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.
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.
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.
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.
Automated Bangla sign language translation system for alphabets by means of M...TELKOMNIKA JOURNAL
Individuals with hearing and speaking impairment communicate using sign language. The movement of hand, body and expressions of face are the means by which the people, who are unable to hear and speak, can communicate. Bangla sign alphabets are formed with one or two hand movements. There are some features which differentiates the signs. To detect and recognize the signs, analyzing its shape and comparing its features is necessary. This paper aims to propose a model and build a computer systemthat can recognize Bangla Sign Lanugage alphabets and translate them to corresponding Bangla letters by means of deep convolutional neural network (CNN). CNN has been introduced in this model in form of a pre-trained model called “MobileNet” which produced an average accuracy of 95.71% in recognizing 36 Bangla Sign Language alphabets.
Video Audio Interface for recognizing gestures of Indian sign LanguageCSCJournals
We proposed a system to robotically recognize gestures of sign language from a video stream of the signer. The developed system converts words and sentences of Indian sign language into voice and text in English. We have used the power of image processing techniques and artificial intelligence techniques to achieve the objective. To accomplish the task we used powerful image processing techniques such as frame differencing based tracking, edge detection, wavelet transform, image fusion techniques to segment shapes in our videos. It also uses Elliptical Fourier descriptors for shape feature extraction and principal component analysis for feature set optimization and reduction. Database of extracted features are compared with input video of the signer using a trained fuzzy inference system. The proposed system converts gestures into a text and voice message with 91 percent accuracy. The training and testing of the system is done using gestures from Indian Sign Language (INSL). Around 80 gestures from 10 different signers are used. The entire system was developed in a user friendly environment by creating a graphical user interface in MATLAB. The system is robust and can be trained for new gestures using GUI.
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.
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.
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.
Automated Bangla sign language translation system for alphabets by means of M...TELKOMNIKA JOURNAL
Individuals with hearing and speaking impairment communicate using sign language. The movement of hand, body and expressions of face are the means by which the people, who are unable to hear and speak, can communicate. Bangla sign alphabets are formed with one or two hand movements. There are some features which differentiates the signs. To detect and recognize the signs, analyzing its shape and comparing its features is necessary. This paper aims to propose a model and build a computer systemthat can recognize Bangla Sign Lanugage alphabets and translate them to corresponding Bangla letters by means of deep convolutional neural network (CNN). CNN has been introduced in this model in form of a pre-trained model called “MobileNet” which produced an average accuracy of 95.71% in recognizing 36 Bangla Sign Language alphabets.
Video Audio Interface for recognizing gestures of Indian sign LanguageCSCJournals
We proposed a system to robotically recognize gestures of sign language from a video stream of the signer. The developed system converts words and sentences of Indian sign language into voice and text in English. We have used the power of image processing techniques and artificial intelligence techniques to achieve the objective. To accomplish the task we used powerful image processing techniques such as frame differencing based tracking, edge detection, wavelet transform, image fusion techniques to segment shapes in our videos. It also uses Elliptical Fourier descriptors for shape feature extraction and principal component analysis for feature set optimization and reduction. Database of extracted features are compared with input video of the signer using a trained fuzzy inference system. The proposed system converts gestures into a text and voice message with 91 percent accuracy. The training and testing of the system is done using gestures from Indian Sign Language (INSL). Around 80 gestures from 10 different signers are used. The entire system was developed in a user friendly environment by creating a graphical user interface in MATLAB. The system is robust and can be trained for new gestures using GUI.
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.
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.
Hand gesture recognition method arriving great consideration in latest few years since of its manifoldness application and facility to interrelate by machine efficiently during human computer interaction. This paper mainly focuses on the survey on Hand Gesture Recognition. The hand gestures give a divide complementary modality to speech for express ones data. Hand gesture is the method of non-verbal communiqué for human beings for its freer expressions much more other than the body parts. Hand gesture detection has greater significance in scheme a competent human computer interaction method. This paper emphasis on different hand gesture approaches, technologies and applications.
A Real-Time Letter Recognition Model for Arabic Sign Language Using Kinect an...INFOGAIN PUBLICATION
The objective of this research is to develop a supervised machine learning hand-gesturing model to recognize Arabic Sign Language (ArSL), using two sensors: Microsoft's Kinect with a Leap Motion Controller. The proposed model relies on the concept of supervised learning to predict a hand pose from two depth images and defines a classifier algorithm to dynamically transform gestural interactions based on 3D positions of a hand-joint direction into their corresponding letters whereby live gesturing can be then compared and letters displayed in real time. This research is motivated by the need to increase the opportunity for the Arabic hearing-impaired to communicate with ease using ArSL and is the first step towards building a full communication system for the Arabic hearing impaired that can improve the interpretation of detected letters using fewer calculations. To evaluate the model, participants were asked to gesture the 28 letters of the Arabic alphabet multiple times each to create an ArSL letter data set of gestures built by the depth images retrieved by these devices. Then, participants were later asked to gesture letters to validate the classifier algorithm developed. The results indicated that using both devices for the ArSL model were essential in detecting and recognizing 22 of the 28 Arabic alphabet correctly 100 %.
Hindi digits recognition system on speech data collected in different natural...csandit
This paper presents a baseline digits speech recognizer for Hindi language. The recording environment is different for all speakers, since the data is collected in their respective homes. The different environment refers to vehicle horn noises in some road facing rooms, internal background noises in some rooms like opening doors, silence in some rooms etc. All these recordings are used for training acoustic model. The Acoustic Model is trained on 8 speakers’ audio data. The vocabulary size of the recognizer is 10 words. HTK toolkit is used for building
acoustic model and evaluating the recognition rate of the recognizer. The efficiency of the recognizer developed on recorded data, is shown at the end of the paper and possible directions for future research work are suggested.
The technical study had been performed on
many foreign languages like Japanese; Chinese etc. but the
efforts on Indian ancient script is still immature. As the Modi
script language is ancient and cursive type, the OCR of it is still
not widely available. As per our knowledge, Prof. D.N.Besekar,
Dept. of Computer Science, Shri. Shivaji College of Science,
Akola had proposed a system for recognition of offline
handwritten MODI script Vowels. The challenges of
recognition of handwritten Modi characters are very high due
to the varying writing style of each individual. Many vital
documents with precious information have been written in
Modi and currently, these documents have been stored and
preserved in temples and museums. Over a period of time these
documents will wither away if not given due attention. In this
paper we propose a system for recognition of handwritten
Modi script characters; the proposed method uses Image
processing techniques and algorithms which are described
below.
General Terms
Preprocessing techniques: Gray scaling, Thresholding,
Boundary detection, Thinning, cropping, scaling, Template
generation. Other algorithms used- Average method, otsu
method, Stentiford method, Template-based matching method
The growing trend of online image sharing and downloads today mandate the need for better encoding and
decoding scheme. This paper looks into this issue of image coding. Multiple Description Coding is an
encoding and decoding scheme that is specially designed in providing more error resilience for data
transmission. The main issue of Multiple Description Coding is the lossy transmission channels. This work
attempts to address the issue of re-constructing high quality image with the use of just one descriptor
rather than the conventional descriptor. This work compare the use of Type I quantizer and Type II
quantizer. We propose and compare 4 coders by examining the quality of re-constructed images. The 4
coders are namely JPEG HH (Horizontal Pixel Interleaving with Huffman Coding) model, JPEG HA
(Horizontal Pixel Interleaving with Arithmetic Encoding) model, JPEG VH (Vertical Pixel Interleaving
with Huffman Encoding) model, and JPEG VA (Vertical Pixel Interleaving with Arithmetic Encoding)
model. The findings suggest that the use of horizontal and vertical pixel interleavings do not affect the
results much. Whereas the choice of quantizer greatly affect its performance.
