
and the combination of color spaces.
Recognition of Facial Emotions Based on Sparse CodingIJERA Editor
This paper deals with acknowledgment of characteristic feelings from human countenances is a fascinating subject with an extensive variety of potential applications like human-PC communication, robotized mentoring frameworks, picture and video recovery, brilliant situations, what's more, driver cautioning frameworks. Generally, facial feeling acknowledgment frameworks have been assessed on lab controlled information, which is not illustrative of the earth confronted in genuine applications. To vigorously perceive facial feelings in genuine regular circumstances, this paper proposes a methodology called Extreme Sparse Learning (ESL), which can mutually take in a word reference (set of premise) and a non-direct grouping model. The proposed approach consolidates the discriminative force of Extreme Learning Machine (ELM) with the reproduction property of meager representation to empower exact arrangement when given uproarious signs and blemished information recorded in common settings. Moreover, this work exhibits another neighborhood spatioworldly descriptor that is particular what's more, posture invariant. The proposed structure can accomplish best in class acknowledgment precision on both acted what's more, unconstrained facial feeling databases.
A New Skin Color Based Face Detection Algorithm by Combining Three Color Mode...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Character Recognition (Devanagari Script)IJERA Editor
Character Recognition is has found major interest in field of research and practical application to analyze and study characters in different languages using image as their input. In this paper the user writes the Devanagari character using mouse as a plotter and then the corresponding character is saved in the form of image. This image is processed using Optical Character Recognition in which location, segmentation, pre-processing of image is done. Later Neural Networks is used to identify all the characters by the further process of OCR i.e. by using feature extraction and post-processing of image. This entire process is done using MATLAB.
BrailleOCR: An Open Source Document to Braille Converter Applicationpijush15
This presentation is actually about an Open Source application, BrailleOCR that helps to convert scanned documents to Braille and thus helps the Visually Impaired.
What is the use of this application in real life? Well, BrailleOCR is currently the only app that integrated Optical character recognition and Braille Translation together. This app will eventually help converting a lot of important documents to Braille. The project site for this project is given here
IJCA Paper: http://www.ijcaonline.org/archives/volume68/number16/11664-7254
Project site: https://code.google.com/p/brailleocr/
The app uses a four step process. Initially, we have a scanned image, which is a RGB image. The first step or the Pre-Processing step deals with conversion of a RGB image to grayscale. The 2nd step deals with Character Recognition using the Tesseract Engine. Now, the recognition step may have errors and we require post processing to correct them. The 3rd step is thus the Post-Processing step and it actually corrects errors in the previous step. The final and the most important step is the Braille Conversion step.
HVDLP : HORIZONTAL VERTICAL DIAGONAL LOCAL PATTERN BASED FACE RECOGNITION sipij
Face image is an efficient biometric trait to recognize human beings without expecting any co-operation from a person. In this paper, we propose HVDLP: Horizontal Vertical Diagonal Local Pattern based face recognition using Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP). The face images of different sizes are converted into uniform size of 108×990and color images are converted to gray scale images in pre-processing. The Discrete Wavelet Transform (DWT) is applied on pre-processed images and LL band is obtained with the size of 54×45. The Novel concept of HVDLP is introduced in the proposed method to enhance the performance. The HVDLP is applied on 9×9 sub matrix of LL band to consider HVDLP coefficients. The local Binary Pattern (LBP) is applied on HVDLP of LL band. The final features are generated by using Guided filters on HVDLP and LBP matrices. The Euclidean Distance (ED) is used to compare final features of face database and test images to compute the performance parameters.
A bidirectional text transcription of braille for odia, hindi, telugu and eng...eSAT Journals
Abstract
Communication gap establishes an aura of unwillingness of understanding the factor behind. Basically, this is one of the common
agenda for the visually challenged people. In order to bridge this gap, a proper platform of learning for both the mass and the
visually challenged for any native language is emphasized in this paper through Braille pattern. Braille, a code, well known to
visually challenged as their mode of communication is now converted into normal text in Odia, Hindi, Telugu and English
languages using Image segmentation as the base criteria with MATLAB as its simulation field so that every person can be able to
easily decode the information being conveyed by these people. The algorithm makes the best use of segmentation, histogram
analysis, pattern recognition, letter arrays, data base generation with testing in software and dumping in using Spartan 3e FPGA
kit. This paper also elaborates on the reverse conversion of native languages and English to Braille making the paper to be more
compatible. The processing speed with efficiency and accuracy defines the effective features of this paper as a successful
approach in both software and hardware.
Keywords: visually challenge, image segmentation, MATLAB, pattern recognition, Spartan 3e FPGA kit, compatible.
Speech Recognition using HMM & GMM Models: A Review on Techniques and Approachesijsrd.com
Many ways of communications are used between human and computer, while using gesture is considered to be one of the most natural ways in a virtual reality system. Speech recognition is one of the typical methods of non-verbal communication for human beings and we naturally use various gestures to express our own intentions in everyday life. Gesture recognizers are supposed to capture and analyze the information transmitted by the hands of a person who communicates in sign language. This is a prerequisite for automatic sign-to-spoken-language translation, which has the potential to support the integration of deaf people into society. This paper present part of literature review on ongoing research and findings on different technique and approaches in gesture recognition using Hidden Markov Models for vision-based approach.
This paper presents a new local facial feature descriptor, Local Gray Code Pattern (LGCP), for facial expression recognition in contrast to widely adopted Local Binary pattern. Local Gray Code Pattern (LGCP) characterizes both the texture and contrast information of facial components. The LGCP descriptor is obtained using local gray color intensity differences from a local 3x3 pixels area weighted by their corresponding TF (term frequency). I have used extended Cohn-Kanade expression (CK+) dataset and Japanese Female Facial Expression (JAFFE) dataset with a Multiclass Support Vector Machine (LIBSVM) to evaluate proposed method. The proposed method is performed on six and seven basic expression classes in both person dependent and independent environment. According to extensive experimental results with prototypic expressions on static images, proposed method has achieved the highest recognition rate, as compared to other existing appearance-based feature descriptors LPQ, LBP, LBPU2, LBPRI, and LBPRIU2.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Hand and wrist localization approach: sign language recognition Sana Fakhfakh
This paper proposes a new hand detection and wrist localization method which presents an important step in the hand gesture recognizing process. The wrist localization step has not been given much attention and the existing works are limited and include many conditions. Our proposed approach was evaluated on a public dataset whose obtained results underscore its performance. We highlight through a comparative study with existing work, the superiority of our approach and the importance of the wrist localization step. We also propose to benefit from our proposed method which can be applied in the sign language recognition domain, and more precisely in the Arabic digit sign language recognition.
