Smartphone-based Calorie Estimation From Food Image Using Distance Information

Personal assistive systems for diet control can play a vital role to combat obesity. As smartphones have become inseparable companions for a large number of people around the world, designing smartphone-based system is perhaps the best choice at the moment. Using this system people can take an image of their food right before eating, know the calorie content based on the food items on the plate. In this paper, we propose a simple method that ensures both user flexibility and high accuracy at the same time. The proposed system employs capturing food images with a fixed posture and estimating the volume of the food using simple geometry. The real world experiments on different food items chosen arbitrarily show that the proposed system can work well for both regular and liquid food items

Feasibility of Food Recognition and Calorie Estimation of Fast Food and Healthy Meals Available in the Philippines

This paper presents the design and development of a food recognition smartphone application which can also display the estimated calorie/s of the food itself. It is intended for people who would like to monitor their diet through food calorie intake measurement (i.e. user’s daily calorie intake record). It is equipped with a food database consisting of typical fruits and vegetables commonly found in the Philippines. As part of the study, it also includes some of the meals in food chains (i.e. McDonald's, and The Healthy Corner) found in the Philippines where the calorie information is readily available. The result shows 82.86 % accuracy for the top-1 category, and 99.29 % for the top-5 category. The algorithm being used in this project is Artificial Neural Network (ANN) wherein the recognition process must properly be achieved. Furthermore, the aforementioned database is supported by TensorFlow which is an open-source software library for Machine Intelligence

Foodopedia: A Convolutional Neural Network Based Food Calorie Estimation

The number of calories consumed determines how healthy a body is in the modern world; therefore, it's important to watch your calorie intake to be healthy. People must keep track of their caloric intake in order to become in shape or maintain a healthy weight. The suggested model uses a deep learning algorithm to offer a novel method of calorie measurement. In the medical area, the estimation of dietary calories is crucial. This measurement is derived from the representation of food in various objects, such as fruits and vegetables. The neural network is used to take this measurement. This technique uses a convolutional neural network to determine the calories in food. An image of food is used as the input for this calculated model. The suggested CNN model uses food object identification to calculate the calorie content of the food. Volume error estimation serves as the primary parameter for the outcome, while calorie error estimation serves as the secondary parameter

Dietary assessment and obesity aviodance system based on vision: A review

Using technology for food objects recognition and estimation of its calories is very useful to spread food culture and awareness among people in the age of obesity due to the bad habits of food consumption and wide range of inappropriate food products.Image based sensing of such system is very promising with the large expanding of camera embedded portable devices such as smartphones, PC tablets, and laptops.In the past decade, researchers have been working on developing a reliable image based system for food recognition and calories estimation.Different approaches have tackled the system from different aspects.This paper reviews the state of the art of this interesting application, and presents its experimental results.Future work of research is presented in order to guide new researchers toward potential tracks to create more maturity and reliability to this application

Automatic Food Intake Assessment Using Camera Phones

Obesity is becoming an epidemic phenomenon in most developed countries. The fundamental cause of obesity and overweight is an energy imbalance between calories consumed and calories expended. It is essential to monitor everyday food intake for obesity prevention and management. Existing dietary assessment methods usually require manually recording and recall of food types and portions. Accuracy of the results largely relies on many uncertain factors such as user\u27s memory, food knowledge, and portion estimations. As a result, the accuracy is often compromised. Accurate and convenient dietary assessment methods are still blank and needed in both population and research societies. In this thesis, an automatic food intake assessment method using cameras, inertial measurement units (IMUs) on smart phones was developed to help people foster a healthy life style. With this method, users use their smart phones before and after a meal to capture images or videos around the meal. The smart phone will recognize food items and calculate the volume of the food consumed and provide the results to users. The technical objective is to explore the feasibility of image based food recognition and image based volume estimation. This thesis comprises five publications that address four specific goals of this work: (1) to develop a prototype system with existing methods to review the literature methods, find their drawbacks and explore the feasibility to develop novel methods; (2) based on the prototype system, to investigate new food classification methods to improve the recognition accuracy to a field application level; (3) to design indexing methods for large-scale image database to facilitate the development of new food image recognition and retrieval algorithms; (4) to develop novel convenient and accurate food volume estimation methods using only smart phones with cameras and IMUs. A prototype system was implemented to review existing methods. Image feature detector and descriptor were developed and a nearest neighbor classifier were implemented to classify food items. A reedit card marker method was introduced for metric scale 3D reconstruction and volume calculation. To increase recognition accuracy, novel multi-view food recognition algorithms were developed to recognize regular shape food items. To further increase the accuracy and make the algorithm applicable to arbitrary food items, new food features, new classifiers were designed. The efficiency of the algorithm was increased by means of developing novel image indexing method in large-scale image database. Finally, the volume calculation was enhanced through reducing the marker and introducing IMUs. Sensor fusion technique to combine measurements from cameras and IMUs were explored to infer the metric scale of the 3D model as well as reduce noises from these sensors

DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion

A reasonable and balanced diet is essential for maintaining good health. With the advancements in deep learning, automated nutrition estimation method based on food images offers a promising solution for monitoring daily nutritional intake and promoting dietary health. While monocular image-based nutrition estimation is convenient, efficient, and economical, the challenge of limited accuracy remains a significant concern. To tackle this issue, we proposed DPF-Nutrition, an end-to-end nutrition estimation method using monocular images. In DPF-Nutrition, we introduced a depth prediction module to generate depth maps, thereby improving the accuracy of food portion estimation. Additionally, we designed an RGB-D fusion module that combined monocular images with the predicted depth information, resulting in better performance for nutrition estimation. To the best of our knowledge, this was the pioneering effort that integrated depth prediction and RGB-D fusion techniques in food nutrition estimation. Comprehensive experiments performed on Nutrition5k evaluated the effectiveness and efficiency of DPF-Nutrition