Pub Date : 2013-12-02DOI: 10.1109/ICITEED.2013.6676206
Syna Sreng, Noppadol Maneerat, D. Isarakorn, B. Pasaya, J. Takada, Ronakorn Panjaphongse, R. Varakulsiripunth
Diabetic Retinopathy (DR) is the most common cause of blindness in diabetic patients, but early detection and timely treatment can prevent this problem. Exudates have been found to be one of the signs and serious DR anomalies so the proper detection of these lesions and the treatment should be done immediately to prevent loss of vision. The aim of this study is to automatically detect these lesions in fundus images. To achieve this goal, the proposed method first preprocesses to improve the quality of fundus image, and then Optic Disc (OD) is detected and eliminated to prevent the interference to the result of exudate detection by combination of 3 methods; image binarization, Region Of Interest (ROI) based segmentation and Morphological Reconstruction (MR). Next, exudates are detected by applying the maximum entropy thresholding to filter out the bright pixels from the result of OD region eliminated. Since the result contains some noises which appear as bright light at the edge of fundus area in some images, that affect is considered and eliminated to improve the result of false positive. Finally, exudates are extracted by using MR. The proposed technique has been tested on 100 fundus images from hospital. Experimental results show that 91 % of exudate is extracted correctly with the average process of 3.92 second per image.
{"title":"Automatic exudate extraction for early detection of Diabetic Retinopathy","authors":"Syna Sreng, Noppadol Maneerat, D. Isarakorn, B. Pasaya, J. Takada, Ronakorn Panjaphongse, R. Varakulsiripunth","doi":"10.1109/ICITEED.2013.6676206","DOIUrl":"https://doi.org/10.1109/ICITEED.2013.6676206","url":null,"abstract":"Diabetic Retinopathy (DR) is the most common cause of blindness in diabetic patients, but early detection and timely treatment can prevent this problem. Exudates have been found to be one of the signs and serious DR anomalies so the proper detection of these lesions and the treatment should be done immediately to prevent loss of vision. The aim of this study is to automatically detect these lesions in fundus images. To achieve this goal, the proposed method first preprocesses to improve the quality of fundus image, and then Optic Disc (OD) is detected and eliminated to prevent the interference to the result of exudate detection by combination of 3 methods; image binarization, Region Of Interest (ROI) based segmentation and Morphological Reconstruction (MR). Next, exudates are detected by applying the maximum entropy thresholding to filter out the bright pixels from the result of OD region eliminated. Since the result contains some noises which appear as bright light at the edge of fundus area in some images, that affect is considered and eliminated to improve the result of false positive. Finally, exudates are extracted by using MR. The proposed technique has been tested on 100 fundus images from hospital. Experimental results show that 91 % of exudate is extracted correctly with the average process of 3.92 second per image.","PeriodicalId":204082,"journal":{"name":"2013 International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116678086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-02DOI: 10.1109/ICITEED.2013.6676254
D. Perdana, R. F. Sari
One of the most challenging issues in IEEE 1609.4 is the assurance of Quality of Service (QoS), i.e. to improve throughput and reduce delay in the sublayer Medium Access Control (MAC) IEEE 1609.4. The prioritization of each service package, using Enhanced Distributed Channel Access (EDCA) at the MAC sublayer is designed based on the IEEE 802.11e with some modifications to the transmission parameters.
{"title":"Performance comparison of IEEE 1609.4/802.11p and 802.11e with EDCA implementation in MAC sublayer","authors":"D. Perdana, R. F. Sari","doi":"10.1109/ICITEED.2013.6676254","DOIUrl":"https://doi.org/10.1109/ICITEED.2013.6676254","url":null,"abstract":"One of the most challenging issues in IEEE 1609.4 is the assurance of Quality of Service (QoS), i.e. to improve throughput and reduce delay in the sublayer Medium Access Control (MAC) IEEE 1609.4. The prioritization of each service package, using Enhanced Distributed Channel Access (EDCA) at the MAC sublayer is designed based on the IEEE 802.11e with some modifications to the transmission parameters.","PeriodicalId":204082,"journal":{"name":"2013 International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120859902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-02DOI: 10.1109/ICITEED.2013.6676274
O. Qudsi, N. Windarko, A. Priyadi, M. Purnomo
This paper presents analytical techniques for reducing switching losses of voltage source inverter (VSI) using Generalized Discontinuous PWM (GDPWM). The switching losses of inverter is influenced by the angle on the modulation of GDPWM. This problem will be optimized using a new optimization method. This method is called as Spontaneous Evolutionary GA (SEGA). The inverter switching losses is formulated as objective function to optimize the angle. At this optimization process, angle values will be determined to minimize the inverter switching losses. Thermal module of Power Simulator (PSIM) is used to verify the optimized angle of GDPWM. The simulation was performed using a three-phase voltage source inverter (VSI) and an inductive load. Simulation results confirm the method could minimize the losses of inverter.