A Deep Neural Framework for Continuous Sign Language Recognition by Iterative...ijtsrd
Sign Language SL is a medium of communication for physically disabled people. It is a gesture based language for communication of dumb and deaf people. These people communicate by using different actions of hands, where each different action means something. Sign language is the only way of conversation for deaf and dumb people. It is very difficult to understand this language for the common people. Hence sign language recognition has become an important task. There is a necessity for a translator to communicate with the world. Real time translator for sign language provides a medium to communicate with others. Previous methods employs sensor gloves, hat mounted cameras, armband etc. which has wearing difficulties and have noisy behaviour. To alleviate this problem, a real time gesture recognition system using Deep Learning DL is proposed. It enables to achieve improvements on the gesture recognition performance. Jeni Moni | Anju J Prakash ""A Deep Neural Framework for Continuous Sign Language Recognition by Iterative Training: Survey"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30032.pdf
Paper Url : https://www.ijtsrd.com/engineering/computer-engineering/30032/a-deep-neural-framework-for-continuous-sign-language-recognition-by-iterative-training-survey/jeni-moni
Vocabulary length experiments for binary image classification using bov approachsipij
Bag-of-Visual-words (BoV) approach to image classification is popular among computer vision scientists.
The visual words come from the visual vocabulary which is constructed using the key points extracted from
the image database. Unlike the natural language, the length of such vocabulary for image classification is
task dependent. The visual words capture the local invariant features of the image. The region of image
over which a visual word is constrained forms the spatial content for the visual word. Spatial pyramid
representation of images is an approach to handle spatial information. In this paper, we study the role of
vocabulary lengths for the levels of a simple two level spatial pyramid to perform binary classifications.
Two binary classification problems namely to detect the presence of persons and cars are studied. Relevant
images from PASCAL dataset are being used for the learning activities involved in this work
A SIGNATURE BASED DRAVIDIAN SIGN LANGUAGE RECOGNITION BY SPARSE REPRESENTATIONijnlc
Sign language is a visual-gestural language used by deaf-dumb people for communication. As normal people are unfamiliar of sign language, the hearing-impaired people find it difficult to communicate with them. The communication gap between the normal and the deaf-dumb people can be bridged by means of Human–Computer Interaction. The objective of this paper is to convert the Dravidian (Tamil) sign language into text. The proposed method recognizes 12 vowels, 18 consonants and a special character “Aytham” of Tamil language by a vision based approach. In this work, the static images of the hand signs are obtained a web/digital camera. The hand region is segmented by a threshold applied to the hue channel of the input image. Then the region of interest (i.e. from wrist to fingers) is segmented using the reversed horizontal projection profile and the Discrete Cosine transformed signature is extracted from the boundary of hand sign. These features are invariant to translation, scale and rotation. Sparse representation classifier is incorporated to recognize 31 hand signs. The proposed method has attained a maximum recognition accuracy of 71% in a uniform background.
Abstract: The main communication methods used by deaf people are sign language, but opposed to common thought, there is no specific universal sign language: every country or even regional group uses its own set of signs. The use of sign language in digital systems can enhance communication in both directions: animated avatars can synthesize signals based on voice or text recognition; and sign language can be translated into various text or sound forms based on different images, videos and sensors input. The ultimate goal of this research, but it is not a simple spelling of spoken language, so that recognizing different signs or letters of the alphabet (which has been a common approach) is not sufficient for its transcription and automatic interpretation. Here proposes an algorithm and method for an application this would help us in recognising the various user defined signs. The palm images of right and left hand are loaded at runtime. Firstly these images will be seized and stored in directory. Then technique called Template matching is used for finding areas of an image that match (are similar) to a template image (patch). Our goal is to detect the highest matching area. We need two primary components- A) Source image (I): In the template image in which we try to find a match. B) Template image (T): The patch image which will be compared to the template image. In proposed system user defined patterns will be having 60% accuracy while default patterns will be provided with 80% accuracy.
Translation of sign language using generic fourier descriptor and nearest nei...ijcisjournal
Sign languages are used all over the world as a primary means of communication by deaf people. Sign
language translation is a promising application for vision-based gesture recognition methods. Therefore, it
is need such a tool that can translate sign language directly. This paper aims to create a system that can
translate static sign language into textual form automatically based on computer vision. The method
contains three phases, i.e. segmentation, feature extraction, and recognition. We used Generic Fourier
Descriptor (GFD) as feature extraction method and K-Nearest Neighbour (KNN) as classification
approach to recognize the signs. The system was applied to recognize each 120 stored images in database
and 120 images which is captured real time by webcam. We also translated 5 words in video sequences.
The experiment revealed that the system can recognized the signs with about 86 % accuracy for stored
images in database and 69 % for testing data which is captured real time by webcam.
50Combining Color Spaces for Human Skin Detection in Color Images using Skin ...idescitation
Skin detection remains a challenging task over
several decades in spite of many techniques evolved. It is the
elementary step of most of the computer vision applications
like face recognition, human computer interaction, etc. It
depends on the suitability of color space chosen, skin modeling
and classification of skin and non-skin pixels under varying
illumination conditions. This paper presents a symbolic
interpretation on the performance of the color spaces using
piecewise linear decision boundary classifier in color images
to find the winning color space (s). The whole task is divided
into three processes: analysis of color spaces individually;
analysis of the combination of two color spaces; and finally
making a comparative analysis among the results obtained by
the above two processes. For performing the fair evaluation,
the whole experiment is tested over commonly used databases.
