Pub Date : 2023-05-30DOI: 10.19101/tipcv.2023.924002
{"title":"Enhancing video encryption: AES and blowfish algorithms with random password generation","authors":"","doi":"10.19101/tipcv.2023.924002","DOIUrl":"https://doi.org/10.19101/tipcv.2023.924002","url":null,"abstract":"","PeriodicalId":243044,"journal":{"name":"ACCENTS Transactions on Image Processing and Computer Vision","volume":"93 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139372605","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 : 2023-02-20DOI: 10.19101/tipcv.2023.924001
{"title":"A review of blockchain cyber security","authors":"","doi":"10.19101/tipcv.2023.924001","DOIUrl":"https://doi.org/10.19101/tipcv.2023.924001","url":null,"abstract":"","PeriodicalId":243044,"journal":{"name":"ACCENTS Transactions on Image Processing and Computer Vision","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132284105","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 : 2022-02-20DOI: 10.19101/tipcv.2022.823001
{"title":"A review and analysis of digital image forensic techniques","authors":"","doi":"10.19101/tipcv.2022.823001","DOIUrl":"https://doi.org/10.19101/tipcv.2022.823001","url":null,"abstract":"","PeriodicalId":243044,"journal":{"name":"ACCENTS Transactions on Image Processing and Computer Vision","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121549256","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 : 2020-11-15DOI: 10.19101/tipcv.2020.618054
A. Z. Mesquita, Adriano de Almeida Massaud Felippe, A. M. Lage, P. A. M. Ribeiro
Nuclear Technology Development Center (CDTN) offers the Training Course for Research Reactor Operator (Ctorp). This course is offered since 1974 and about 250 nuclear professionals were certificated by CDTN. Thus, a digital simulation system for the IPR-R1 Triga research reactor was developed to be a tool for teaching, training and recycling professionals. The simulator was developed using the LabVIEW® (Laboratory Virtual Instruments Engineering Workbench), with support calculation software, where mathematical models and graphical interface configurations form a friendly platform, which allows the trainee to be identified with the physical systems of the research reactor. A simplified modeling of the main physical phenomena related to the operation of the reactor and the reactivity control systems, reactor cooling and reactor protection was used. The digital simulator allows an HMI (Human-Machine Interaction) by manipulating system variables and monitoring trends in quantities during the operation of the reactor, showing an interactive tool for teaching, training and recycling for professionals in the IPR-R1 Triga nuclear research, allowing simulations of the start, power and stop operations. This paper presents the design and results of the user visual interfaces developed for the reactor operation simulator. This is the equivalent part of structured text programming and, therefore, the most significant part of the developed simulator.
{"title":"Visual interfaces for the digital simulation system of the IPR-R1 Triga nuclear research reactor","authors":"A. Z. Mesquita, Adriano de Almeida Massaud Felippe, A. M. Lage, P. A. M. Ribeiro","doi":"10.19101/tipcv.2020.618054","DOIUrl":"https://doi.org/10.19101/tipcv.2020.618054","url":null,"abstract":"Nuclear Technology Development Center (CDTN) offers the Training Course for Research Reactor Operator (Ctorp). This course is offered since 1974 and about 250 nuclear professionals were certificated by CDTN. Thus, a digital simulation system for the IPR-R1 Triga research reactor was developed to be a tool for teaching, training and recycling professionals. The simulator was developed using the LabVIEW® (Laboratory Virtual Instruments Engineering Workbench), with support calculation software, where mathematical models and graphical interface configurations form a friendly platform, which allows the trainee to be identified with the physical systems of the research reactor. A simplified modeling of the main physical phenomena related to the operation of the reactor and the reactivity control systems, reactor cooling and reactor protection was used. The digital simulator allows an HMI (Human-Machine Interaction) by manipulating system variables and monitoring trends in quantities during the operation of the reactor, showing an interactive tool for teaching, training and recycling for professionals in the IPR-R1 Triga nuclear research, allowing simulations of the start, power and stop operations. This paper presents the design and results of the user visual interfaces developed for the reactor operation simulator. This is the equivalent part of structured text programming and, therefore, the most significant part of the developed simulator.","PeriodicalId":243044,"journal":{"name":"ACCENTS Transactions on Image Processing and Computer Vision","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133849872","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 : 2020-05-30DOI: 10.19101/tipcv.2020.618040
Carey E. Ciaburri, Monica Kiehnle Benitez, A. Sheta, Malik Braik
Automatic extraction of water bodies from satellite imagery has been broadly studied for many reasons, including mapping of natural resources (i.e., forest and water resources), drinking water supplies, food production, agricultural planning, and disaster management. With the growth of global warming, it became essential to maintain the sustainable management of these resources for the preservation of human life. Several methods attempted to allocate water bodies from different satellite imagery in both spatial and spectral domains. In this paper, we present an automatic segmentation method to extract the water body from Landsat satellite imagery. The proposed segmentation approach consists of several stages, including histogram stretching, de-correlation, binarization of the image, and clutter removal using morphological operations. The segmentation results are promising.