Video Compression Algorithm Based on Frame Difference Approaches ijsc
The huge usage of digital multimedia via communications, wireless communications, Internet, Intranet and cellular mobile leads to incurable growth of data flow through these Media. The researchers go deep in developing efficient techniques in these fields such as compression of data, image and video. Recently, video compression techniques and their applications in many areas (educational, agriculture, medical …) cause this field to be one of the most interested fields. Wavelet transform is an efficient method that can be used to perform an efficient compression technique. This work deals with the developing of an efficient video compression approach based on frames difference approaches that concentrated on the calculation of frame near distance (difference between frames). The
selection of the meaningful frame depends on many factors such as compression performance, frame details, frame size and near distance between frames. Three different approaches are applied for removing the lowest frame difference. In this paper, many videos are tested to insure the efficiency of this technique, in addition a good performance results has been obtained.
Contactless palmprint recognition systems alleviate the concerns on personal hygiene, acquisition flexibility, etc. Unfortunately, the preprocessing of contactless palmprint image faces several severe challenges, including unconstrained hand placement, complex background, light interference, etc. This paper proposes logical conjunction of triple-perpendiculardirectional translation residual (TPDTR) for the improvement of contactless palmprint image preprocessing. The search of hand valley point is within the borders of hand valley gap detected by TPDTR; therefore, the computational cost is effectively decreased. Furthermore, the anti-interference capacity of region is stronger than that of point and line, so TPDTR improves the accuracy of hand valley point detection. The experimental results confirm the superiorities of TPDTR over the existing methods in computational cost and accuracy.
Proceedings of The International Conference on Information Technology: New Generations, USA.
Computers still have a long way to go before they can interact with users in a truly natural fashion. From a
user’s perspective, the most natural way to interact with a computer would be through a
speech and gesture
interface. Although speech recognition has made significant advances in the past ten years, gesture
recognition has been lagging behind.
Sign L
anguages (SL) are the most accomplished forms of gestural
communication. Therefore, their auto
matic analysis is a real challenge, which is interestingly implied to their
lexical and syntactic organization levels. Statements dealing with sign language occupy a significant interest
in the Automatic Natural Language Processing (ANLP) domain. In this w
ork, we are dealing with sign
language recognition, in particular of French Sign Language (FSL). FSL has its own specificities, such as the
simultaneity of several parameters, the important role of the facial expression or movement and the use of
space for
the proper utterance organization. Unlike speech recognition, Frensh sign language (FSL) events
occur both sequentially and simultaneously. Thus, the computational processing of FSL is too complex than
the spoken languages. We present a novel approach bas
ed on HMM to reduce the recognition complexity.
A novel authenticated cipher for rfid systemsijcisjournal
In this paper, we present RBS (Redundant Bit Security) algorithm which is a low-complexity symmetric
encryption with a 132-bit secret key. In this algorithm redundant bits are distributed among plaintext data
bits to change the location of the plaintext bits in the transmitted data without changing their order. The
location of redundant bits inside the transmitted data represents the secret key between sender and
receiver. The algorithm provides integrity and authentication of the original data as well. The
implementation comparison of this algorithm with other algorithms confirms that it a good candidate for
resource-constraint devices such as RFID systems and wireless sensors.
Improvement of Search Algorithm for Integral Distinguisher in Subblock-Based ...ijcisjournal
Integral distinguisher is the main factor of integral attack. Conventionally, higher order integral distinguisher is obtained as an extension of first order integral (conventional algorithm). The algorithm was applied to many subblock-based block ciphers, however, the conventional algorithm has some problems. We find other integral distinguisher of two sub block-based block ciphers, TWINE and LBlock, which are different from the conventional evaluations. As a solution, we propose a new algorithm to search for higher order integral distinguisher. The point of a proposal algorithm is exploitation of bijective and injective components of cipher functions. Applying the proposal algorithm to TWINE and LBlock, we confirm the results of the proposal algorithm are consistent with the results which are calculated from computer experiment. The results are the optimal distinguisher and the most advantageous one for the attackers. Our proposal algorithm contributes to development of stronger block ciphers by obtaining such integral distinguisher.
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.
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.
Hand gesture recognition method arriving great consideration in latest few years since of its manifoldness application and facility to interrelate by machine efficiently during human computer interaction. This paper mainly focuses on the survey on Hand Gesture Recognition. The hand gestures give a divide complementary modality to speech for express ones data. Hand gesture is the method of non-verbal communiqué for human beings for its freer expressions much more other than the body parts. Hand gesture detection has greater significance in scheme a competent human computer interaction method. This paper emphasis on different hand gesture approaches, technologies and applications.
A Real-Time Letter Recognition Model for Arabic Sign Language Using Kinect an...INFOGAIN PUBLICATION
The objective of this research is to develop a supervised machine learning hand-gesturing model to recognize Arabic Sign Language (ArSL), using two sensors: Microsoft's Kinect with a Leap Motion Controller. The proposed model relies on the concept of supervised learning to predict a hand pose from two depth images and defines a classifier algorithm to dynamically transform gestural interactions based on 3D positions of a hand-joint direction into their corresponding letters whereby live gesturing can be then compared and letters displayed in real time. This research is motivated by the need to increase the opportunity for the Arabic hearing-impaired to communicate with ease using ArSL and is the first step towards building a full communication system for the Arabic hearing impaired that can improve the interpretation of detected letters using fewer calculations. To evaluate the model, participants were asked to gesture the 28 letters of the Arabic alphabet multiple times each to create an ArSL letter data set of gestures built by the depth images retrieved by these devices. Then, participants were later asked to gesture letters to validate the classifier algorithm developed. The results indicated that using both devices for the ArSL model were essential in detecting and recognizing 22 of the 28 Arabic alphabet correctly 100 %.
Hindi digits recognition system on speech data collected in different natural...csandit
This paper presents a baseline digits speech recognizer for Hindi language. The recording environment is different for all speakers, since the data is collected in their respective homes. The different environment refers to vehicle horn noises in some road facing rooms, internal background noises in some rooms like opening doors, silence in some rooms etc. All these recordings are used for training acoustic model. The Acoustic Model is trained on 8 speakers’ audio data. The vocabulary size of the recognizer is 10 words. HTK toolkit is used for building
acoustic model and evaluating the recognition rate of the recognizer. The efficiency of the recognizer developed on recorded data, is shown at the end of the paper and possible directions for future research work are suggested.
The technical study had been performed on
many foreign languages like Japanese; Chinese etc. but the
efforts on Indian ancient script is still immature. As the Modi
script language is ancient and cursive type, the OCR of it is still
not widely available. As per our knowledge, Prof. D.N.Besekar,
Dept. of Computer Science, Shri. Shivaji College of Science,
Akola had proposed a system for recognition of offline
handwritten MODI script Vowels. The challenges of
recognition of handwritten Modi characters are very high due
to the varying writing style of each individual. Many vital
documents with precious information have been written in
Modi and currently, these documents have been stored and
preserved in temples and museums. Over a period of time these
documents will wither away if not given due attention. In this
paper we propose a system for recognition of handwritten
Modi script characters; the proposed method uses Image
processing techniques and algorithms which are described
below.
