Fitur Pengkategorian Otomatis dari Gambar Berbasis Web dengan Metode SURF dan Haar Cascade Classifiers
(1) Program Studi Teknik Informatika
(2) Program Studi Teknik Informatika
(3) Program Studi Teknik Informatika
(*) Corresponding Author
Abstract
Ecommerce is a Market Place and a technological advancement nowadays where it is now widely used by the public to conducts transaction of needed goods due to very practical. Shopping by using Ecommerce also cheaps and the price is very competitive then conventional store, which more expensive due to large operational costs. But the errors of data contained in Ecommerce is greater than a conventional store, because the data input is depends on the person who using it.
With the development of technology, especially on Web Services that helps Ecommerce grow significantly, of course there are solutions to solve the problem and reduce an errors that would made disadvantageous on both side. And one of them is a Computer Intelligence developed to be able to detect and recognize an object.
The detection and recognition of objects which will be added on Ecommerce for its feature use frequent of image processing methods for face detection, the Haar Cascade Classifier, and the recognition using SURF. So these feature can improve the performance of Ecommerce so as not to use significant costs and upgrade.
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A.L. Yuille. 2012. Computer Vision Needs a Core and Foundation. Image and Vision Computing no.30, pp. 469-471.
Alban Vergnaud, Jean Baptise Fasquel, Laurent Atrique. 2015. Python based Internet Tools in Control Education. International Federation of Automatic Control, pp.43-48.
Christian Szegedy et al. 2015. Going Deeper with Convolutions. University of North Carolina, Chapel Hill, University of Michigan, Ann Arbor, Magic Leap Inc.
Danijela Vukadinovic, Maja Pantic. 2015. Fully Automatic Facial Feature Point Detection Using Gabor Feature Based Boosted Classifiers. IEEE International Conference on System, Man and Cybernatics.
Divya Chhabra, Dr. Amandeep Verma. 2012. Multiple Object Detection for Smart TV Shopping Video using Point to Point feature based SURF Method. Department of Computer Science and Engineering.
Er. Saurabh Walia, Er. Satinderjit Kaur Gill. 2014. A Framework for Web Based Student Record Management System using PHP. International Journal of Computer Science and Information Technology, vol. 3, issue. 8, pp. 24-33.
G.M Zhu, Z.L. Ying, dan L.W Huang. 2015. A New Face Recognition Algorithm Based on Haar-Like Features and SRC with Gentle Adaboost. International Conference on Artificial Intelligence Engineering.
Kanuengnit Patoommakesorn , Frédéric Vignat, dan François Villeneuve. 2016. A New Straight Line Matching Technique by Integration of Vision-based Image Processing. Procedia CIRP Conference no.41, pp.777-782.
Kie-Yeong Park, Sun-Young Hwang. 2014. An Improved Haar-Liked Feature for Efficient Object Detection. Pattern Recognition Letters no.42, pp. 148-153.
Krupali Mistry, Avneet Saluja. 2016. An Introduction to OpenCV using Python with Ubuntu. International Journal of Scientific Research in Computer Science , Engineering and Information Technology (2456-3307), vol. 1, issue 2, pp. 65-68.
Silica Kole et al. 2015. SURF and RANSAC: A Conglomerative Approach to Object Recognition. International Journal of Computer Applications (0975 – 8887), vol. 109, no.4
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