PISHVA Davar    Department   Ritsumeikan Asia Pacific University  College of Sustainability and Tourism    Position   Professor
Language	English
Publication Date	2010/06
Type	Research paper (Academic/Professional Journal)
Title	Texture Analysis for Food Recognition
Contribution Type	Joint Work
Journal	IEICE Technical Reports on Pattern Recognition and Media Understanding
Volume, Issue, Page	110(97),pp.69-74
Author and coauthor	K. N. Do, J. Ohya
Details	This paper studies the effectiveness of texture analysis methods for classifying different food items having the same color. This paper studies two texture analysis methods: gray-level co-occurrence matrix (GLCM) based features and Fourier Transform (FT) based features. We carried out experiments on testing the effectiveness of the two texture features using six different food items, where two food items having three colors: white, red and yellow are used. From the experimental results, it turns out that GLCM features and FT’s spatial frequency based features are promising.