Based on the success rate, false positive and false negative of
each color spaces, the winner(s) has been chosen among single
and the combination of color spaces.
Recognition of Facial Emotions Based on Sparse CodingIJERA Editor
This paper deals with acknowledgment of characteristic feelings from human countenances is a fascinating subject with an extensive variety of potential applications like human-PC communication, robotized mentoring frameworks, picture and video recovery, brilliant situations, what's more, driver cautioning frameworks. Generally, facial feeling acknowledgment frameworks have been assessed on lab controlled information, which is not illustrative of the earth confronted in genuine applications. To vigorously perceive facial feelings in genuine regular circumstances, this paper proposes a methodology called Extreme Sparse Learning (ESL), which can mutually take in a word reference (set of premise) and a non-direct grouping model. The proposed approach consolidates the discriminative force of Extreme Learning Machine (ELM) with the reproduction property of meager representation to empower exact arrangement when given uproarious signs and blemished information recorded in common settings. Moreover, this work exhibits another neighborhood spatioworldly descriptor that is particular what's more, posture invariant. The proposed structure can accomplish best in class acknowledgment precision on both acted what's more, unconstrained facial feeling databases.
A New Skin Color Based Face Detection Algorithm by Combining Three Color Mode...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Character Recognition (Devanagari Script)IJERA Editor
Character Recognition is has found major interest in field of research and practical application to analyze and study characters in different languages using image as their input. In this paper the user writes the Devanagari character using mouse as a plotter and then the corresponding character is saved in the form of image. This image is processed using Optical Character Recognition in which location, segmentation, pre-processing of image is done. Later Neural Networks is used to identify all the characters by the further process of OCR i.e. by using feature extraction and post-processing of image. This entire process is done using MATLAB.
BrailleOCR: An Open Source Document to Braille Converter Applicationpijush15
This presentation is actually about an Open Source application, BrailleOCR that helps to convert scanned documents to Braille and thus helps the Visually Impaired.
What is the use of this application in real life? Well, BrailleOCR is currently the only app that integrated Optical character recognition and Braille Translation together. This app will eventually help converting a lot of important documents to Braille. The project site for this project is given here
IJCA Paper: http://www.ijcaonline.org/archives/volume68/number16/11664-7254
Project site: https://code.google.com/p/brailleocr/
The app uses a four step process. Initially, we have a scanned image, which is a RGB image. The first step or the Pre-Processing step deals with conversion of a RGB image to grayscale. The 2nd step deals with Character Recognition using the Tesseract Engine. Now, the recognition step may have errors and we require post processing to correct them. The 3rd step is thus the Post-Processing step and it actually corrects errors in the previous step. The final and the most important step is the Braille Conversion step.
HVDLP : HORIZONTAL VERTICAL DIAGONAL LOCAL PATTERN BASED FACE RECOGNITION sipij
Face image is an efficient biometric trait to recognize human beings without expecting any co-operation from a person. In this paper, we propose HVDLP: Horizontal Vertical Diagonal Local Pattern based face recognition using Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP). The face images of different sizes are converted into uniform size of 108×990and color images are converted to gray scale images in pre-processing. The Discrete Wavelet Transform (DWT) is applied on pre-processed images and LL band is obtained with the size of 54×45. The Novel concept of HVDLP is introduced in the proposed method to enhance the performance. The HVDLP is applied on 9×9 sub matrix of LL band to consider HVDLP coefficients. The local Binary Pattern (LBP) is applied on HVDLP of LL band. The final features are generated by using Guided filters on HVDLP and LBP matrices. The Euclidean Distance (ED) is used to compare final features of face database and test images to compute the performance parameters.
A bidirectional text transcription of braille for odia, hindi, telugu and eng...eSAT Journals
Abstract
Communication gap establishes an aura of unwillingness of understanding the factor behind. Basically, this is one of the common
agenda for the visually challenged people. In order to bridge this gap, a proper platform of learning for both the mass and the
visually challenged for any native language is emphasized in this paper through Braille pattern. Braille, a code, well known to
visually challenged as their mode of communication is now converted into normal text in Odia, Hindi, Telugu and English
languages using Image segmentation as the base criteria with MATLAB as its simulation field so that every person can be able to
easily decode the information being conveyed by these people. The algorithm makes the best use of segmentation, histogram
analysis, pattern recognition, letter arrays, data base generation with testing in software and dumping in using Spartan 3e FPGA
kit. This paper also elaborates on the reverse conversion of native languages and English to Braille making the paper to be more
compatible. The processing speed with efficiency and accuracy defines the effective features of this paper as a successful
approach in both software and hardware.
Keywords: visually challenge, image segmentation, MATLAB, pattern recognition, Spartan 3e FPGA kit, compatible.
Speech Recognition using HMM & GMM Models: A Review on Techniques and Approachesijsrd.com
Many ways of communications are used between human and computer, while using gesture is considered to be one of the most natural ways in a virtual reality system. Speech recognition is one of the typical methods of non-verbal communication for human beings and we naturally use various gestures to express our own intentions in everyday life. Gesture recognizers are supposed to capture and analyze the information transmitted by the hands of a person who communicates in sign language. This is a prerequisite for automatic sign-to-spoken-language translation, which has the potential to support the integration of deaf people into society. This paper present part of literature review on ongoing research and findings on different technique and approaches in gesture recognition using Hidden Markov Models for vision-based approach.
This paper presents a new local facial feature descriptor, Local Gray Code Pattern (LGCP), for facial expression recognition in contrast to widely adopted Local Binary pattern. Local Gray Code Pattern (LGCP) characterizes both the texture and contrast information of facial components. The LGCP descriptor is obtained using local gray color intensity differences from a local 3x3 pixels area weighted by their corresponding TF (term frequency). I have used extended Cohn-Kanade expression (CK+) dataset and Japanese Female Facial Expression (JAFFE) dataset with a Multiclass Support Vector Machine (LIBSVM) to evaluate proposed method. The proposed method is performed on six and seven basic expression classes in both person dependent and independent environment. According to extensive experimental results with prototypic expressions on static images, proposed method has achieved the highest recognition rate, as compared to other existing appearance-based feature descriptors LPQ, LBP, LBPU2, LBPRI, and LBPRIU2.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Hand and wrist localization approach: sign language recognition Sana Fakhfakh
This paper proposes a new hand detection and wrist localization method which presents an important step in the hand gesture recognizing process. The wrist localization step has not been given much attention and the existing works are limited and include many conditions. Our proposed approach was evaluated on a public dataset whose obtained results underscore its performance. We highlight through a comparative study with existing work, the superiority of our approach and the importance of the wrist localization step. We also propose to benefit from our proposed method which can be applied in the sign language recognition domain, and more precisely in the Arabic digit sign language recognition.