{"title":"Optimized GDPWM based on Spontaneous Evolutionary GA for reducing switching losses on inverter","authors":"O. Qudsi, N. Windarko, A. Priyadi, M. Purnomo","doi":"10.1109/ICITEED.2013.6676274","DOIUrl":"https://doi.org/10.1109/ICITEED.2013.6676274","url":null,"abstract":"This paper presents analytical techniques for reducing switching losses of voltage source inverter (VSI) using Generalized Discontinuous PWM (GDPWM). The switching losses of inverter is influenced by the angle on the modulation of GDPWM. This problem will be optimized using a new optimization method. This method is called as Spontaneous Evolutionary GA (SEGA). The inverter switching losses is formulated as objective function to optimize the angle. At this optimization process, angle values will be determined to minimize the inverter switching losses. Thermal module of Power Simulator (PSIM) is used to verify the optimized angle of GDPWM. The simulation was performed using a three-phase voltage source inverter (VSI) and an inductive load. Simulation results confirm the method could minimize the losses of inverter.","PeriodicalId":204082,"journal":{"name":"2013 International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123547940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-02DOI: 10.1109/ICITEED.2013.6676226
Heri Setiawan, Iwan Setyawan, Saptadi Nugroho
Various methods of hand gesture recognition have been proposed in the literature, with high recognition rate. But implementing these methods in embedded system is still challenging since image processing applications needs a high-performance processor. In this paper, a hand gesture recognition system is implemented on a system with an OK6410B board. This board has a processor that runs at 532 MHz, which is relatively high for a small processor. The hand gesture recognition method proposed in this paper is based on the Neural Network Shape Fitting. In this paper we propose some modifications to this method. The modifications were pixel randomizing during the initialization step, addition of several neurons in the iterations, using lookup table for distance measurement and simplification of the finger detection. These modifications yielded a faster processing time (0.95s on the OK6410B) and a higher recognition rate (94.44% using still images as input and 84.53% using live input from a webcam).
{"title":"Hand gesture recognition using Optimized Neural Network Shape Fitting on ARM11","authors":"Heri Setiawan, Iwan Setyawan, Saptadi Nugroho","doi":"10.1109/ICITEED.2013.6676226","DOIUrl":"https://doi.org/10.1109/ICITEED.2013.6676226","url":null,"abstract":"Various methods of hand gesture recognition have been proposed in the literature, with high recognition rate. But implementing these methods in embedded system is still challenging since image processing applications needs a high-performance processor. In this paper, a hand gesture recognition system is implemented on a system with an OK6410B board. This board has a processor that runs at 532 MHz, which is relatively high for a small processor. The hand gesture recognition method proposed in this paper is based on the Neural Network Shape Fitting. In this paper we propose some modifications to this method. The modifications were pixel randomizing during the initialization step, addition of several neurons in the iterations, using lookup table for distance measurement and simplification of the finger detection. These modifications yielded a faster processing time (0.95s on the OK6410B) and a higher recognition rate (94.44% using still images as input and 84.53% using live input from a webcam).","PeriodicalId":204082,"journal":{"name":"2013 International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"326 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126678296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-02DOI: 10.1109/ICITEED.2013.6676283
A. Yusuf, W. Widada, W. Taruno
In this research, we proposed a capacitance measurement circuit Electrical Capacitance Volume Tomography (ECVT) to perform three-dimensional image visualization. The ECVT system is consists of three main parts i.e. sensor, data acquisition system, and computer. Data acquisition system is composed of capacitance measurement circuit and microcontroller to measure an unknown capacitance inside the sensor, collect data and send it to the computer. Further, these data is used to reconstruct 3D image. The design of the circuit used a sine wave 14.6 Vp-p and 2.5 MHz of frequency injected to the electrode pair to measure an unknown capacitance inside the sensor. An experiment is performed using simulated phantom using sensor having the form of a half-sphere with combined triangular and rectangular shapes. The system is able to measure a capacitance value as low as four femto-Farads with 0.34% margin error.
