Food Calorie Measurement Using Matlab -Image Processing Projects


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High calorie intake has proved harmful worldwide, as it has led to many diseases. However, dieticians have mistake that a standard intake of number of calories is essential to maintain the right balance of calorie content in human body. In this thesis, we consider the category of tools that use image processing to recognize single and multiple mixed food objects, namely support vector machine (SVM). We propose a method for the fully automatic and user-friendly calibration of the sizes of food portions. This calibration is required to estimate the total number of calories in food portions. In this work, to compute the number of calories in the food object, we go beyond the finger-based calorie calibration method that has been used in the past, by automatically measuring the distance between the user and the food object.



There has been an unprecedented rise worldwide in over weightiness and obesity, as well as in diseases they cause . Thus, monitoring daily eating routines in order to avoid excess calorie intake has become an important issue in maintaining a good quality of life. Studies have shown that several diseases are linked to excessive calorie intake in humans.  Pan found that breast, colon, and prostate cancers are caused by high calorie intake. High calorie intake was found to be only second to tobacco in directly causing cancer. Moreover, a previous study found that a proper diet involving lower calorie intake helped the residents of Okinawa to increase their life expectancy and lower the risk of age-associated diseases. High calorie values in food that is nutritionally poor led to systemic inflammation and reduced insulin sensitivity, as well as a cluster of metabolic abnormalities, including obesity, hypertension, and glucose intolerance. a review of the literature showed that reducing the calorie intake lessened the risk of cancer in humans. To assist people in tracking their calorie intake, efficient and convenient mobile applications have been developed that alert users to the number of calories they consume. Such mobile applications are becoming increasingly popular. Mobile applications have the capacity to provide the easy collection of personal health-related data and timely behavioral cues. Additionally, research has focused on the benefits of mobile and 2 Internet technologies in reaching diverse populations, such as rural communities, in order to reduce health disparities and promote health interventions.


Food used to portioned and search by segmentation process using  Discreet cosine transform then used to classify by K-NN(k nearest neighbor) by that it takes much time trainee the images and classify no proper data been assumed more data loss may occur.


In this paper a automatic dietary monitoring of canteen customers that is based on histograms feature techniques for automatic food recognition and leftover estimation in a canteen scenario. Although the canteen scenario includes some apparent simplification such as controlled image acquisition conditions, known weekly menu etc., the problem of food recognition and leftover estimation is still a challenging problem due to the enormous variations in the tray and plate composition. The visual appearance of the same dish may greatly change depending on how it is placed on the plate. The system is able to identify and recognize the food category and estimate the amount of food  and  carbohydrates Moreover, we can classify by support vector machine and identify different calories of  food


food calorie detection using matlab


  • Measuring of consumption calories in food image.
  • The accuracy of the system will be acceptable


  • MATLAB 7.14 and Above versions


  • Bio medical field


In The proposed system a measurement method that estimates the amount of calories from a food’s image by measuring the area of the food portions from the image and using nutritional facts tables to measure the amount of calorie and nutrition in the food. And calorie is shown in final results with approximate value. Thus the paper is designed to aid dieticians for the treatment of obese or overweight people, although normal people can also benefit from our system by controlling more closely their daily eating without worrying about overeating and weight gain This is simple and easy to use. Hence this system is very important in the field of biomedical, the actual programming We focused on identifying food items in an image by using image processing and segmentation, food classification using NN, food portion area measurement, and calorie measurement based on food portion and nutritional tables. Our results indicated reasonable accuracy of our method in area measurement.


In Future, we also implement these system by using an hardware for an calorie and nutrition measurement along with mass. liquid food such as milk, sauce, tea, juices and etc. also, more work is needed for supporting mixed or even liquid food. Advance system can be design to use any kind of plates having a different color for capturing a photo instead of white only. An obvious avenue for future work is to cover more food types from a variety of cuisines around the world 


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2. Anthimopoulos, M.M., Gianola, L., Scarnato, L., Diem, P., Mougiakakou, S.G.: A food recognition system for diabetic patients based on an optimized bag-of-features model. IEEE Journal of Biomedical and Health Informatics 18(4), 1261–1271 (2014)

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4. Bettadapura, V., Thomaz, E., Parnami, A., Abowd, G., Essa, I.: Leveraging con-text to support automated food recognition in restaurants. In: 2015 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 580–587 (2015)

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6. Chae, J., Woo, I., Kim, S., Maciejewski, R., Zhu, F., Delp, E.J., Boushey, C.J.,Ebert, D.S.: Volume estimation using food specific shape templates in mobile image-based  dietary assessment. In: IS&T/SPIE Electronic Imaging, p. 78730. International Society for Optics and Photonics (2011)

7. Chatzichristofis, S.A., Boutalis, Y.S.: CEDD: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 312–322. Springer, Heidelberg (2008)

8. Ciocca, G., Napoletano, P., Schettini, R.: Iat-image annotation tool: Manual. arXiv preprint arXiv:1502.05212 (2015)

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10. Cusano, C., Napoletano, P., Schettini, R.: Combining local binary patterns and local color contrast for texture classification under varying illumination. JOSA A 31(7), 1453–1461 (2014)

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