A Deep Neural Framework for Continuous Sign Language Recognition by Iterative...ijtsrd
Sign Language SL is a medium of communication for physically disabled people. It is a gesture based language for communication of dumb and deaf people. These people communicate by using different actions of hands, where each different action means something. Sign language is the only way of conversation for deaf and dumb people. It is very difficult to understand this language for the common people. Hence sign language recognition has become an important task. There is a necessity for a translator to communicate with the world. Real time translator for sign language provides a medium to communicate with others. Previous methods employs sensor gloves, hat mounted cameras, armband etc. which has wearing difficulties and have noisy behaviour. To alleviate this problem, a real time gesture recognition system using Deep Learning DL is proposed. It enables to achieve improvements on the gesture recognition performance. Jeni Moni | Anju J Prakash ""A Deep Neural Framework for Continuous Sign Language Recognition by Iterative Training: Survey"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30032.pdf
Paper Url : https://www.ijtsrd.com/engineering/computer-engineering/30032/a-deep-neural-framework-for-continuous-sign-language-recognition-by-iterative-training-survey/jeni-moni
Vocabulary length experiments for binary image classification using bov approachsipij
Bag-of-Visual-words (BoV) approach to image classification is popular among computer vision scientists.
The visual words come from the visual vocabulary which is constructed using the key points extracted from
the image database. Unlike the natural language, the length of such vocabulary for image classification is
task dependent. The visual words capture the local invariant features of the image. The region of image
over which a visual word is constrained forms the spatial content for the visual word. Spatial pyramid
representation of images is an approach to handle spatial information. In this paper, we study the role of
vocabulary lengths for the levels of a simple two level spatial pyramid to perform binary classifications.
Two binary classification problems namely to detect the presence of persons and cars are studied. Relevant
images from PASCAL dataset are being used for the learning activities involved in this work
A SIGNATURE BASED DRAVIDIAN SIGN LANGUAGE RECOGNITION BY SPARSE REPRESENTATIONijnlc
Sign language is a visual-gestural language used by deaf-dumb people for communication. As normal people are unfamiliar of sign language, the hearing-impaired people find it difficult to communicate with them. The communication gap between the normal and the deaf-dumb people can be bridged by means of Human–Computer Interaction. The objective of this paper is to convert the Dravidian (Tamil) sign language into text. The proposed method recognizes 12 vowels, 18 consonants and a special character “Aytham” of Tamil language by a vision based approach. In this work, the static images of the hand signs are obtained a web/digital camera. The hand region is segmented by a threshold applied to the hue channel of the input image. Then the region of interest (i.e. from wrist to fingers) is segmented using the reversed horizontal projection profile and the Discrete Cosine transformed signature is extracted from the boundary of hand sign. These features are invariant to translation, scale and rotation. Sparse representation classifier is incorporated to recognize 31 hand signs. The proposed method has attained a maximum recognition accuracy of 71% in a uniform background.
Abstract: The main communication methods used by deaf people are sign language, but opposed to common thought, there is no specific universal sign language: every country or even regional group uses its own set of signs. The use of sign language in digital systems can enhance communication in both directions: animated avatars can synthesize signals based on voice or text recognition; and sign language can be translated into various text or sound forms based on different images, videos and sensors input. The ultimate goal of this research, but it is not a simple spelling of spoken language, so that recognizing different signs or letters of the alphabet (which has been a common approach) is not sufficient for its transcription and automatic interpretation. Here proposes an algorithm and method for an application this would help us in recognising the various user defined signs. The palm images of right and left hand are loaded at runtime. Firstly these images will be seized and stored in directory. Then technique called Template matching is used for finding areas of an image that match (are similar) to a template image (patch). Our goal is to detect the highest matching area. We need two primary components- A) Source image (I): In the template image in which we try to find a match. B) Template image (T): The patch image which will be compared to the template image. In proposed system user defined patterns will be having 60% accuracy while default patterns will be provided with 80% accuracy.
Translation of sign language using generic fourier descriptor and nearest nei...ijcisjournal
Sign languages are used all over the world as a primary means of communication by deaf people. Sign
language translation is a promising application for vision-based gesture recognition methods. Therefore, it
is need such a tool that can translate sign language directly. This paper aims to create a system that can
translate static sign language into textual form automatically based on computer vision. The method
contains three phases, i.e. segmentation, feature extraction, and recognition. We used Generic Fourier
Descriptor (GFD) as feature extraction method and K-Nearest Neighbour (KNN) as classification
approach to recognize the signs. The system was applied to recognize each 120 stored images in database
and 120 images which is captured real time by webcam. We also translated 5 words in video sequences.
The experiment revealed that the system can recognized the signs with about 86 % accuracy for stored
images in database and 69 % for testing data which is captured real time by webcam.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Abstract: The main communication methods used by deaf people are sign language, but opposed to common thought, there is no specific universal sign language: every country or even regional group uses its own set of signs. The use of sign language in digital systems can enhance communication in both directions: animated avatars can synthesize signals based on voice or text recognition; and sign language can be translated into various text or sound forms based on different images, videos and sensors input. The ultimate goal of this research, but it is not a simple spelling of spoken language, so that recognizing different signs or letters of the alphabet (which has been a common approach) is not sufficient for its transcription and automatic interpretation. Here proposes an algorithm and method for an application this would help us in recognising the various user defined signs. The palm images of right and left hand are loaded at runtime. Firstly these images will be seized and stored in directory. Then technique called Template matching is used for finding areas of an image that match (are similar) to a template image (patch). Our goal is to detect the highest matching area. We need two primary components- A) Source image (I): In the template image in which we try to find a match. B) Template image (T): The patch image which will be compared to the template image. In proposed system user defined patterns will be having 60% accuracy while default patterns will be provided with 80% accuracy.