General Terms
Preprocessing techniques: Gray scaling, Thresholding,
Boundary detection, Thinning, cropping, scaling, Template
generation. Other algorithms used- Average method, otsu
method, Stentiford method, Template-based matching method
The growing trend of online image sharing and downloads today mandate the need for better encoding and
decoding scheme. This paper looks into this issue of image coding. Multiple Description Coding is an
encoding and decoding scheme that is specially designed in providing more error resilience for data
transmission. The main issue of Multiple Description Coding is the lossy transmission channels. This work
attempts to address the issue of re-constructing high quality image with the use of just one descriptor
rather than the conventional descriptor. This work compare the use of Type I quantizer and Type II
quantizer. We propose and compare 4 coders by examining the quality of re-constructed images. The 4
coders are namely JPEG HH (Horizontal Pixel Interleaving with Huffman Coding) model, JPEG HA
(Horizontal Pixel Interleaving with Arithmetic Encoding) model, JPEG VH (Vertical Pixel Interleaving
with Huffman Encoding) model, and JPEG VA (Vertical Pixel Interleaving with Arithmetic Encoding)
model. The findings suggest that the use of horizontal and vertical pixel interleavings do not affect the
results much. Whereas the choice of quantizer greatly affect its performance.
Video Compression Algorithm Based on Frame Difference Approaches ijsc
The huge usage of digital multimedia via communications, wireless communications, Internet, Intranet and cellular mobile leads to incurable growth of data flow through these Media. The researchers go deep in developing efficient techniques in these fields such as compression of data, image and video. Recently, video compression techniques and their applications in many areas (educational, agriculture, medical …) cause this field to be one of the most interested fields. Wavelet transform is an efficient method that can be used to perform an efficient compression technique. This work deals with the developing of an efficient video compression approach based on frames difference approaches that concentrated on the calculation of frame near distance (difference between frames). The
selection of the meaningful frame depends on many factors such as compression performance, frame details, frame size and near distance between frames. Three different approaches are applied for removing the lowest frame difference. In this paper, many videos are tested to insure the efficiency of this technique, in addition a good performance results has been obtained.
Contactless palmprint recognition systems alleviate the concerns on personal hygiene, acquisition flexibility, etc. Unfortunately, the preprocessing of contactless palmprint image faces several severe challenges, including unconstrained hand placement, complex background, light interference, etc. This paper proposes logical conjunction of triple-perpendiculardirectional translation residual (TPDTR) for the improvement of contactless palmprint image preprocessing. The search of hand valley point is within the borders of hand valley gap detected by TPDTR; therefore, the computational cost is effectively decreased. Furthermore, the anti-interference capacity of region is stronger than that of point and line, so TPDTR improves the accuracy of hand valley point detection. The experimental results confirm the superiorities of TPDTR over the existing methods in computational cost and accuracy.
Proceedings of The International Conference on Information Technology: New Generations, USA.
Computers still have a long way to go before they can interact with users in a truly natural fashion. From a
user’s perspective, the most natural way to interact with a computer would be through a
speech and gesture
interface. Although speech recognition has made significant advances in the past ten years, gesture
recognition has been lagging behind.
Sign L
anguages (SL) are the most accomplished forms of gestural
communication. Therefore, their auto
matic analysis is a real challenge, which is interestingly implied to their
lexical and syntactic organization levels. Statements dealing with sign language occupy a significant interest
in the Automatic Natural Language Processing (ANLP) domain. In this w
ork, we are dealing with sign
language recognition, in particular of French Sign Language (FSL). FSL has its own specificities, such as the
simultaneity of several parameters, the important role of the facial expression or movement and the use of
space for
the proper utterance organization. Unlike speech recognition, Frensh sign language (FSL) events
occur both sequentially and simultaneously. Thus, the computational processing of FSL is too complex than
the spoken languages. We present a novel approach bas
ed on HMM to reduce the recognition complexity.
A novel authenticated cipher for rfid systemsijcisjournal
In this paper, we present RBS (Redundant Bit Security) algorithm which is a low-complexity symmetric
encryption with a 132-bit secret key. In this algorithm redundant bits are distributed among plaintext data
bits to change the location of the plaintext bits in the transmitted data without changing their order. The
location of redundant bits inside the transmitted data represents the secret key between sender and
receiver. The algorithm provides integrity and authentication of the original data as well. The
implementation comparison of this algorithm with other algorithms confirms that it a good candidate for
resource-constraint devices such as RFID systems and wireless sensors.
Improvement of Search Algorithm for Integral Distinguisher in Subblock-Based ...ijcisjournal
Integral distinguisher is the main factor of integral attack. Conventionally, higher order integral distinguisher is obtained as an extension of first order integral (conventional algorithm). The algorithm was applied to many subblock-based block ciphers, however, the conventional algorithm has some problems. We find other integral distinguisher of two sub block-based block ciphers, TWINE and LBlock, which are different from the conventional evaluations. As a solution, we propose a new algorithm to search for higher order integral distinguisher. The point of a proposal algorithm is exploitation of bijective and injective components of cipher functions. Applying the proposal algorithm to TWINE and LBlock, we confirm the results of the proposal algorithm are consistent with the results which are calculated from computer experiment. The results are the optimal distinguisher and the most advantageous one for the attackers. Our proposal algorithm contributes to development of stronger block ciphers by obtaining such integral distinguisher.
A Wallace Tree Approach for Data Aggregation in Wireless Sensor Nodes ijcisjournal
Wireless Sensor Networks (WSN) refers to a gathering of spatially scattered and committed sensors used
for to sense the environmental and physical conditions. The WSN collects and aggregates the data from
all the sensor nodes and send it to the sink. But the delay required for Radio transmission of collected
information to the sink is very high. If the delay of the network is high then the power consumption may be
high it leads to decrease in node life time. So to avoid that problem the delay of the network must be kept
at minimum in order to increase the node lifetime. If number of computations required for data
aggregation process are low then automatically the delay of the network is also very less. At present a
carry look ahead adder with parallel prefix algorithm for data aggregation is used but with this approach
is having the disadvantages like high latency and memory. To avoid all those disadvantages a novel tree
approach is proposed. The expected results are reduced in latency that is it increase the speed of data
aggregation process in Wireless sensor nodes along with less memory requirement for that Tree structure.
An Optimized Device Sizing of Two-Stage CMOS OP-AMP Using Multi-Objective Gen...ijcisjournal
A novel approach for optimizing the transistor dimensions of two stage CMOS op-amp using MultiObjective
Genetic Algorithm (MOGA) is presented. The proposed approach is used to find the optimal
dimensions of each transistor to improve op-amp performances for analog and mixed signal integrated
circuit design. The aim is to automatically determine the device sizes to meet the given performance
specifications while minimizing the cost function such as power dissipation and a weighted sum of the
active area. This strongly suggests that the approach is capable of determining the globally optimal
solutions to the problem. Exactness of performance prediction in the device sizing program (implemented
in MATLAB) maintained. Here Six parameters are considered i.e., open loop gain, Phase Margin (PM),
Area (A), Bandwidth of unity Gain (UGB), Power Consumption (P) and Slew Rate (SR). The circuit is
simulated in cadence(Virtuoso Spectre) 0.18um CMOS technology.
