CFP last date
20 May 2024
Reseach Article

Color Based Recognition and Estimation of Temperature Levels of Images of Boiled Food Grains

by Basavaraj S. Anami, Vishwanath C. Burkpalli
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 14
Year of Publication: 2010
Authors: Basavaraj S. Anami, Vishwanath C. Burkpalli
10.5120/292-456

Basavaraj S. Anami, Vishwanath C. Burkpalli . Color Based Recognition and Estimation of Temperature Levels of Images of Boiled Food Grains. International Journal of Computer Applications. 1, 14 ( February 2010), 98-103. DOI=10.5120/292-456

@article{ 10.5120/292-456,
author = { Basavaraj S. Anami, Vishwanath C. Burkpalli },
title = { Color Based Recognition and Estimation of Temperature Levels of Images of Boiled Food Grains },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 14 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 98-103 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number14/292-456/ },
doi = { 10.5120/292-456 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:42:18.856840+05:30
%A Basavaraj S. Anami
%A Vishwanath C. Burkpalli
%T Color Based Recognition and Estimation of Temperature Levels of Images of Boiled Food Grains
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 14
%P 98-103
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Automated food processing and evaluation is considered a significant research area in computer vision. The development of automated cooking and food serving by robots is envisaged as part of automated food processing and temperature plays a major role in cooking Indian foods. The delicious Indian foods are generally boiled or fried with other ingredients. The boiled grains like Bengal Gram, Black Gram, Green Gram, Red Gram and Toor Dal are part of typical Indian foods and taste differently, when boiled or cooked at different temperatures and periods of time. Therefore, identifying the effect of boiling and automatic recognition of images of boiled food grains is presented in this paper. The boiling temperatures chosen are 400 C, 500 C, 600 C, 800 C and 1000 C. A color feature centered knowledge based classifier is proposed. The classification accuracy observed is high at lower and higher temperatures and low at medium temperatures. The work finds applications in automatic inspection of food preparations in food industries, drug preparation in pharmaceutical industries, automatic serving, cooking and monitoring of foods in restaurants and motels.

References
  1. Anami B S and D G Savakar, (2009). Improved method for Identification of Foreign Bodies Mixed Food Grain Image Samples, International Journal of Artificial Intelligence and machine learning(AIML), Vol 9, Issue 1, pp 1-9.
  2. B.S.Anami, Vishwanath Burkpalli, S. A. Angadi, Nagamma Patil, (2003). Neural network approach for grain classification and gradation, Proceedings of the second national conference on document analysis and recognition, pp 394-405.
  3. B.S.Anami,Vishwanath Burkpalli, Sharanabasappa Madival, (2005). A texture based approach for classification of bulk boiled food grain images, Proceedings of the International conference on Cognation and Recognition, pp 419-426.
  4. A. Beatty, R.G. Gosine, & C.W. de Silva, (1993). Recent Developments In The Application Of Computer Vision For Automated Herring Roe Assessment, in the proceedings of IEEE Pacific Rim Conference on Communications Computers and Signal Processing, pp 698-701.
  5. Bin Zhu, Lu Jiang, Yaguang Luo & Yang Tao, (2007). Gabor feature-based apple quality inspection using kernel principal component analysis. Journal of Food Engineering. Vol. 81, Issue 4, pp 741-749.
  6. Lin D, Luo H, Lee J, (2007). Effects of Temperatures on Mortar Quantified by Surface Color Changes, Journal of ASTM International (JAI), Vol 4, Issue 4. 4, Issue 4.
  7. Chen. Y-R, (1989). Applying Knowledge Based expert System to meat grading, IEEE Conference Proceedings of the AI Systems in Government Conference of the Annual, pp-120-123.
  8. Cheng-Jin Du, Da-Wen, (2005). Pizza sauce spread classification using color vision and support vector machines. Journal of Food Engineering. Vol. 66, Issue 2, 2005, Page 137-145.
  9. Gary Kay, Gerhard de Jager. (1992). A Versatile Color System Capable of Fruit Sorting and Accurate Object Classification , proceedings of the 1992 South African Symposium COMSIG, pp 145-148.
  10. F.Pedrerschi. Mery, F.Mendoza & J.M. Aguilera, (2004). Classification of Potato Chips Using Pattern Recognition, Journal of Food Science. Vol. 69, pp-E264-270.
  11. Liyanage C De Silva, Anton Pereira, & Amal Punchihewa, (2005). Food Classifications using Color Imaging , Conference on Image and Vision Computing New Zealand (ivcnz) University of Otago, Dunedin, pp 1-6.
  12. Majumdar, S. D.S.Jayas, (1999). Classification of bulk Objects of Cereal grains using machine vision system, Journal of Agricultural Engineering Research. Vol. 731(1), pp. 35-37.
  13. M.M. Lana, L.M.M. Tijskens, O. van Kooten, (2006). Modeling RGB color aspects and translucency of fresh-cut tomatoes, Postharvest Biology and Technology, Volume40,Issue1,April2006,Pages15-25
  14. M.M. Lana, L.M.M. Tijskens, A. de Theije, M. Hogenkamp, O. van Kooten, (2006). Assessment of changes in optical properties of fresh-cut tomato using video image analysis, Postharvest Biology and Technology, Volume 41, Issue 3, Pages,296-306.
  15. Zhao-yan Liu, Fang Cheng, Yi-bin Ying, & Xiu-qin Rao, (2005). Identification of rice seed varieties using neural network. Journal of Zhejiang University SCIENCE B, Vol. 6(11), pp 1095-1100.
Index Terms

Computer Science
Information Sciences

Keywords

Color Features Knowledge Based Classifier Boiled Food Grains