PROJECT TITLE :

You are what you eat: So measure what you eat!

ABSTRACT:

Measuring food calorie and nutrition intake on a daily basis is one in every of the main tools that allows dieticians, doctors, and their patients to control and treat obesity, overweightness, or alternative food-related health issues. Nevertheless doing this measurement properly and daily is difficult and one among the main reasons why diet programs fail. In this article, we tend to study calorie-intake measurement techniques, and we cowl both traditional and newer strategies with stress on the latter. Among the newly proposed ways, Vision Primarily based Measurement (VBM) [one] has gained a heap of attention, as a result of it makes it terribly easy for users to live their food's calories and nutrition by simply taking a picture of their food with their smartphone. However, this still faces challenges, like achieving higher measurement accuracies, recognizing complicated food items like mixed food, lack of sufficient processing power, etc. When measuring food calories with VBM, recognition of the food may be a significantly difficult method as a result of food things have totally different variations in shape and look. Furthermore, the algorithms used for food recognition and classification are computationally intensive. We have a tendency to can cover many solutions and architectures in this text that are proposed to tackle these challenges.