128-Bit Area Efficient Reconfigurable Carry Select Adder ijcisjournal
Adders are one of the most critical arithmetic circuits in a system and their throughput affects the overall
performance of the system. Carry Select Adder (CSLA) is one of the fastest adders used in many dataprocessing
processors to perform fast arithmetic functions. From the structure of the CSLA, it is clear that
there is scope for reducing the area and power consumption in the CSLA. In this paper, we proposed an
area-efficient carry select adder by sharing the common Boolean logic term. After logic optimization and
sharing partial circuit, we only need one XOR gate and one inverter gate for sum generation. Through the
multiplexer, we can select the final-sum only and for carry selection we need only one AND gate and one
OR gate. Based on this modification 16-, 32-, 64-, and 128-bit CSLA architecture have been developed and
compared with the conventional CSLA architecture. The proposed design greatly reduces the area
compared to other CSLAs. From this improvement, the gate count of a 128-bit carry select adder can be
reduced from 3320 to 1664. The proposed structure is implemented in Artix-7 FPGA. Compared with the
proposed design, the conventional CSLA has 65.80% less area.
Target Detection Using Multi Resolution Analysis for Camouflaged Images ijcisjournal
Target detection is a challenging problem having many applications in defense and civil. Most of the
targets in defense are camouflaged. It is difficult for a system to detect camouflaged targets in an image. A
novel and constructive approach is proposing to detect object in camouflage images. This method uses
various methodologies such as 2-D DWT, gray level co-occurrence matrix (GLCM), wavelet coefficient
features, region growing algorithm and canny edge detection. Target detection is achieved by calculating
wavelet coefficient features from GLCM of transformed sub blocks of the image. Seed block is obtained by
evaluating wavelet coefficient features. Finally the camouflage object is highlighted using image
processing schemes. The proposed target detection system is implemented in Matlab 7.7.0 and tested on
different kinds of images.
Noise Immune Convolutional Encoder Design and Its Implementation in Tanner ijcisjournal
With the rapid advances in integrated circuit(IC) technologies, number of functions on a chip was
increasing at a very fast rate, with which interconnect density is increasing especially in functional logic
chips. The on-chip noise affects are increasing and needs to be addressed. In this paper we have
implemented a convolution encoder using a technique that provides higher noise immunity. The encoder
circuit is simulated in Tanner 15.0 with data rate of 25Mbps and a clock frequency of 250MHz
Comparative Performance Analysis of Low Power Full Adder Design in Different ...ijcisjournal
This paper gives the comparison of performance of full adder design in terms of area, power and delay in
different logic styles. Full adder design achieves low power using the Transmission Gate logic compared to
all other topologies such as Basic CMOS, Pass Transistor and GDI techniques but it make use of more
number of transistors compared to GDI. GDI occupies less area compared to all other logic design styles.
This paper presents the simulated outcome using Tanner tools and also H-Spice tool which shows power
and speed comparison of different full adder designs. All simulations have been performed in 90nm, 45nm
and 22nm scaling parameters using Predictive Technology Models in H-Spice tool.
Implementation of Low-Complexity Redundant Multiplier Architecture for Finite...ijcisjournal
In the present work, a low-complexity Digit-Serial/parallel Multiplier over Finite Field is proposed. It is
employed in applications like cryptography for data encryption and decryptionto deal with discrete
mathematical andarithmetic structures. The proposedmultiplier utilizes a redundant representation because
of their free squaring and modular reduction. The proposed 10-bit multiplier is simulated and synthesized
using Xilinx VerilogHDL. It is evident from the simulation results that the multiplier has significantly low
area and power when compared to the previous structures using the same representation.
Cryptography technology is a security technique used to change plain text to another shape of data or to
symbols, which is known as the cipher text. Cryptography aims to keep the data secure during its journey
through public networks. Currently, there are many proposed algorithms that provide this service
especially for sensitive data or very important conversations either through mobile or video conferences. In
this paper, an inventive security symmetric algorithm is implemented and evaluated, and its performance is
compared to the AES. The algorithm has four different rounds for each quarter of the key container table,
and each of them serves to shift the table. The algorithm uses the XOR operation, which, being lightweight
and cheap, is very appropriate for use with Real Time Applications. The result shows that the suggested
algorithm spends less time than AES although it has 16 rounds and the numbers used to mix up the table
are big.
Content Based Image Retrieval Using Gray Level Co-Occurance Matrix with SVD a...ijcisjournal
In this paper, gray level co-occurrence matrix, gray level co-occurrence matrix with singular value
decomposition and local binary pattern are presented for content based image retrieval. Based upon the
feature vector parameters of energy, contrast, entropy and distance metrics such as Euclidean distance,
Canberra distance, Manhattan distance the retrieval efficiency, precision, and recall of the images are
calculated. The retrieval results of the proposed method are tested on Corel-1k database. The results after
being investigated shows a significant improvement in terms of average retrieval rate, average retrieval
precision and recall of different algorithms such as GLCM, GLCM & SVD, LBP with radius one and LBP
with radius two based on different distance metrics.
Slope at Zero Crossings (ZC) of Speech Signal for Multi-Speaker Activity Dete...ijcisjournal
Multi-Speaker activity (MSA) detection helps in detecting the presence of whether the speech signals has a
single speaker or multiple speaker speeches in the speech signal. It is easy to calculate the slope at ZCs
(zero crossings) of the speech signal and makes a comparison with a suitable threshold (Th). Multi-speaker
is declared as and when the zero crossing value exceeds the threshold. The impact of the proposed
technique is compared to the existing technique by calculating the sample-by-sample ZCR (Zero crossing
rate) value is demonstrated. Experimental results prove that the proposed ZCR technique achieves accurate
results than the traditional techniques for MSA detection that uses the cepstrum resynthesis residual
magnitude (CRRM) in the literature.
A survey on privacy preserving data publishingijcisjournal
Data mining is a computational process of analysing and extracting the data from large useful datasets. In
recent years, exchanging and publishing data has been common for their wealth of opportunities. Security,
Privacy and data integrity are considered as challenging problems in data
mining.Privacy is necessary to protect people’s interest in competitive situations. Privacy is an abilityto
create and maintain different sort of social relationships with people. Privacy Preservation is one of the
most important factor for an individual since he should not embarrassed by an adversary. The Privacy
Preservation is an important aspect of data mining to ensure the privacy by various methods. Privacy
Preservation is necessary to protect sensitive information associated with individual. This paper provides a
survey of key to success and an approach where individual’s privacy would to be non-distracted.
Secure Image Transfer in The Domain Transform DFTijcisjournal
This paper presents a new approach for secure image transmission. It consists of three treatments including: a compression based on Discrete Fourier Transform (DFT), a use of symmetric encryption Advanced Encryption Standard (AES) and a Data Hidden Insertion technique for the transport of sensitive information.
Design of Tripl-Band CPW FED Circular Fractal Antenna ijcisjournal
A novel miniaturized circular fractal antenna is designed by inscribing circular slot on rectangular ground
plane and successively forming circular rings connected by semi-circles for circular-fractal patch. Novel
modified Coplanar Waveguide (CPW) is used as feed for fractal circular patch. The analysis of parametric
variations is performed by consecutive fractal iterations, varying the radius of inscribed circle of ground
plane, slots and different ground plane configurations. To further enhance gain and radiation pattern a
dual inverted L slots is included in ground plane. From the results it is evident that, the proposed fractal
antenna possesses triple bands at 1.8GHz, 3.5GHz and 5.5GHz. These bands are used in Digital
Communication Systems (DCS) (1.8GHz), IEEE 802.16d fixed WiMAX (3.5GHz) and IEEE 802.11a WLAN
(5.5GHz) applications.