VISION BASED HAND GESTURE RECOGNITION USING FOURIER DESCRIPTOR FOR INDIAN SIG...sipij
Indian Sign Language (ISL) interpretation is the major research work going on to aid Indian deaf and dumb people. Considering the limitation of glove/sensor based approach, vision based approach was considered for ISL interpretation system. Among different human modalities, hand is the primarily used modality to any sign language interpretation system so, hand gesture was used for recognition of manual
alphabets and numbers. ISL consists of manual alphabets, numbers as well as large set of vocabulary with grammar. In this paper, methodology for recognition of static ISL manual alphabets, number and static symbols is given. ISL alphabet consists of single handed and two handed sign. Fourier descriptor as a feature extraction method was chosen due the property of invariant to rotation, scale and translation. True
positive rate was achieved 94.15% using nearest neighbourhood classifier with Euclidean distance where
sample data were considered with different illumination changes, different skin color and varying distance
from camera to signer position.
Segmentation, tracking and feature extractionijcsa
The study evaluates three background subtraction techniques. The techniques ranges from very basic
algorithm to state of the art published techniques categorized based on speed, memory requirements and
accuracy. Such a review can effectively guide the designer to select the most suitable method for a given
application in a principled way. The algorithms used in the study ranges from varying levels of accuracy
and computational complexity. Few of them can also deal with real time challenges like rain, snow, hails,
swaying branches, objects overlapping, varying light intensity or slow moving objects.
Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...IJERA Editor
Gesture recognition system received great attention in the recent few years because of its manifoldness applications and the ability to interact with machine efficiently through human computer interaction. Gesture is one of human body languages which are popularly used in our daily life. It is a communication system that consists of hand movements and facial expressions via communication by actions and sights. This research mainly focuses on the research of gesture extraction and finger segmentation in the gesture recognition. In this paper, we have used image analysis technologies to create an application by encoding in MATLAB program. We will use this application to segment and extract the finger from one specific gesture. This paper is aimed to give gesture recognition in different natural conditions like dark and glare condition, different distances condition and similar object condition then collect the results to calculate the successful extraction rate.
A Study on Sparse Representation and Optimal Algorithms in Intelligent Comput...MangaiK4
Abstract -Computer vision is a dynamic research field which involves analyzing, modifying, and high-level understanding of images. Its goal is to determine what is happening in front of a camera and use the facts understood to control a computer or a robot, or to provide the users with new images that are more informative or esthetical pleasing than the original camera images. It uses many advanced techniques in image representation to obtain efficiency in computation. Sparse signal representation techniqueshave significant impact in computer vision, where the goal is to obtain a compact high-fidelity representation of the input signal and to extract meaningful information. Segmentation and optimal parallel processing algorithms are expected to further improve the efficiency and speed up in processing.
A Study on Sparse Representation and Optimal Algorithms in Intelligent Comput...MangaiK4
Abstract -Computer vision is a dynamic research field which involves analyzing, modifying, and high-level understanding of images. Its goal is to determine what is happening in front of a camera and use the facts understood to control a computer or a robot, or to provide the users with new images that are more informative or esthetical pleasing than the original camera images. It uses many advanced techniques in image representation to obtain efficiency in computation. Sparse signal representation techniqueshave significant impact in computer vision, where the goal is to obtain a compact high-fidelity representation of the input signal and to extract meaningful information. Segmentation and optimal parallel processing algorithms are expected to further improve the efficiency and speed up in processing.
Real time Myanmar Sign Language Recognition System using PCA and SVMijtsrd
Communication is the process of exchanging information, views and expressions between two or more persons, in both verbal and non verbal manner. The sign language is a visual language used by the people with the speech and hearing disabilities for communication in their daily conversation activities. Myanmar Sign Language MSL is the language of choice for most deaf people in this country. In this research paper, Real time Myanmar Sign Language Recognition System RMSLRS is proposed. The major objective is to accomplish the translation of 30 static sign gestures into Myanmar alphabets. The input video stream is captured by webcam and is inputed to computer vision. The incoming frames are converted into YCbCr color space and skin like region is detected by YCbCr threshold technique. The hand region is also segmented and converted into grayscale image and morphological operation is applied for feature extraction. In order to translate the signs of ASL into the corresponding alphabets, PCA is used for feature extraction and SVM is used for recognition of MSL signs. Experimental results show that the proposed system gives the successful recognition accuracy of static sign gestures of MSL alphabets with 89 . Myint Tun | Thida Lwin "Real-time Myanmar Sign Language Recognition System using PCA and SVM" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26797.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/26797/real-time-myanmar-sign-language-recognition-system-using-pca-and-svm/myint-tun
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Hand Segmentation Techniques to Hand Gesture Recognition for Natural Human Co...Waqas Tariq
This work is the part of vision based hand gesture recognition system for Natural Human Computer Interface. Hand tracking and segmentation are the primary steps for any hand gesture recognition system. The aim of this paper is to develop robust and efficient hand segmentation algorithm where three algorithms for hand segmentation using different color spaces with required morphological processing have were utilized. Hand tracking and segmentation algorithm (HTS) is found to be most efficient to handle the challenges of vision based system such as skin color detection, complex background removal and variable lighting condition. Noise may contain, sometime, in the segmented image due to dynamic background. An edge traversal algorithm was developed and applied on the segmented hand contour for removal of unwanted background noise.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
ISI 2024: Application Form (Extended), Exam Date (Out), EligibilitySciAstra
The Indian Statistical Institute (ISI) has extended its application deadline for 2024 admissions to April 2. Known for its excellence in statistics and related fields, ISI offers a range of programs from Bachelor's to Junior Research Fellowships. The admission test is scheduled for May 12, 2024. Eligibility varies by program, generally requiring a background in Mathematics and English for undergraduate courses and specific degrees for postgraduate and research positions. Application fees are ₹1500 for male general category applicants and ₹1000 for females. Applications are open to Indian and OCI candidates.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
A SIGN LANGUAGE RECOGNITION APPROACH FOR HUMAN-ROBOT SYMBIOSIS
1. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
DOI : 10.5121/ijcseit.2014.4402 11
A SIGN LANGUAGE RECOGNITION APPROACH FOR
HUMAN-ROBOT SYMBIOSIS
Afzal Hossian1
, Shahrin Chowdhury2
, and Asma-ull-Hosna3
1
IIT (Institute of Information Technology), University of Dhaka, Bangladesh
2
Chalmers University of Technology, Gothenburg, Sweden
3
Sookmyung Women’s University, Seoul, South-Korea
ABSTRACT
This paper introduces a new concept for the establishment of human-robot symbiotic relationship. The
system is based on the implementation of knowledge-based image processing methodologies for model
based vision and intelligent task scheduling for an autonomous social robot. This paper aims to develop an
automatic translation of static gestures of alphabets and signs in American Sign Language (ASL), using
neural network with backpropagation algorithm. System deals with images of bare hands to achieve the
recognition task. For each individual sign 10 sample images have been considered, which means in
total300 samples have been processed. In order to compare between the training set of signs and the
considered sample images, are converted into feature vectors. Experimental results reveal that this can
recognize selected ASL signs (accuracy of 92.00%). Finally, the system has been implemented issuing hand
gesture commands for ASL to a robot car, named “Moto-robo”.
