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

A probabilistic framework for geometry reconstruction using prior information

ABSTRACT In this paper, we propose a probabilistic framework for reconstructing scene geometry utilizing prior knowledge of a class of scenes, for example, scenes captured by a camera mounted on a vehicle driving through city streets. In this framework, we assume the video camera is calibrated, i.e., the intrinsic and extrinsic parameters are known all the time. While we assume a single camera moving during capturing, the framework can be generalized to multiple cameras as well. Traditional approaches try to match the points, lines or patches in multiple images to reconstruct scene geometry. The proposed framework also takes advantage of each patch's appearance and location to infer its orientation using prior information based on statistical learning from training data. The prior hence enhances the geometry reconstruction performance. We show that prior-based 3D reconstruction outperforms traditional 3D reconstruction with both synthetic data and real data, especially in the textureless areas

A probabilistic framework for geometry reconstruction using prior information

10.1109/ICIP.2007.4379209Proceedings - International Conference on Image Processing, ICIP2II529-II53