Enhanced Optimization of Edge Detection for High Resolution Images Using Veri...ijcisjournal
dge Detection plays a crucial role in Image Processing and Segmentation where a set of algorithms aims
to identify various portions of a digital image at which a sharpened image is observed in the output or
more formally has discontinuities. The contour of Edge Detection also helps in Object Detection and
Recognition. Image edges can be detected by using two attributes such as Gradient and Laplacian. In our
Paper, we proposed a system which utilizes Canny and Sobel Operators for Edge Detection which is a
Gradient First order derivative function for edge detection by using Verilog Hardware Description
Language and in turn compared with the results of the previous paper in Matlab. The process of edge
detection in Verilog significantly reduces the processing time and filters out unneeded information, while
preserving the important structural properties of an image. This edge detection can be used to detect
vehicles in Traffic Jam, Medical imaging system for analysing MRI, x-rays by using Xilinx ISE Design
Suite 14.2.
Impedance Cardiography Filtering Using Non-Negative Least-Mean-Square Algorithmijcisjournal
In general using several signal acquisition methods are applied to get cardio-impedance signal to analyse
the cardiac output. The analysis completely based on frequency information obtained after applying
frequency selection filters and frequency shaping filters. Here proposing a constructive approach involves
a developed Non-Negative LMS (NNLMS) followed by filtering techniques to measure and overcome the
limitations of commonly used approaches. The proposed technique performance is analysed by considering
different types of noise environments like fundamental one white noise and also sum of sinusoidal noise.
The simulation results are useful to measure the performance and accuracy under different noise
environments also a comparative analysis is done with the proposed work with existing methods under
different performance metrics by the help of quantitative analysis of algorithms. Simulation results are
found to be satisfactory in the analysis of cardiac output.
International Journal on Cryptography and Information Security ( IJCIS)ijcisjournal
International Journal on Cryptography and Information Security ( IJCIS) is an open access peer reviewed journal that focuses on cutting-edge results in applied cryptography and Information security. It aims to bring together scientists, researchers and students to exchange novel ideas and results in all aspects of cryptography, coding and Information security.
AUTOMATIC TRANSLATION OF ARABIC SIGN TO ARABIC TEXT (ATASAT) SYSTEMcscpconf
Sign language continues to be the preferred tool of communication between the deaf and the hearing-impaired. It is a well-structured code by hand gesture, where every gesture has a specific meaning, In this paper has goal to develop a system for automatic translation of Arabic Sign Language. To Arabic Text (ATASAT) System this system is acts as a translator among deaf and dumb with normal people to enhance their communication, the proposed System consists of five main stages Video and Images capture, Video and images processing, Hand Signs Construction, Classification finally Text transformation and interpretation, this system depends on building a two datasets image features for Arabic sign language gestures alphabets from two resources: Arabic Sign Language dictionary and gestures from different signer's human, als using gesture recognition techniques, which allows the user to interact with the outside world.This system offers a novel technique of hand detection is proposed which detect and extract hand gestures of Arabic Sign from Image or video, in this paper we use a set of appropriate features in step hand sign construction and classification of based on different classification algorithms such as KNN, MLP, C4.5, VFI and SMO and compare these results to get better classifier.
A SIGN LANGUAGE RECOGNITION APPROACH FOR HUMAN-ROBOT SYMBIOSISijcseit
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”.
A SIGN LANGUAGE RECOGNITION APPROACH FOR HUMAN-ROBOT SYMBIOSISijcseit
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”.
A Translation Device for the Vision Based Sign Languageijsrd.com
The Sign language is very important for people who have hearing and speaking deficiency generally called Deaf and Mute. It is the only mode of communication for such people to convey their messages and it becomes very important for people to understand their language. This paper proposes the method or algorithm for an application which would help in recognizing the different signs which is called Indian Sign Language. The images are of the palm side of right and left hand and are loaded at runtime. The method has been developed with respect to single user. The real time images will be captured first and then stored in directory and on recently captured image and feature extraction will take place to identify which sign has been articulated by the user through SIFT(scale invariance Fourier transform) algorithm. The comparisons will be performed in arrears and then after comparison the result will be produced in accordance through matched key points from the input image to the image stored for a specific letter already in the directory or the database the outputs for the following can be seen in below sections. There are 26 signs in Indian Sign Language corresponding to each alphabet out which the proposed algorithm provided with 95% accurate results for 9 alphabets with their images captured at every possible angle and distance.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
Translation of sign language using generic fourier descriptor and nearest neighbour
1. International Journal on Cybernetics & Informatics ( IJCI) Vol.3, No.1, February 2014
DOI: 10.5121/ijci.2014.3104 31
TRANSLATION OF SIGN LANGUAGE USING GENERIC
FOURIER DESCRIPTOR AND NEAREST NEIGHBOUR
Abidatul Izzah 1
and Nanik Suciati 2
1,2
Informatics Department, Institute Teknology Sepuluh Nopember, Indonesia
ABSTRACT
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.
KEYWORDS
Sign Language, Generic Fourier Descriptor, K-Nearest Neighbour, Image Processing
1. INTRODUCTION
The sign language is the fundamental communication between deaf people. Hand gestures
language and dynamic movement plays an important role in deaf learning and their
communication. The dynamic movement from the deaf people in sign language can means such
as greeting hello or good-bye. Moreover, we can divide gestures into static gestures and dynamic
gestures. If static gesture only use hand configuration, dynamic gesture use moving gesture by
sequence and the hand configuration [1]. There are also two types of gesture interaction:
communicative gestures, which work as a symbolic language and manipulative gestures, which
provide multi-dimensional control. Sign language is not universal. Different countries have
different sign languages in alphabets and word sets, for example American has American Sign
Language (ASL) and German has German Sign Language (GSL). The similarity is that sign
languages use body movements, right hand, left hand, or both. Signers may also utilize their
heads, eyes, and facial expressions [2].
The studies in hand gesture began in the 90s, so that many researches have been developed in
sign language recognition. Ding in [3] discovers an innovative algorithm for obtaining the
motion, hand shape and Place of Articulation (POA) of the manual sign. Hand shape, motion, and
POA are manual component from ASL, whereas facial expressions are non-manual component.
POA can identify the location of the hand with respect to the face and torso of the signer. The
motion by the dominant hand is detected from video starts to ends. The dominant hand is single
handed that usually signer used. Most of signers usually left hand as dominant hand, but in
2. International Journal on Cybernetics & Informatics ( IJCI) Vol.3, No.1, February 2014
32
several cases there are right-handed people. Oz in [2] has developed a reliable adaptive filtering
system with a Recurrent Neural Network (RNN). It used to determine the duration of ASL
signing. By using histogram method to extract features from signs and neural network to classify,
the system can convert ASL signs into English words. Other study, Munib in [1] presented a
developing of a system for automatic translation of static gestures of alphabets and signs in ASL.
By using Hough transform and neural networks, the system trained images to recognize signs.