KEYWORDS
American Sign Language, Histogram equalization, Human-robot symbiosis, Moto-Robo, Skin colour
segmentation.
1. INTRODUCTION
In the dictionary of American Cultural Heritage the word “symbiosis” is defined as following: “A
close, prolonged association among two or more different organisms of different species that may
but does not necessarily benefit each member” [1]. In recent times this biological term has been
used to define similar relations among wider collection of entities. In this research, the main
purpose is to establish a symbiotic relationship between robots and human beings for their
coexistence and co-operative work and consolidate their relationship for the benefit of each other.
Image understanding concerns the issues of finding interpretations of images. These
interpretations would explain the meaning of the contents of the images. In order to establish a
human-robot symbiotic society, different kinds of objects are being interpreted using the visual,
geometrical and knowledge-based approaches. When the robots are working cooperatively with
human beings, it is necessary to share and exchange their ideas and thoughts. Human hand
gesture is, therefore, immerging tremendous interest in the advancement of human-robot interface
since it provides a natural and efficient way of exploring expressions.
The sign language is the fundamental communication method between people who suffer from
hearing defects. In order for an ordinary person to communicate with deaf people, a translator is
usually needed to translate sign language into natural language and vice versa [2]. As a primary
2. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
12
component of many sign languages and in particular the American Sign Language (ASL), hand
gestures and finger-spelling language plays an important role in deaf learning and their
communication. Therefore, sign language can be considered as a collection of gestures,
movements, postures, and facial expressions corresponding to letters and words in natural
languages.
American Sign Language (ASL) is considered to be a complete language which includes signs
using hands, other gesture with the support of facial expression and postures of the body [2]. ASL
follows different grammar pattern compare to any other normal languages. Near about 6000
gestures of common words are represented using finger spelling by ASL. 26 individual alphabets
are signified by 26 different gesture with the use of single hand. These 26 alphabets of ASL are
presented in Fig. 1.
Charayaphan and Marble [3] investigated a way using image processing to understand ASL. Out
of 31 ASL symbols 27 can correctly recognize by their suggested system. Fels and Hinton [4]
have developed a system. VPL DataGlove Mark II along with a Polhemus tracker was used as
input devices in their developed system. For categorized hand gestures neural network method
was applied. For the input of HMMs, two-dimensional features of a solo camera along with view-
based approach were applied by Starner and Pentland [5]. Using HMMs and considering 262-sign
vocabulary, 91.3% accuracy was achieved for recognized isolated signs by Grobel and Assan [6].
While collecting sample features from the video recordings of users, they were using colour
gloves. Bowden and Sarhadi [7] developed a non-linear model of shape and motion for tracking
finger spelt American Sign Language. This approach is similar to HMM where ASL’s are
projected into shape space to guess the models and also to follow them.
Figure 1. Alphabets of American Sign Language
This system is capable of visually detecting all static signs of the American Sign Language
(ASL): alphabets, numerical digits as well as general words for example: like, love, not agreed
A B C D E
K L M N O
F G H I J (dynamic)
P Q R S T
Z (dynamic)
U V W X Y
3. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
13
etc. can also be represent using ASL. Fortunately the users can interact using his/her fingers only;
there is no need to use additional gloves or any other devices. Still, variation of hand shapes and
operational habit leads to recognition difficulties. Therefore, we realized the necessity to
investigate the signer independent sign language recognition to improve the system robustness
and practicability. Since the system is based on Affine transformation, our method relies on
presenting the gesture as a feature vector that is translation, scale and rotation invariant.
2. SYSTEM DESIGN
The ASL recognition system has two phases: the feature extraction phase and the classification
phase, as shown in Fig. 2.
The image samples are resized and then converted from RGB to YIQ colour model. Afterwards
the images are segmented to detect and digitize the sign image.
In the classification stage, a 3-layer, feed-forward backpropagation neural network is constructed.
It consists
Figure 2. System overview
start
Resize the Image
Convert RGB to YIQ
Feature
Extraction
stage
Classifica-
tion stage
Gesture command
generation
Is gesture?
Neural Network
Robot action
Yes
No
Skin color segmentation
Connected Component
analysis
4. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
14
of (40×30) neurons in the input layer, 768 (70% of input) neurons for the hidden layer, and 30
(total number of ASL image for the classification network) for the neurons in the output layer.
2.1 Features to analyse images
Normalization of sample images, equalization of histogram, image filtering, and skin colour
segmentation are highlighted in this phase.
