Pub Date : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862126
C. Sivaranjani, Lekshmi Kalinathan, R. Amutha, Ruba Soundar Kathavarayan, K. J. Jegadish Kumar
The lighting condition of the environment are uncontrolled, so the segmentation of a leaf from the background is considered as a complex task. Here we propose a system which can identify the plant species based on the input leaf sample. An improved vegetation index, ExG-ExR is used to obtain more vegetative information from the images. The reason here is, it fixes a built-in zero threshold and hence there is no need to use otsu or any threshold value selected by the user. Inspite of the existence of more vegetative information in ExG with otsu method, our ExG-ExR index works well irrespective of the lighting background. Therefore, the ExG-ExR index identifies a binary plant region of interest. The original color pixel of the binary image serves as the mask which isolates leaves as sub-images. The plant species are classified by the color and texture features on each extracted leaf using Logistic Regression classifier with the accuracy of 93.3%.
{"title":"Real-Time Identification of Medicinal Plants using Machine Learning Techniques","authors":"C. Sivaranjani, Lekshmi Kalinathan, R. Amutha, Ruba Soundar Kathavarayan, K. J. Jegadish Kumar","doi":"10.1109/ICCIDS.2019.8862126","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862126","url":null,"abstract":"The lighting condition of the environment are uncontrolled, so the segmentation of a leaf from the background is considered as a complex task. Here we propose a system which can identify the plant species based on the input leaf sample. An improved vegetation index, ExG-ExR is used to obtain more vegetative information from the images. The reason here is, it fixes a built-in zero threshold and hence there is no need to use otsu or any threshold value selected by the user. Inspite of the existence of more vegetative information in ExG with otsu method, our ExG-ExR index works well irrespective of the lighting background. Therefore, the ExG-ExR index identifies a binary plant region of interest. The original color pixel of the binary image serves as the mask which isolates leaves as sub-images. The plant species are classified by the color and texture features on each extracted leaf using Logistic Regression classifier with the accuracy of 93.3%.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130442710","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862125
H. Muthu Mariappan, V. Gomathi
The real-time sign language recognition system is developed for recognising the gestures of Indian Sign Language (ISL). Generally, sign languages consist of hand gestures and facial expressions. For recognising the signs, the Regions of Interest (ROI) are identified and tracked using the skin segmentation feature of OpenCV. The training and prediction of hand gestures are performed by applying fuzzy c-means clustering machine learning algorithm. The gesture recognition has many applications such as gesture controlled robots and automated homes, game control, Human-Computer Interaction (HCI) and sign language interpretation. The proposed system is used to recognize the real-time signs. Hence it is very much useful for hearing and speech impaired people to communicate with normal people.
{"title":"Real-Time Recognition of Indian Sign Language","authors":"H. Muthu Mariappan, V. Gomathi","doi":"10.1109/ICCIDS.2019.8862125","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862125","url":null,"abstract":"The real-time sign language recognition system is developed for recognising the gestures of Indian Sign Language (ISL). Generally, sign languages consist of hand gestures and facial expressions. For recognising the signs, the Regions of Interest (ROI) are identified and tracked using the skin segmentation feature of OpenCV. The training and prediction of hand gestures are performed by applying fuzzy c-means clustering machine learning algorithm. The gesture recognition has many applications such as gesture controlled robots and automated homes, game control, Human-Computer Interaction (HCI) and sign language interpretation. The proposed system is used to recognize the real-time signs. Hence it is very much useful for hearing and speech impaired people to communicate with normal people.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117313429","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 : 2019-02-01DOI: 10.1109/iccids.2019.8862094
{"title":"ICCIDS 2019 Schedule","authors":"","doi":"10.1109/iccids.2019.8862094","DOIUrl":"https://doi.org/10.1109/iccids.2019.8862094","url":null,"abstract":"","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132772724","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 : 2019-02-01DOI: 10.1109/ICCIDS.2019.8862152
V. Swaminathan, Shrey Arora, R. Bansal, R. Rajalakshmi
According to statistics, most road accidents take place due to lack of response time to instant traffic events. With the self-driving cars, this problem can be addressed by implementing automated systems to detect these traffic events. To design such recognition system in self-driving automated cars, it is important to monitor and manoeuvre through real-time traffic events. This involves correctly identifying the traffic signs that can be faced by an automated vehicle, classifying them, and responding to them. In this paper, an attempt is made to design such system, by applying image recognition to capture traffic signs, classify them correctly using Convolutional Neural Network, and respond to it in real-time through an Arduino controlled autonomous car. To study the performance of this road sign recognition system, various experiments were conducted using Belgium Traffic Signs dataset and an accuracy of 83.7% has been achieved by this approach.
{"title":"Autonomous Driving System with Road Sign Recognition using Convolutional Neural Networks","authors":"V. Swaminathan, Shrey Arora, R. Bansal, R. Rajalakshmi","doi":"10.1109/ICCIDS.2019.8862152","DOIUrl":"https://doi.org/10.1109/ICCIDS.2019.8862152","url":null,"abstract":"According to statistics, most road accidents take place due to lack of response time to instant traffic events. With the self-driving cars, this problem can be addressed by implementing automated systems to detect these traffic events. To design such recognition system in self-driving automated cars, it is important to monitor and manoeuvre through real-time traffic events. This involves correctly identifying the traffic signs that can be faced by an automated vehicle, classifying them, and responding to them. In this paper, an attempt is made to design such system, by applying image recognition to capture traffic signs, classify them correctly using Convolutional Neural Network, and respond to it in real-time through an Arduino controlled autonomous car. To study the performance of this road sign recognition system, various experiments were conducted using Belgium Traffic Signs dataset and an accuracy of 83.7% has been achieved by this approach.","PeriodicalId":196915,"journal":{"name":"2019 International Conference on Computational Intelligence in Data Science (ICCIDS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125800472","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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