{"title":"Design of capacitance measurement circuit for data acquisition system ECVT","authors":"A. Yusuf, W. Widada, W. Taruno","doi":"10.1109/ICITEED.2013.6676283","DOIUrl":"https://doi.org/10.1109/ICITEED.2013.6676283","url":null,"abstract":"In this research, we proposed a capacitance measurement circuit Electrical Capacitance Volume Tomography (ECVT) to perform three-dimensional image visualization. The ECVT system is consists of three main parts i.e. sensor, data acquisition system, and computer. Data acquisition system is composed of capacitance measurement circuit and microcontroller to measure an unknown capacitance inside the sensor, collect data and send it to the computer. Further, these data is used to reconstruct 3D image. The design of the circuit used a sine wave 14.6 Vp-p and 2.5 MHz of frequency injected to the electrode pair to measure an unknown capacitance inside the sensor. An experiment is performed using simulated phantom using sensor having the form of a half-sphere with combined triangular and rectangular shapes. The system is able to measure a capacitance value as low as four femto-Farads with 0.34% margin error.","PeriodicalId":204082,"journal":{"name":"2013 International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126981231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-02DOI: 10.1109/ICITEED.2013.6676278
Sarjiya, A. Mulyawan, Apri Setiawan, A. Sudiarso
Unit commitment (UC) is one of optimization problem which is important in electrical power systems as effort to minimize generation cost by applying an effective scheduling. However, the size of search space and many constraints in this problem are becoming the problems. This paper will present hybrid algorithm which integrates genetic algorithm (GA) combined with the principle of tabu search (TS) and priority list (PL) methods to solve the UC problem. PL will be used for solving the unit scheduled problem. GA and the principle of TS are used for solving the economic dispatch problem. To optimize GA parameters, design of experiment (DOE) method will be used. The proposed algorithm is tested on the IEEE 10 unit systems for a one day scheduling periods. The results are compared with methodological priority list, shuffled frog leaping algorithm, hybrid particle swarm optimization, standard GA, integer coded GA, and Lagrange relaxation GA methods. This proposed hybrid method shows that the total cost of the unit commitment problem is better than other compared methods and near-optimal solution.
{"title":"Thermal unit commitment solution using genetic algorithm combined with the principle of tabu search and priority list method","authors":"Sarjiya, A. Mulyawan, Apri Setiawan, A. Sudiarso","doi":"10.1109/ICITEED.2013.6676278","DOIUrl":"https://doi.org/10.1109/ICITEED.2013.6676278","url":null,"abstract":"Unit commitment (UC) is one of optimization problem which is important in electrical power systems as effort to minimize generation cost by applying an effective scheduling. However, the size of search space and many constraints in this problem are becoming the problems. This paper will present hybrid algorithm which integrates genetic algorithm (GA) combined with the principle of tabu search (TS) and priority list (PL) methods to solve the UC problem. PL will be used for solving the unit scheduled problem. GA and the principle of TS are used for solving the economic dispatch problem. To optimize GA parameters, design of experiment (DOE) method will be used. The proposed algorithm is tested on the IEEE 10 unit systems for a one day scheduling periods. The results are compared with methodological priority list, shuffled frog leaping algorithm, hybrid particle swarm optimization, standard GA, integer coded GA, and Lagrange relaxation GA methods. This proposed hybrid method shows that the total cost of the unit commitment problem is better than other compared methods and near-optimal solution.","PeriodicalId":204082,"journal":{"name":"2013 International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114326844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-02DOI: 10.1109/ICITEED.2013.6676201
Khaengkai Compapong, Sumonta Kasemvilas
In this paper, we clustered clinical risk data of a mental health service, Khon Kaen Rajanagarindra Psychiatric Hospital. This study aims to compare performance values of cluster (k) in k-means clustering algorithm and hierarchical clustering algorithm. The result shows that for k-means clustering algorithm, sum of squared error (SSE) is 32.68, minimum of distance (MD) is 1.38, mean squared error (MSE) is 2.95 and values of k is 11. Therefore, we found that k-means clustering algorithm is the most appropriate method for using in cluster the risk group of the Psychiatric Patient Service. The result also suggests that the most risky age is between the ages of 32 and 36. The result can be a guideline for further research about data prediction. The implications of this study can assist medical staff to be knowledgeable about what should beware of when they treat psychiatric patients and this can be basic planning medicate guidelines for medical staff.