2.1.1 Normalization of sample images
A low pass filter is used in order to reduce aliasing the nearest neighbour interpolation method, to
find out the values of pixels in the output image where images are resized to 160 by 120.
2.1.2. Equalization of Histogram
Equalization of Histogram is used to improve the lighting conditions and the contrast of image as
the hand images contrast depends on the lighting condition. Let the histogram
n
p
r
h
i
i =
)
( of a
digital hand image consists of the colour bins in the range ]
1
,
0
[ −
C , where i
r is the I th colour bin,
i
p is the number of pixels in the image with that colour bin and n is the total number of pixels in
the image.
Some scaling constant are calculated using the cumulative sum of bins for any interval [0,1] of r
[8]. For individual pixel value r in the original images of level s and .
1
0
1
)
(
0 ≤
≤
≤
≤ r
for
r
T is
used to yield the mapping to perform the function ),
(r
T
s = of transforming by allowing the range.
The histogram equalization process is illustrated in Fig. 3.
2.1.3. Image Filtering
Prewitt filter provides the advantage of suppressing the noise which collected from various
sources without erasing some of the image details like low-pass filter.
2.1.4. Skin colour segmentation
Skin colour segmentation is based on visual information of the human skin colours from the
image sequences in YIQ colour space. The image samples are converted from RGB to YIQ
colour model. To check, the amount of skin colour value to identify the specific colour that have
dominance over the image by searching in YIQ space.
In the following matrix, luminance channel and two chrominance channels are represented with Y
and (I,Q) respectively where linear transformation of RGB is produced from YIQ.
Luminance, hue and saturation these three attributes are described using YIQ colour model [8]:
−
−
−
=
B
G
R
Q
I
Y
79
.
0
639
.
0
212
.
0
320
.
0
384
.
0
45
.
0
49
.
0
587
.
0
25
.
0
(1)
Here red, green, and blue component values are denoted with R, G, B between the range of
[0,255].
5. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
15
Since the human skin colours are clustered in colour space and differ from person to person and
of races, so in order to detect the hand parts in an image, the skin pixels are thresholded
empirically [9],[10].
The threshold value is calculated using following equation:
)
50
20
(
)
200
60
( <
<
<
< I
and
Y (2)
The detection of hand region boundaries by such a YIQ segmentation process is illustrated in Fig.
4.
The exact location of the hand is then determined from the image with largest connected region of
skin-coloured pixels. For uneven segment image detection of connected components, the Region-
growing algorithm is applied.
In this experiment, 8-pixel neighbourhood connectivity is employed. In order to remove the false
regions from the isolated blocks, smaller connected regions are assigned by the values of the
background pixels
2.2 Classification phase
The classification phase includes neural network training for the recognition of binary image
patterns of the hand. In the neural networks the result will be not perfect. Sometimes practice
represents the best solution. Decision in this field is very difficult; we had to examine different
architectures and decide according to their results
Figure 3. Histogram equalization
Figure 4. Skin colour segmentation
6. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
16
Therefore, after several experiments, it has been decided that the proposed system should be
based on supervised learning in which the learning rule is provided with the set of examples (the
training set). When the parameters, weights and biases of the network are initialized, the network
is ready for training. The multi-layer perceptron, as shown in Fig. 5, with backpropagation
algorithm has been employed for this research.
Figure 5. Network architecture of BPNN
The number of epochs was 10,000 and the goal was 0.0001. The back-propagation training
algorithm is given below.
Step 1 Initial Phase
To ensure the uniform distribution, the random numbers are generated using weight and threshold
from network levels
,
.
,
.
+
−
i
i F
F
4
2
4
2
where Fi is the total number of inputs of neurons I in the network.
Step 2 Active Phase
Back-propagation neural network is activated to get desire yields ).
(
...,
),
(
),
( ,
,
, t
y
t
y
t
y n
d
d
d 2
1 by
applying inputs )
(
...,
),
(
),
( t
x
t
x
t
x n
2
1 .
(a) The hidden layer of authentic output of neurons, is calculated using below function:
,
)
(
)
(
)
(
1
∑ −
×
=
=
n
i
j
ij
i
j t
w
t
x
sigmoid
t
y θ (3)
where n is the number of inputs of neuron j in the hidden layer, and sigmoid is the sigmoid
activation function.
(b) The output layer of authentic outputs of the neurons, is calculated using below function:
,
)
(
)
(
)
(
1
∑ −
×
=
=
m
j
k
jk
j
k t
w
t
y
sigmoid
t
y θ (4)
where m is the number of inputs of neuron k in the hidden layer.
Step 3 Training of Weight
The following equation is used to propagate errors related with output neurons to update the
weights in the back-propagation network:
M
M
M
x1
xn yn
y1
wij
wjk
7. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
17
)
(
)
(
)
1
( p
w
p
w
p
w jk
jk
jk ∆
+
=
+ , (5)
where )
(
)
(
)
( p
p
y
p
w
k
j
ij
δ
α
=
∆ , (6)
)
(
)
(
)
1
( p
w
p
w
p
w ij
ij
ij ∆
+
=
+ , (7)
where )
(
)
(
)
( p
p
x
p
w
j
j
ij
δ
α
=
∆ , (8)
where error, )
(
)
(
)
( p
y
p
y
p
e
k
dk
k
−
= , (9)
error gradient for neuron in the output layer is:
)
(
)]
(
1
)[
(
)
( p
e
p
y
p
y
p
k
k
k
k
−
=
δ , (10)
and )
(
)
(
)]
(
1
)[
(
)
(
1
p
w
p
p
y
p
y
p
l
k
jk
k
j
j
j
∑
=
−
= δ
δ . (11)
Step 4 Iteration
Increase iteration t by one, go back to Step 2 and repeat the process until the error value reduces
to the desired level. A complete flow chart of our proposed network is shown in Fig. 6.
3. EXPERIMENTS RESULTS & PERFORMANCE
The performance and effectiveness of the system has been justified using different hand
expressions and issuing commands to a robot named “Moto-Robo”. The computer configuration
for this experiment was Pentium IV 1.2 GHz PC along with 512 MB RAM. Visual C++ was used
as the programming language to implement the algorithm.