The interesting is that system does not rely on using any gloves or visual markings to recognize
so it makes the system more flexible. Besides that research in real time sign language recognition
is highly essential. According to [4], there are several issues affecting Real-time sign language
recognition, i.e. (i) video image acquisition process depends on environmental conditions like
lighting sensitivity, and background condition, (ii) Detecting bounding box as hand shape
boundary should be determined automatically from the captured video streaming data, (iii) A sign
is affected by the preceding and the subsequent sign. Moreover, in this study we will translate the
sign language based on ASL into text form in real time. Although many studies used various
method have been done, we still must explore other technique to find a better one which can
translates sign language correctly.
In image processing, we firstly need to extract features from image to go to next process. One of
the techniques to feature extraction is Generic Fourier Description (GFD). GFD which is derived
by applying 2D Fourier Transform is robust in shape descriptor because it captures spectral
feature in both radial and circular direction [5]. In other hand, K-Nearest Neighbour (KNN) is a
simple classifier that easy to compute. By using a similarity measure, KNN can determine a label
of testing data. Because of their good ability, we proposed GFD and KNN algorithm to translate
sign language. By using this hybrid method, we hope that it provides better experiment result than
the existing method. Finally, this paper aims to create a system that can translate static sign
language into textual form automatically based on computer vision using GFD as feature
extraction method and KNN as classification approach. Sign language that will be recognized is
based on ASL. Translation process has three phase, they are segmentation, feature extraction, and
recognition. In this system, signers are not required to wear any gloves or to use any devices. To
know the performance, the system tested in 120 stored images in database and 120 images which
is captured real time by webcam. We also translated 5 words in video sequences.
2. RESEARCH METHOD
2.1. Data
Sign language used based on static ASL which involves the discrete set of 24 signs corresponding
to the letters of the alphabet (without two letters, J and Z). These are shown in Fig 1. The dataset
is divided into training and testing data afterward. The dataset used for training in recognition
process consists of 360 images, i.e. 15 images for each static signs. The images for training
process were taken from 5 different volunteers. The data set used for testing in recognition
process consists of 240 images, i.e. 120 stored images in database and 120 images which is
captured by webcam. Over all, for each sign, 15 out of 25 captured images were used for training
purpose, while the remaining 10 signs were used for testing.
2.2. Skin Detection
In images and videos, skin colour is an indication of the existence of hand shape or human in
video. Skin detection aims to segment skin area or hand shape as foreground from background.
Skin colour segmented from RGB colour space. RGB colour space is the most commonly used
colour space in digital images. It encodes colours as a combination red (R), green (G) and blue
3. International Journal on Cybernetics & Informatics ( IJCI) Vol.3, No.1, February 2014
33
(B) as of three primary colours. In fact, distances in the RGB space is not linearly correspond to
human perception. In this colour space, luminance and chrominance are not separated. The
luminance of a given RGB pixel is a linear combination of the R, G, and B values [6]. In skin
detection, we should transform RGB colour space into YCbCr to retain the luminance. The
YCbCr is a family of colour spaces used as a part of the colour image popeline in video. In this
colour space Y is the luma component and Cb and Cr are the blue-difference and red-difference
chroma components. Transforming from RGB to YCbCr compute based on Eq 1
A B C D E
F G H I K
L M N O P
Q R S T U
V W X Y
Fig 1. Static American Sign Language
ܻ = 0,299 ∗ ܴ + 0,587 ∗ ܩ + 0,114 ∗ ܤ
ܾܥ = − 0,1687 ∗ ܴ − 0,3312 ∗ ܩ + 0,5 ∗ ܤ
ݎܥ = 0,5 ∗ ܴ – 0,4183 ∗ – ܩ 0,0816 ∗ )1( ܤ
After transforming YCbCr, pixels with certain criteria will be detected as foreground. We used
simple criteria to detect skin colour, that are Cr on interval [132,173] or Cb on interval [76,126]
[7]. Skin detection processing shown in Fig 2. Fig 2(b) shows transformation result into YCbCr
colour space. Fig 2(c) is result of skin area detection. Fig 2(d) is transformation result from
YCbCr into RGB. Transforming back from YCbCr into RGB we use Eq 2
4. International Journal on Cybernetics & Informatics ( IJCI) Vol.3, No.1, February 2014
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(a) (b) (c) (d)
Figure 2. Skin Detection Process
ܴ = ܻ + 1,4022 ∗ ݎܥ
ܩ = ܻ – 0,3456 ∗ – ܾܥ 0,7145 ∗ ݎܥ
ܤ = ܻ + 1,7710 ∗ )2( ܾܥ
A custom user interface is created using MATLAB 2009a software to facilitate an efficient
acquisition of training samples. Sign from hand shape is recognized from significant fingers.
Significant fingers are captured from video sequence of the signer which is paused [3]. In this
work, we position the camera facing the signer in order to capture the front view of the signer’s
hand shape. Actually, moving hand detection has the same approach as simple skin detection. It
starts to detect skin area and determine the bounding box area. If the image moves, the bounding
box will follows until the acquisition process is finished. Fig 3 shows a motion object being
detected as hand shape.
(a) (b)
Figure 3. Motion Detection Process
2.3. Generic Fourier Descriptor
Zhang in [5] explained that shape description is one of the key parts of image content description
for image retrieval. Most of the existing shape descriptors are usually either application
dependent or non-robust, making them undesirable for generic shape description. GFD is
proposed to overcome the drawbacks of the existing shape representation techniques that
independent and robust. Generally, 1-D Fourier Descriptor (FD) is obtained through Fourier
transform (FT) on a shape signature function derived from shape boundary coordinates
{ݔ(ݐ), ݕ(ݐ), ݐ = 0,1,2, … , ܰ − 1}. GFD is derived by applying 2-D Fourier transform on a
polar shape image. In region based techniques, shape descriptors are derived using all the pixel
information within a shape region.
Given a shape image ܫ = {݂(,ݔ ;)ݕ 0 < ݔ < ,ܯ 0 < ݕ < ܰ}. Then, image I is converted from
cartesian space to polar space ܫ = {݂(,ݎ ߠ); 0 < ݎ < ܴ, 0 < ߠ < 2ߨ }, where R is the
maximum radius of the shape. The origin of the polar space is set to be the central of the shape, so
that the shape is translation invariant. The centroid (ݔ, ݕ)is given by Eq. 3.
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ݔ =
1
ܰ
ݔ
ேିଵ
௫ୀ
, ݕ =
1
ܯ
ݕ
ெିଵ
௬ୀ
(3)
where distance r and angle θ can be calculated based on Eq. 4.
ݎ = ඥ(ݔ − ݔ)ଶ + (ݕ − ݕ)ଶ, ߠ = ܽ݊ܽݐܿݎ
ݕ − ݕ
ݔ − ݔ
(4)
Moreover, if PF is polar Fourier transform, R is radial frequency resolution and T is angular
frequency resolution, m is number of radial frequency maximum and n is number of angular
frequency, we obtained PF based on Eq. 5 and shape descriptor based on Eq. 6.