{"title":"A comparison of effectiveness of risk data clustering method in Psychiatric Patient Service","authors":"Khaengkai Compapong, Sumonta Kasemvilas","doi":"10.1109/ICITEED.2013.6676201","DOIUrl":"https://doi.org/10.1109/ICITEED.2013.6676201","url":null,"abstract":"In this paper, we clustered clinical risk data of a mental health service, Khon Kaen Rajanagarindra Psychiatric Hospital. This study aims to compare performance values of cluster (k) in k-means clustering algorithm and hierarchical clustering algorithm. The result shows that for k-means clustering algorithm, sum of squared error (SSE) is 32.68, minimum of distance (MD) is 1.38, mean squared error (MSE) is 2.95 and values of k is 11. Therefore, we found that k-means clustering algorithm is the most appropriate method for using in cluster the risk group of the Psychiatric Patient Service. The result also suggests that the most risky age is between the ages of 32 and 36. The result can be a guideline for further research about data prediction. The implications of this study can assist medical staff to be knowledgeable about what should beware of when they treat psychiatric patients and this can be basic planning medicate guidelines for medical staff.","PeriodicalId":204082,"journal":{"name":"2013 International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"338 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133888406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-02DOI: 10.1109/ICITEED.2013.6676233
Achmad Arwan, Bayu Priyambadha, R. Sarno, Mohamad Sidiq, H. Kristianto
Foods recommendation for diabetes patients is indispensable for controlling blood sugar levels. Currently, the foods preparation is done by a nutrition expert. The patient's dependence on the nutrition experts is very high, thus the selection of foods could not be done independently. The Automation system to determine foods combination for diabetic patients is needed to solve these problems. In this study, the automation system has been designed and implemented. The technologies used in this research are the OWL and SWRL. There are few researches that explore an automation process of foods recommendation for diabetes patients using the technology of OWL and SWRL. Domain knowledge based on Ontology is needed to process foods composition automatically. However, using SWRL and OWL technology is not enough, because the accuracy of the words required. A semantic ontology understanding was added using weighted tree similarity method to specify the composition of foods for diabetic patients. 73% data were able to be correctly predicted by this method.