{"title":"Automatic extraction of rivers from satellite images using image processing techniques","authors":"Carey E. Ciaburri, Monica Kiehnle Benitez, A. Sheta, Malik Braik","doi":"10.19101/tipcv.2020.618040","DOIUrl":"https://doi.org/10.19101/tipcv.2020.618040","url":null,"abstract":"Automatic extraction of water bodies from satellite imagery has been broadly studied for many reasons, including mapping of natural resources (i.e., forest and water resources), drinking water supplies, food production, agricultural planning, and disaster management. With the growth of global warming, it became essential to maintain the sustainable management of these resources for the preservation of human life. Several methods attempted to allocate water bodies from different satellite imagery in both spatial and spectral domains. In this paper, we present an automatic segmentation method to extract the water body from Landsat satellite imagery. The proposed segmentation approach consists of several stages, including histogram stretching, de-correlation, binarization of the image, and clutter removal using morphological operations. The segmentation results are promising.","PeriodicalId":243044,"journal":{"name":"ACCENTS Transactions on Image Processing and Computer Vision","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134224611","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 : 2020-05-30DOI: 10.19101/tipcv.2020.618022
H. S. Hemachitra, A. Lakshmi
Image processing has been functional to the numerous expansions of agricultural engineering in regulate to accomplish a quick accurate process. The procedure of physical categorization is leisurely and attains a level of bias, which is hard to be enumerated for typical type seed varieties. Seed examination and categorization can afford extra acquaintance in their creation, seeds superiority control and adulteration identification. Several techniques are utilized to resolve the struggle in perceiving and recognizing the standard type seed varieties, but the most objective is to categorize and recognize the Multifaceted Type Seed Varieties may be a quite difficult process, owing to its textural, shape and color patterns. These techniques do not provide an optimized and a correct depiction of the complex type seed varieties. The main objective of this work is to identify the complex type seed varieties for prospect fertilization within the field of agriculture. This context plans novel image processing systems to recognize, which incorporates an enhanced feature selection, and classification methodologies, which might optimize the exactness and reduce the time consumption of identifying the multifaceted type seed varieties. This novel technique provides efficient identification by feature selection and classification of those composite type seeds. The identification process, Adaptive Median Filter is employed for image enhancement; the edge detection for the image employs Sobel operator and Watershed Segmentation is used for the segmentation. Then Ant Colony Optimization (ACO) strategy is employed for the feature selection and Support Vector Machine (SVM) is employed in the classification process. The ACO based feature selection (ACOFS) provides ranges 8s to 20s of feature selection time for the dataset and the SVM classification provide 93.487% of accuracy while prediction.
{"title":"Complex type seed variety identification and recognition using optimized image processing techniques","authors":"H. S. Hemachitra, A. Lakshmi","doi":"10.19101/tipcv.2020.618022","DOIUrl":"https://doi.org/10.19101/tipcv.2020.618022","url":null,"abstract":"Image processing has been functional to the numerous expansions of agricultural engineering in regulate to accomplish a quick accurate process. The procedure of physical categorization is leisurely and attains a level of bias, which is hard to be enumerated for typical type seed varieties. Seed examination and categorization can afford extra acquaintance in their creation, seeds superiority control and adulteration identification. Several techniques are utilized to resolve the struggle in perceiving and recognizing the standard type seed varieties, but the most objective is to categorize and recognize the Multifaceted Type Seed Varieties may be a quite difficult process, owing to its textural, shape and color patterns. These techniques do not provide an optimized and a correct depiction of the complex type seed varieties. The main objective of this work is to identify the complex type seed varieties for prospect fertilization within the field of agriculture. This context plans novel image processing systems to recognize, which incorporates an enhanced feature selection, and classification methodologies, which might optimize the exactness and reduce the time consumption of identifying the multifaceted type seed varieties. This novel technique provides efficient identification by feature selection and classification of those composite type seeds. The identification process, Adaptive Median Filter is employed for image enhancement; the edge detection for the image employs Sobel operator and Watershed Segmentation is used for the segmentation. Then Ant Colony Optimization (ACO) strategy is employed for the feature selection and Support Vector Machine (SVM) is employed in the classification process. The ACO based feature selection (ACOFS) provides ranges 8s to 20s of feature selection time for the dataset and the SVM classification provide 93.487% of accuracy while prediction.","PeriodicalId":243044,"journal":{"name":"ACCENTS Transactions on Image Processing and Computer Vision","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116221585","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 : 2019-12-31DOI: 10.19101/tipcv.2019.5150016
Vishnu, Paavai Anand
India is the second most populated country with 1.37 billion people so that avoiding traffic is impossible. But with proper traffic signal control method, we can control the amount of time spent in traffic. Our solution for this problem is to control the traffic signal timing and allocate more time length of green light to lanes containing a greater number of vehicles using deep learning-based computer vision approaches such as object detection. In January 2019, more than a million and a half (1,607,315) new vehicles were bought and registered all across the country. In which 74% of the vehicles were two-wheelers and more than 80% of the total vehicles were petrol driven. India has 5.5 million kilometres of road network while now the number of vehicles registered is three times greater. These single statistics should reveal why Indian roads are getting more congested every month. In 2017, a total of 4,64,910 road accidents have been reported in which 1,47,913 deaths occurred and 4,70,975 people were injured. An average of 1274 accidents and 405 deaths every day. By using deep learning for controlling traffic signals, we can clear traffic more effectively and reduce traffic congestion, traffic violations, accidents, fuel consumption, pollution and time in traffic.
