Publication Type : Journal Article
Publisher : Journal of Chemical and Pharmaceutical Sciences
Source : Journal of Chemical and Pharmaceutical Sciences, SPB Pharma Society, Volume 9, Number 1, p.554-558 (2016)
Url : https://www.scopus.com/inward/record.url?eid=2-s2.0-84963575175&partnerID=40&md5=63e3a0df0d58c28e2115a296a7ebddd9
Keywords : classification, classifier, model, Recognition, Regression analysis, Robotics
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking, Electronics Communication and Instrumentation Forum (ECIF)
Department : Electronics and Communication
Year : 2016
Abstract : This paper deals with the Fourier descriptor features for shape deformation classification using Random Kitchen Sink algorithm accessed through GURLS library. Shape recognition is an important method used in all industrial environments which are mostly concerned with robots. It is a highly essential task to make the robot understand the shape of an object. The object may have many deformed shapes and so it is necessary to train the classifier accordingly. Recognition methods based on polar coordinates and probabilistic models are already developed, but its accuracy for finding the deformed shape of the object is low. In this context, Random Kitchen Sink algorithm is used and the classification is done through GURLS in which, regularized least square method is used, which leads to better shape recognition.
Cite this Research Publication : S. Se, Pradeep, D., Sowmya, and Dr. Soman K. P., “Fourier Descriptor features for Shape Deformation Classification using Random Kitchen Sink”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 554-558, 2016.