ܲ,ߩ(ܨ ∅) = ݂(,ݎ ߠ)exp [݆2ߨ(
ݎ
ܴ
ߩ +
2ߨ
ܶ
∅)]
(5)
ܦܨܩ = ቊ
ܲ)0,0(ܨ
2ߨݎଶ
,
ܲ)1,0(ܨ
ܲ)0,0(ܨ
, … ,
ܲ,݉(ܨ ݊)
ܲ)0,0(ܨ
ቋ (6)
2.4. Translation of Sign Language Using GFD and KNN
The first step of translation of sign language using GFD and KNN is pre processing. In pre
processing step, we should detecting and cropping the skin area, resizing, and also filtering the
image. After skin detection, cropping is applied based on a bounding box as the boundary that lies
from upper left corner to bottom right corner of the skin area. The pre processing result is shown
in Fig 4. Fig 4(b) shows the result of skin area detection or hand shape detection based on Section
2.2 and Fig 4(c) shows the generated bounding box as the boundary of the skin area. After that,
the images must be resized into a smaller size for an easier computation. In this experiment we
resize the images into 20% of the original size. Fig 4(e) shows the resized image. In pre
processing, we also need to filter the sign. A moving average or median filter mask 3x3 is used to
remove the unwanted noise from the image scenes. Fig 4(f) shows the image after filtering.
(a) (b) (c)
(d) (e) (f)
Fig 4. Pre processing
After pre processing, we extract image features using GFD method. Feature extraction is applied
on binary image so canny method is used in order to detect the edges. The edge detection result is
shown in Fig 5(b). In section 2.3, we also consider m radial frequency and n angular frequency to
6. International Journal on Cybernetics & Informatics ( IJCI) Vol.3, No.1, February 2014
36
obtained (nT + m + 1) features representing an image. In this paper, we choose 4 as radial
frequency and 6 as angular frequency.
(a) (b)
Fig 5. Edge Detection Process
Then, (nT + m + 1) features of each image are processed and converted to a features vector for the
training set. For recognition task, we use K Nearest Neighbour (KNN) to classify the testing data.
In KNN classification, similarity measures used are Euclidean Distance and Cosine Similarity.
Given two pixel a(xଵ, yଵ) and b(xଶ, yଶ) similarity measures can be calculated by Eq (6) and (7).
d = ඥ(xଶ − xଵ)ଶ + (yଶ − yଵ)ଶ (6)
cosine (a, b) =
ቀ
xଵ
yଵ
ቁ . ቀ
xଶ
yଶ
ቁ
ቚቀ
xଵ
yଵ
ቁቚ ቚቀ
xଶ
yଶ
ቁቚ
(7)
Finally, we evaluate the performance of the system based on its ability to classify samples to their
corresponding classes correctly. The recognition rate is defined as the ratio of the number of
correctly classified samples to the total number of samples. Over all, the procedure of this method
is shown in Fig 6.
Fig 6. Flowchart Translation of Sign Language Using GFD and KNN
Database
Bounding BoxSkin
Detection
Segmentation
Classification
Cropping &
Resizing
Median
Filtering
Feature
Vector
GFD Feature Extraction
Output KNN
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3. Result and Analysis
3.1 Experiment result
The system was implemented in MATLAB version 2009a. We implemented it in two scenarios.
In the first scenario, we used 360 images for the training process and 240 images for testing that
each sign alphabet we have trained 15 images for training and 10 images for testing. Based on
this scenario, we will know how system performance in recognizing for each sign. In the second
scenario, we used real time images by capturing several sign sequences. Based on this scenario,
we will know how system performance in translating word.
In first scenario, we recognized signs twice. Firstly, we load 5 stored images for each sign in
database and secondly we capture 5 images for each sign by real time using webcam. When
capturing signs, the system would detect motion object as hand shape. After that, the captured
image would be pre processed and compared with the feature vector of the training data. Fig 7
shows how to capture and crop the hand shape area. In this experiment, we used KNN method to
classify the data testing by using Euclidean Distance and Cosine Similarity. For KNN’s
parameter, we choose k = 1, 3, 5 to obtained k nearest Neighbours. The testing results for both
stored image recognition and real time image recognition are shown in Table 1.
(a) (b)
Fig 7. Capturing Real Time
Table 1. Result of Sign Recognition
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Finally, we obtained 86 % accuracy as the highest result when using Euclidean Distance
similarity and k = 1 for testing in stored images and 69 % accuracy when using Euclidean
Distance similarity and k = 3 for testing in real time images. Result of recognition rate between
first recognition and second one shown in Fig 8. Although the first recognition has a good
performance, it cannot be guaranteed that the system is good enough. In fact, the second
recognition has low accuracy. It may caused by the number of training data is too small that does
not have different reasonable orientations.
(a) (b)
Fig 8. Recognized Rate of Sign Recognition
In the second scenario, we used real time images by capturing several signs sequences. We used
several words consisting of 2 to 4 letters to test the system, i.e. ‘HI’, ‘AYO’, ‘AKU’, ‘KAMU’,
and ‘HALO’. The experiment result show that the system still could not recognized each letter in
word. The word ‘HI’ could only be translated correctly. The result of this scenario is shown in
Table 2.
Table 2. Result of Word Translation
Sign
METHOD
Euclidian Cosine
k=1 k=3 k=5 k=1 k=3 k=5
HI HI HI HI HI HI HI
AYO TYA TYA AYA TYA TYA AYA
AKU TKB TKB IKU TKF TKF YKU
KAMU KAEU KAEU KAEU KAMU KATU KATK
HALO HTLA HTLA HTLA BTLA HTLA HTLA
We also used PCA for comparing the performance of our method. The performance of the PCA
model was tested in 24 static signs in the ASL alphabet using Euclidean Distance and k =1,3,5.
The results are given in Figure 9.
74
76
78
80
82
84
86
88
1 3 5
RecognitionRate(%)
k value
Result of Sign Recognition in
Stored Images
52
54
56
58
60
62
64
66
68
70
1 3 5
RecognitionRate(%)
k value
Result of Sign Recognition in
Real Time
Euclidian
Distance
Cosine
Similarity
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3.2 Discussion
In scenario 1, we translated 24 signs into alphabet. In the first recognition by using stored images
as testing data, we obtained good recognized rate. But, in the second one, recognition by using
real time images as testing data, we obtained a less accura
came across some misclassifications. In ‘C’ and ‘L’ case, the system recognized the sign with
100% accuracy. However, in ‘A’, ‘M’, ‘N’, and ‘T’ case, it could only achieve under 50 %
accuracy. It may be caused by their
recognized it as ‘M’ and vice versa. Moreover, in ‘E’ and ‘S’ case, system could not recognized
at all. From different directions, some signs looked like different signs e.g. ‘A’ looked like ‘E’ i
the thumb was blurred and the edge was significantly undetected. In other hand, ‘S’ looked like
‘A’ if the index and middle fingers were not raised highly enough. Actually, these problems may
cloud be solved by using a better skin detection and filtering
try to focus on in future research. An example of poor skin detection and filtering is shown in Fig
10. Fig 10(b) shows the result of skin detection in poor lighting condition. In such condition, the
skin area might have the same luminosity as the background (in this case a wall) to an extent that
some skin area failed to be segmented. Fig
could only blur little area to reduce noise in
used bigger mask, some edges of the fingers would fade. It would be ideal if we could fill the
missing skin area in the center of the image.
