A Novel Technique for Grading of Dates using Shape and Texture Features

Autor:	Mohana, S. H., Prabhakar, C. J.
Rok vydání:	2015
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	This paper presents a novel method to grade the date fruits based on the combination of shape and texture features. The method begins with reducing the specular reflection and small noise using a bilateral filter. Threshold based segmentation is performed for background removal and fruit part selection from the given image. Shape features is extracted using the contour of the date fruit and texture features are extracted using Curvelet transform and Local Binary Pattern (LBP) from the selected date fruit region. Finally, combinations of shape and texture features are fused to grade the dates into six grades. k-Nearest Neighbour(k-NN) classifier yields the best grading rate compared to other two classifiers such as Support Vector Machine (SVM) and Linear Discriminant(LDA) classifiers. The experiment result shows that our technique achieves highest accuracy. Comment: 15 pages, 3 figures and 6 tables in Machine Learning and Applications: An International Journal (MLAIJ) Vol.1, No.2, December 2014
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1501.01090 Zobrazit plný text záznamu View this record from Arxiv