{"title":"A camera-based autonomous mobile robot on a simple simulated highway","authors":"Brendan A. Marcellino, E. Kuantama, S. Sofyan","doi":"10.1109/ICICI-BME.2011.6108596","DOIUrl":"https://doi.org/10.1109/ICICI-BME.2011.6108596","url":null,"abstract":"The automatic car steering technology has the potential to reduce the rate of traffic accidents, in the future. As a starting point, in this research a mobile robot, which can run between two road lines using a camera is designed. To detect the road lines in the image from the camera, Edge Detection and Line Detection method is used. The kernel used in the Edge Detection method is the Sobel kernel. The Line Detection is done by finding the coordinates of the maximum value for each column in the Edge Detection's image result. The intersection of two lines will show a certain direction of the robot to correct its direction of movement. Both methods are implemented on a wheeled mobile robot that is controlled by a microcontroller. The robot can detect the miniature road lines and correct its direction of movement so that it can run between the two road lines in parallel with the lines.","PeriodicalId":395673,"journal":{"name":"2011 2nd International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115255069","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}
N. Manshor, Alfian Abdul Halin, M. Rajeswari, D. Ramachandram
{"title":"Feature selection via dimensionality reduction for object class recognition","authors":"N. Manshor, Alfian Abdul Halin, M. Rajeswari, D. Ramachandram","doi":"10.1109/ICICI-BME.2011.6108645","DOIUrl":"https://doi.org/10.1109/ICICI-BME.2011.6108645","url":null,"abstract":"This paper investigates the effects of feature selection via dimensionality reduction techniques for the task of object class recognition. Two filter-based algorithms are considered namely Correlation-based Feature Selection (CFS) and Principal Components Analysis (PCA). A Support Vector Machine is used to compare these two techniques against classical feature concatenation, based on the Graz02 dataset. Experimental results show that the feature selection algorithms are able to retain the most relevant and discriminant features, while maintaining recognition accuracy and improving model building time.","PeriodicalId":395673,"journal":{"name":"2011 2nd International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133567148","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}