Coin recognition using texture feature based on SPLM and SGLDM algorithm
H N Veena, M. Muruganandam, Thangamuthu Senthil Kumaran · AIP conference proceedings · 2018
In this paper, texture classification is applied on coin image using coin recognition system. This system consists of training and testing phase. Based on Hough transform, firstly coin position will be identified in training phase. After the identification of the position, the texture feature will be extracted and uses these features to generate the dictionaries based on the Bag of word (BoW) approach. After extracting the texture feature, the image is divided into rings and fans structure and it follows the rotation invariant property. Further the features are quantized and generated a histogram to represent the texture features of the coin. In testing phase, the Support Vector Machine (SVM) is used to train a model for each class of the coin. Experimental results show that the proposed system provides more accuracy of recognition rate when compared to related studies.