{"title":"Ontology and semantic matching for diabetic food recommendations","authors":"Achmad Arwan, Bayu Priyambadha, R. Sarno, Mohamad Sidiq, H. Kristianto","doi":"10.1109/ICITEED.2013.6676233","DOIUrl":"https://doi.org/10.1109/ICITEED.2013.6676233","url":null,"abstract":"Foods recommendation for diabetes patients is indispensable for controlling blood sugar levels. Currently, the foods preparation is done by a nutrition expert. The patient's dependence on the nutrition experts is very high, thus the selection of foods could not be done independently. The Automation system to determine foods combination for diabetic patients is needed to solve these problems. In this study, the automation system has been designed and implemented. The technologies used in this research are the OWL and SWRL. There are few researches that explore an automation process of foods recommendation for diabetes patients using the technology of OWL and SWRL. Domain knowledge based on Ontology is needed to process foods composition automatically. However, using SWRL and OWL technology is not enough, because the accuracy of the words required. A semantic ontology understanding was added using weighted tree similarity method to specify the composition of foods for diabetic patients. 73% data were able to be correctly predicted by this method.","PeriodicalId":204082,"journal":{"name":"2013 International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"48 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132974316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-02DOI: 10.1109/ICITEED.2013.6676242
Indriana Hidayah, A. E. Permanasari, Ning Ratwastuti
Conventional classroom is still the main learning method applied in undergraduate program of Electrical Engineering and Information Technology Department, Gadjah Mada University. There are several problems in this method, such as large amount of students and limited number of meetings making difficult to understand each student. Student classification is a way to solve the problem by mapping the condition of each student based on certain parameters. Many methods have been applied to classify students that are based on IF-THEN rules and pattern recognition. However, many studies were done on intelligent tutoring systems and e-learning systems, not in a conventional classroom. Moreover, there are no researches that measure basic values by considering intelligence and non-intelligence performances. In this work, a student classification model was developed by applying neuro fuzzy concept; a combination of fuzzy's IF-THEN rules and neural network's ability to learn, so this method has the ability to learn from the generated rules to produce the best classification model. The model can be used to predict students' academic performance. Data were processed using ANFIS Editor-Matlab Fuzzy Logic. The results showed that combination of three parameter values -interest, talent, and motivation- is the best model for students classification, which has training RMSE value 0.12301 and testing average RMSE value 0.25611.
{"title":"Student classification for academic performance prediction using neuro fuzzy in a conventional classroom","authors":"Indriana Hidayah, A. E. Permanasari, Ning Ratwastuti","doi":"10.1109/ICITEED.2013.6676242","DOIUrl":"https://doi.org/10.1109/ICITEED.2013.6676242","url":null,"abstract":"Conventional classroom is still the main learning method applied in undergraduate program of Electrical Engineering and Information Technology Department, Gadjah Mada University. There are several problems in this method, such as large amount of students and limited number of meetings making difficult to understand each student. Student classification is a way to solve the problem by mapping the condition of each student based on certain parameters. Many methods have been applied to classify students that are based on IF-THEN rules and pattern recognition. However, many studies were done on intelligent tutoring systems and e-learning systems, not in a conventional classroom. Moreover, there are no researches that measure basic values by considering intelligence and non-intelligence performances. In this work, a student classification model was developed by applying neuro fuzzy concept; a combination of fuzzy's IF-THEN rules and neural network's ability to learn, so this method has the ability to learn from the generated rules to produce the best classification model. The model can be used to predict students' academic performance. Data were processed using ANFIS Editor-Matlab Fuzzy Logic. The results showed that combination of three parameter values -interest, talent, and motivation- is the best model for students classification, which has training RMSE value 0.12301 and testing average RMSE value 0.25611.","PeriodicalId":204082,"journal":{"name":"2013 International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128895873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-02DOI: 10.1109/ICITEED.2013.6676236
Rudy Hartanto, A. Susanto, P. Santosa
Human computer interaction has a long history to become more intuitive. For human being, especially for the deaf, gesture of different kind is one of the most intuitive and common communication. In this paper we focus on creating a system to identified and translate hand gesture pose to Indonesian alphabets. Skin detections method is used to create a segmented hand image and to differentiate with the background. A contours is used to localize hand area. SIFT algorithm in advanced, were used to recognize the signed gesture. The result shows that this system can operate well in translated hand gesture image of sign into Indonesian alphabets.
{"title":"Preliminary design of static indonesian sign language recognition system","authors":"Rudy Hartanto, A. Susanto, P. Santosa","doi":"10.1109/ICITEED.2013.6676236","DOIUrl":"https://doi.org/10.1109/ICITEED.2013.6676236","url":null,"abstract":"Human computer interaction has a long history to become more intuitive. For human being, especially for the deaf, gesture of different kind is one of the most intuitive and common communication. In this paper we focus on creating a system to identified and translate hand gesture pose to Indonesian alphabets. Skin detections method is used to create a segmented hand image and to differentiate with the background. A contours is used to localize hand area. SIFT algorithm in advanced, were used to recognize the signed gesture. The result shows that this system can operate well in translated hand gesture image of sign into Indonesian alphabets.","PeriodicalId":204082,"journal":{"name":"2013 International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124389636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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