Impurity Profiling Of Food Using Template Matching
V.Muthulakshmi1, A.Mano Prarthana2, A.Nithiya3, M.Prabhavathy4
1V.Muthulaksmi, Information Technology, Kumuraguru College of Technology, Coimbatore, India.
2A.Mano Prarthana, Information Technology, Kumuraguru College of Technology , Coimbatore, India.
3Nithiya.A, Information Technology, Kumuraguru College of Technology , Coimbatore , India.
4Prabhavathy.M , Information Technology , Kumuraguru College of Technology , Coimbatore , India.
Manuscript received on March 09, 2013. | Revised Manuscript Received on March 12, 2013. | Manuscript published on March 20, 2013. | PP: 46-51 | Volume-1 Issue-4, March 2013.
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Food is essential for nourishment and sustenance of life. The addition of impurities to food affects the composition and quality of food. The manual method is practical and fast, but lacks the reliability and objectivity required in competitive food industries. Machine vision using morphological features have been reported in numerous studies as an effective solution to detect impurities in food. In this paper we experimented detection of impurities in rice samples using template matching technique. Various image processing techniques have been studied and we’ve concluded that template matching is the best and efficient way to detect the impurities. Using area computation we’ve also identified the broken rice in samples.
Keywords: Machine vision, Edge Detection, Normalized Cross Correlation, Template Matching.