(a)
Fig 10
In scenario 2, we obtained low recognition rate. Overall, the previous problems were also found
in this scenario. Additionally, various backgrounds did not only have the same properties as the
skin, but also contains noises. It was seriously difficult to d
the captured images that contain noises in the background. Besides that, lighting also affected the
0
20
40
60
80
100
A B C D E F G
RecognitionRate(%)
International Journal on Cybernetics & Informatics ( IJCI) Vol.3, No.1, February 2014
Fig 9. Comparison Result
In scenario 1, we translated 24 signs into alphabet. In the first recognition by using stored images
as testing data, we obtained good recognized rate. But, in the second one, recognition by using
real time images as testing data, we obtained a less accuracy. As shown in the result table, we
came across some misclassifications. In ‘C’ and ‘L’ case, the system recognized the sign with
100% accuracy. However, in ‘A’, ‘M’, ‘N’, and ‘T’ case, it could only achieve under 50 %
accuracy. It may be caused by their similarity, so when we captured ‘A’ sign, the system might
recognized it as ‘M’ and vice versa. Moreover, in ‘E’ and ‘S’ case, system could not recognized
at all. From different directions, some signs looked like different signs e.g. ‘A’ looked like ‘E’ i
the thumb was blurred and the edge was significantly undetected. In other hand, ‘S’ looked like
‘A’ if the index and middle fingers were not raised highly enough. Actually, these problems may
cloud be solved by using a better skin detection and filtering method, which the researcher might
try to focus on in future research. An example of poor skin detection and filtering is shown in Fig
(b) shows the result of skin detection in poor lighting condition. In such condition, the
the same luminosity as the background (in this case a wall) to an extent that
some skin area failed to be segmented. Fig 10(c) is the result of image filtering using mask. Mask
could only blur little area to reduce noise in bounding hand, but not in central one. If we have
used bigger mask, some edges of the fingers would fade. It would be ideal if we could fill the
missing skin area in the center of the image.
(a) (b) (c)
10. Poor skin detection and filtering image
In scenario 2, we obtained low recognition rate. Overall, the previous problems were also found
in this scenario. Additionally, various backgrounds did not only have the same properties as the
skin, but also contains noises. It was seriously difficult to detect hand shape of the hand shape of
the captured images that contain noises in the background. Besides that, lighting also affected the
G H J K L M N O P Q R S T U V W X Y
Sign
Comparison Result
International Journal on Cybernetics & Informatics ( IJCI) Vol.3, No.1, February 2014
39
In scenario 1, we translated 24 signs into alphabet. In the first recognition by using stored images
as testing data, we obtained good recognized rate. But, in the second one, recognition by using
cy. As shown in the result table, we
came across some misclassifications. In ‘C’ and ‘L’ case, the system recognized the sign with
100% accuracy. However, in ‘A’, ‘M’, ‘N’, and ‘T’ case, it could only achieve under 50 %
similarity, so when we captured ‘A’ sign, the system might
recognized it as ‘M’ and vice versa. Moreover, in ‘E’ and ‘S’ case, system could not recognized
at all. From different directions, some signs looked like different signs e.g. ‘A’ looked like ‘E’ if
the thumb was blurred and the edge was significantly undetected. In other hand, ‘S’ looked like
‘A’ if the index and middle fingers were not raised highly enough. Actually, these problems may
method, which the researcher might
try to focus on in future research. An example of poor skin detection and filtering is shown in Fig
(b) shows the result of skin detection in poor lighting condition. In such condition, the
the same luminosity as the background (in this case a wall) to an extent that
(c) is the result of image filtering using mask. Mask
one. If we have
used bigger mask, some edges of the fingers would fade. It would be ideal if we could fill the
In scenario 2, we obtained low recognition rate. Overall, the previous problems were also found
in this scenario. Additionally, various backgrounds did not only have the same properties as the
etect hand shape of the hand shape of
the captured images that contain noises in the background. Besides that, lighting also affected the
Y
PCA
GFD
10. International Journal on Cybernetics & Informatics ( IJCI) Vol.3, No.1, February 2014
40
image capture significantly. Fig 11 shows the result of the pre processing of real time image
capture. The edge image, as shown in Fig 11(d), does not resemble hand shapes well due to the
skin detection and filtering method used in this experiment.
(a) (b) (c) (d)
Fig 11. Poor preprocess real time image
Comparing with PCA, the proposed method had a better performance in 81.39% (GFD-KNN)
compared with 69.86% (PCA-KNN). Although PCA had better result in some signs, such as A,
B, D, and G, but over all GFD’ performance is better than PCA’s.
4. Conclusion
In this project, we developed a system with the purpose of translating ASL alphabet. The system
has three phases, i.e. segmentation, feature extraction, and recognition. Without the need of any
gloves, an image of a sign can be taken with a webcam. The proposed system was able to reach a
recognition accuracy of about 86% for testing data in stored images and 69% for testing data in
real time images. We also translated 5 words in video sequences and obtained low recognition
rate in word translation. In fact, we have some problem in skin detection process that caused
some skin area failed to be segmented and affected in recognition rate. Actually, in the future, the
researcher might try to focus in these problems by using a better skin detection and filtering
method.
REFERENCES
[1] Munib Q, Habeeb M, Takruri B, Al-Malik H. American sign language (ASL) recognition based on
Hough transform and neural networks, Expert Systems with Applications. 2007; 32; 24–37
[2] Oz C, Leu M. American Sign Language word recognition with a sensory glove using artificial neural
networks. Engineering Applications of Artificial Intelligence. 2011; 24; 1204–1213
[3] Ding L, Martinez A. Modelling and recognition of the linguistic components in American Sign
Language. Image and Vision Computing. 2009; 27; 1826–1844
[4] Mekala P, Gao Y, Fan J, Davari A. Real-time Sign Language Recognition based on Neural Network
Architecture. IEEE. 2011, 978-1-4244-9592-4/
[5] Zhang D, Lu G. Generic Fourier Descriptor for Shape-based Image Retrieval. 2002
[6] Elgammal A, Muang C, Hu D. Skin Detection - a Short Tutorial, Encyclopedia of Biometrics by
Springer-Verlag Berlin Heidelberg. 2009; 1-10
[7] http://www.mathworks.com/matlabcentral/fileexchange/28565-skin-detection
11. International Journal on Cybernetics & Informatics ( IJCI) Vol.3, No.1, February 2014
Authors
Abidatul Izzah, received her B.S. degree, in Mathematics,
Airlangga University, Indonesia in 2012. Currently she has been
studying in Informatics Engineering, at the third semesster of Master
Degree in Institut Teknologi Sepuluh Nopember (ITS), Indonesia. Her
research interests include data mining, artificia
swarm intelligence.
Nanik Suciati received the B.S. in Computer Engineering from
Teknologi Sepuluh Nopember (ITS), Indonesia in 1994 and M.S.
degrees in Computer Science from University of Indonesia in 1998,
and PhD from Hiroshima University, Japan in 2010. Since 1994
present, she is an academic and researcher staff in Informatics
Department, ITS, Indonesia. Her research interests include Computer
Graphics, Computer Vision and Image Processing.
International Journal on Cybernetics & Informatics ( IJCI) Vol.3, No.1, February 2014
, received her B.S. degree, in Mathematics, from
Airlangga University, Indonesia in 2012. Currently she has been
studying in Informatics Engineering, at the third semesster of Master
Degree in Institut Teknologi Sepuluh Nopember (ITS), Indonesia. Her
research interests include data mining, artificial intelligence, and
received the B.S. in Computer Engineering from Institut
(ITS), Indonesia in 1994 and M.S.
degrees in Computer Science from University of Indonesia in 1998,
m Hiroshima University, Japan in 2010. Since 1994-
present, she is an academic and researcher staff in Informatics
Department, ITS, Indonesia. Her research interests include Computer
Graphics, Computer Vision and Image Processing.
International Journal on Cybernetics & Informatics ( IJCI) Vol.3, No.1, February 2014
41