{"title":"Traffic signal timing control using deep learning","authors":"Vishnu, Paavai Anand","doi":"10.19101/tipcv.2019.5150016","DOIUrl":"https://doi.org/10.19101/tipcv.2019.5150016","url":null,"abstract":"India is the second most populated country with 1.37 billion people so that avoiding traffic is impossible. But with proper traffic signal control method, we can control the amount of time spent in traffic. Our solution for this problem is to control the traffic signal timing and allocate more time length of green light to lanes containing a greater number of vehicles using deep learning-based computer vision approaches such as object detection. In January 2019, more than a million and a half (1,607,315) new vehicles were bought and registered all across the country. In which 74% of the vehicles were two-wheelers and more than 80% of the total vehicles were petrol driven. India has 5.5 million kilometres of road network while now the number of vehicles registered is three times greater. These single statistics should reveal why Indian roads are getting more congested every month. In 2017, a total of 4,64,910 road accidents have been reported in which 1,47,913 deaths occurred and 4,70,975 people were injured. An average of 1274 accidents and 405 deaths every day. By using deep learning for controlling traffic signals, we can clear traffic more effectively and reduce traffic congestion, traffic violations, accidents, fuel consumption, pollution and time in traffic.","PeriodicalId":243044,"journal":{"name":"ACCENTS Transactions on Image Processing and Computer Vision","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128262478","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}
The demand for techniques based on computer vision are constantly increasing due to the development of techniques for decision making pertaining to medical, social and other primary disciples of day to day life. Image processing is a subset of computer vision in which the computer vision systems make use of the image processing algorithms to carry out vision emulation for recognizing objects. This study deal with the construction of convolution neural networks (CNNs) based on deep learning. It is used for classifying chest X-ray images into two classes (Normal, Abnormal) and executed on a graphics processing unit (GPU) based high performance computing platform. Medical image classification is one of the important tasks in many medical imaging applications. Tuberculosis is a communicable disease for which early diagnosis critical for disease control. Manual screening for tuberculosis identification involves a labour-intensive task with poor sensitivity and specificity. To improve diagnosis in medical images there is in need of better classification techniques. This paper proposes CNN to classify lung X-ray images with better classification accuracy and low error rate. The data available for medical image classification problems are insufficient to train accurate and robust classifier. The data augmentation technique helps to generate more new samples from the available images using label-preserving transformations. In this paper various augmentation techniques are implemented such as horizontal flips, vertical flip, rotation (fewer angle), crops, scale right and left, are used for capturing important characteristics of medical images, and they are applied to classification function. Later little work has been done to determine which augmented strategy is best for medical image classification. Here various augmentation results are compared and evaluated to show the better augmentation techniques. It is concluded that shear lead to validation accuracies of 93% and horizontal and vertical flips gives the least accuracy of 53% of accuracy.