3.1. Interfacing the robot
The communication link between the computer and the robot has been established by means of
parallel communication port. The parallel port is a 25 pin D-shaped female (DB25) connector
equipped in the back of the computer. The pin configuration of DB25 connector is shown in the
Fig. 7. The lines in DB25 connector are divided in to three groups: Status lines, Data lines and
Control lines. As the name refers, data is transferred over data lines, Control lines are used to
control the peripheral and of course, the peripheral returns status signals back computer through
Status lines.
In order to access parallel ports by the programmers some library functions are used.
8. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
18
Status Resister Data Resister
Control Resister
Figure 6. Flow chart of BPNN
Figure 7. Pin configuration of DB25 port
Visual C++ provides two functions to access IO mapped peripherals, ‘inp’ for reading data from
the port and ‘outp’ for writing data into the port.
3.2 Analysis of Experiments
At first to determine the testing ability of the recognition system of hand gesture, to classify signs
for both training and testing set of data where the quantity of inputs influences the neural
network. Few of the signs have resemblances between them which lead to create some problems
in the performance.
In this experiment, the binary images are used to recognize the system using training and testing
data set. Also 10 samples for each sign were taken from 10 different volunteers. For each sign, 5
out of 10 samples were used for training purpose, while the remaining 5 signs were used for
testing. Various orientations and distances is considered while collecting sample images using
start
Initialization
Activation
Terminate?
Weight training
Iteration
Stop
No
Yes
9. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
19
digital camera. This way, we were able to obtain a data set with cases that had different sizes and
orientations, so we could examine the capabilities of our feature extraction scheme.
Performance evaluation of the system depends on its capability of correctly categorizing samples
related to their classes. The ratio of correctly categorizing samples and total amount of sample is
denoted as recognition ratio, i.e.
%
100
Re ×
=
signs
of
amount
Total
sign
d
categorize
correctly
of
Number
rate
cognition (12)
In backpropagation learning algorithm modification in weights considered for a number of
periods results to continuous decrement in Training curve is represented in Fig. 8
Figure 8. Error versus iteration for training the BPNN
Figure 9. Program interface for robot control
0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
0 500 1000 1500 2000 2500
Learning time (iteration)
Error
10. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
20
Table 1 Command to control Moto-Robo
3.3 Implementation
A remote control car (Moto-Robo), connected to the pc through the parallel port, has been
controlled by means of commands directed by the hand gesture of the user. The car has several
movements, such as: Forward, Backward, Turn right, Turn Left, Turn Light on, Turn Light off
and so on depending on the sign languages F, B, R, L, W, E, respectively. Some of the ASL
employed for controlling the robot is listed in Table 1.
The system was tested with (300) images, (ten images for each sign) untrained images; previously
unseen for the testing phase. In order to determining the yields in a suitable way a GUI has been
created by us. An example is shown in the Fig 9, where one of the actions of the robot as a result
of hand gesture recognition process is shown.
4. CONCLUSION
This research presents the development of a system for the recognition of American Sign
Language. On recognition of different hand gestures, a real time robot interaction system has
been implemented. For individual image pattern related to the set of training a set of input data
for accomplishing the work. Without the need of any gloves, images for different signs were
captured by digital camera. Deviation in position, direction, size and gesture are proved to be
easily adapted by the developed system. This is because the extracted features method used
Affine transformation to make the system translation, scaling and rotation invariant. The
recognition rate for training data and testing data are 92.0% and 80% respectively for the future
system.
The work presented in this research deals with static signs of ASL only. Adaptation of dynamic
signs can be an interesting thing to watch in future. There is a limitation in the existing system
that it only deals with images that have a non-skin colour background, overcoming this limitation
can make the system more compatible in real life. Beside hand images other types of images for
example eye tracking, facial expression, head gesture etc. can also be considered as sample
images for the network to analyse. The goal is to create a symbiotic environment in order to give
the opportunity to the robots to exchange their ideas with human beings which will definitely
bring benefits for both and also have an positive impact on the society.
11. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 4, No.4, August 2014
21
REFERENCE
[1] M. A. Bhuiyan and H. Ueno,(2003) “Image Understanding for Human -robot Symbiosis”, 6th ICCIT,
Dhaka, Bangladesh, Vol. 2, pp. 782-787.
[2] International Bibliography of Sign Language, (2005),[online]. Available:
http://www.signlang.unihamburg.de/bibweb/FJournals.html
[3] C. Charayaphan and A. Marble, (1992) “Image processing system for interpreting motion in
American sign language”, Journal of Biomedical Engineering, Vol. 14, pp. 419–425.
[4] S. Fels and G. Hinton, (1993) “GloveTalk: a neural network interface between a DataGlove and a
speech synthesizer”, IEEE Transactions on Neural Networks, Vol. 4, pp. 2–8.
[5] T. Starner and A. Pentland,(1995) “Visual recognition of American sign language using hidden
Markov models”, International Workshop on Automatic Face and Gesture Recognition, Zurich,
Switzerland, pp. 189–194.
[6] K. Grobel and M. Assan, (1996) “Isolated sign language recognition using hidden Markov models. In
Proceedings of the international conference of system, man and cybernetics”, pp. 162–167.
[7] R. Bowden and M. Sarhadi, (2002) “A non-linear model of shape and motion for tracking finger spelt
American sign language”, Image and Vision Computing, Vol. 9–10, pp. 597–607.
[8] R. C. Gonzalez and R. E. Woods, (2003) “Digital Image Processing”, Pearson Education Inc., 2nd
Edition, Delhi.
[9] M. A. Bhuiyan, V. Ampornaramveth, S. Muto, and H. Ueno,(2003) “Face Detection and Facial
Feature Localization for Human-machine Interface”, NII Journal, Vol. 5, No. 1, pp. 26-39.
[10] M. A. Bhuiyan, V. Ampornaramveth, S. Muto, and H. Ueno,(2004) “ On Tracking of Eye for Human-
Robot Interface”, International Journal of Robotics and Automation, Vol. 19, No. 1, pp. 42-54.