{"title":"Image pre-processing: enhance the performance of medical image classification using various data augmentation technique","authors":"J. Rama, C. Nalini, A. Kumaravel","doi":"10.19101/TIPCV.413001","DOIUrl":"https://doi.org/10.19101/TIPCV.413001","url":null,"abstract":"The demand for techniques based on computer vision are constantly increasing due to the development of techniques for decision making pertaining to medical, social and other primary disciples of day to day life. Image processing is a subset of computer vision in which the computer vision systems make use of the image processing algorithms to carry out vision emulation for recognizing objects. This study deal with the construction of convolution neural networks (CNNs) based on deep learning. It is used for classifying chest X-ray images into two classes (Normal, Abnormal) and executed on a graphics processing unit (GPU) based high performance computing platform. Medical image classification is one of the important tasks in many medical imaging applications. Tuberculosis is a communicable disease for which early diagnosis critical for disease control. Manual screening for tuberculosis identification involves a labour-intensive task with poor sensitivity and specificity. To improve diagnosis in medical images there is in need of better classification techniques. This paper proposes CNN to classify lung X-ray images with better classification accuracy and low error rate. The data available for medical image classification problems are insufficient to train accurate and robust classifier. The data augmentation technique helps to generate more new samples from the available images using label-preserving transformations. In this paper various augmentation techniques are implemented such as horizontal flips, vertical flip, rotation (fewer angle), crops, scale right and left, are used for capturing important characteristics of medical images, and they are applied to classification function. Later little work has been done to determine which augmented strategy is best for medical image classification. Here various augmentation results are compared and evaluated to show the better augmentation techniques. It is concluded that shear lead to validation accuracies of 93% and horizontal and vertical flips gives the least accuracy of 53% of accuracy.","PeriodicalId":243044,"journal":{"name":"ACCENTS Transactions on Image Processing and Computer Vision","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114001653","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 : 2019-02-21DOI: 10.19101/TIPCV.2018.412004
B. Mohapatra, Prangya Prava Panda
{"title":"Machine learning applications to smart city","authors":"B. Mohapatra, Prangya Prava Panda","doi":"10.19101/TIPCV.2018.412004","DOIUrl":"https://doi.org/10.19101/TIPCV.2018.412004","url":null,"abstract":"","PeriodicalId":243044,"journal":{"name":"ACCENTS Transactions on Image Processing and Computer Vision","volume":"3 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128956390","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 : 2018-11-21DOI: 10.19101/tipcv.2018.412005
Bricio Mares Salles, A. Z. Mesquita, Marley Rosa Luciano
Nuclear research reactors are often found in open pools, allowing visibility of the core and the bluish luminosity of the Cherenkov radiation. In general, the thermal energy released in these reactors is monitored by chambers that measure neutron flux, because this is proportional to the power. There are other methods used to measure the power, including measure of the fuel rod central temperature and the energy balance in the heat exchanger. The brightness of Cherenkov radiation is caused by the emission of visible electromagnetic radiation (in the blue band) by charged particles that pass through an insulating medium (water in nuclear research reactors) at a speed greater than that of light in this medium. This effect was characterized by Pavel Cherenkov, earning him the Nobel Prize in physics in 1958. The objective of the present project is to develop an innovative and alternative method to monitor the power of nuclear research reactors. This will be done by analyzing and monitoring the intensity of luminosity generated by the Cherenkov radiation in the reactor core. This method will be valid for powers up to 250 kW, because above this value the brightness is saturated, as determined by previous studies. The reactor that will be used to test the method is the IPR R1 Triga, located at the Nuclear Technology Development Center (CDTN), currently with a maximum operating power of 250 kW. This project complies with the recommendations of the International Atomic Energy Agency (IAEA) on the safety of reactors. It will provide more redundancy and diversification in this measurement and will not interfere with the operation of the reactor.
{"title":"Development of a research reactor power measurement system using cherenkov radiation","authors":"Bricio Mares Salles, A. Z. Mesquita, Marley Rosa Luciano","doi":"10.19101/tipcv.2018.412005","DOIUrl":"https://doi.org/10.19101/tipcv.2018.412005","url":null,"abstract":"Nuclear research reactors are often found in open pools, allowing visibility of the core and the bluish luminosity of the Cherenkov radiation. In general, the thermal energy released in these reactors is monitored by chambers that measure neutron flux, because this is proportional to the power. There are other methods used to measure the power, including measure of the fuel rod central temperature and the energy balance in the heat exchanger. The brightness of Cherenkov radiation is caused by the emission of visible electromagnetic radiation (in the blue band) by charged particles that pass through an insulating medium (water in nuclear research reactors) at a speed greater than that of light in this medium. This effect was characterized by Pavel Cherenkov, earning him the Nobel Prize in physics in 1958. The objective of the present project is to develop an innovative and alternative method to monitor the power of nuclear research reactors. This will be done by analyzing and monitoring the intensity of luminosity generated by the Cherenkov radiation in the reactor core. This method will be valid for powers up to 250 kW, because above this value the brightness is saturated, as determined by previous studies. The reactor that will be used to test the method is the IPR R1 Triga, located at the Nuclear Technology Development Center (CDTN), currently with a maximum operating power of 250 kW. This project complies with the recommendations of the International Atomic Energy Agency (IAEA) on the safety of reactors. It will provide more redundancy and diversification in this measurement and will not interfere with the operation of the reactor.","PeriodicalId":243044,"journal":{"name":"ACCENTS Transactions on Image Processing and Computer Vision","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125589549","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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