Computer Vision Methods for the Real Time Assessment of Dietary Intake

描述（由申请人提供）：在美国，肥胖是可预防的死亡和残疾的主要原因。对所消耗的所有食物和饮料的自我监测是减肥和维持体重努力的核心;然而，这给使用者带来了沉重的负担。这些负担也阻碍了营养研究。拟议的研究是一个半自动化的，客观的，近实时的计算机视觉和模式识别方法来测量饮食摄入量的测试。在拟议的产品中，手机上的饭菜和零食图片将由软件进行分析，试图自动识别尽可能多的物品。少量的智能是/否问题将有助于在必要时提供额外的信息，以满足目标应用程序的准确性要求。识别物品后，软件将估计所有识别物品的份量。包括该阶段I SBIR的实验是（a）使用从真实的世界膳食图像的现有数据库中获取的大量食品和饮料项目来提取信息量最大的特征集，（B）比较候选模式识别方法的准确性以基于所提取的特征来识别项目，（c）识别用于估计份量大小的最可行算法，以及（d）用产品的模拟版本测试可用性和用户接受度。第二阶段将（a）将该方法应用于更多种类的食品和饮料项目，（B）改进自动化分析，（c）将该方法与现有评估工具进行比较。这项研究将把国防和安全相关技术扩展到肥胖症的评估和治疗。

DESCRIPTION (provided by applicant): Obesity is a leading cause of preventable death and disability in the U.S. Self- monitoring of all foods and beverages consumed is central to weight loss and maintenance efforts; however, this places a heavy burden on the user. These same burdens also impede nutritional research. The proposed research is for the testing of a semi-automated, objective, near real-time computer vision and pattern recognition approach to the measurement of dietary intake. In the proposed product, cell phone pictures of meals and snacks will be analyzed by software in an attempt to automatically recognize as many items as possible. A small number of intelligent yes/no questions will help provide additional information when necessary in order to meet the accuracy demands of the target application. Following identification of the items, the software will estimate the portion sizes of all identified items. The experiments comprising this Phase I SBIR are (a) extract the most informative sets of features using a large number of food and beverage items taken from an existing database of real world meal images, (b) compare the accuracy of candidate pattern recognition approaches to identify items based on the extracted features, (c) identify the most feasible algorithms for estimating portion size, and (d) test usability and user acceptance with a simulated version of the product. Phase II will (a) apply the approach to a greater variety of food and beverage items, (b) improve automated analysis, and (c) compare the approach to existing assessment instruments. This research will extend defense- and security-related technologies to the assessment and treatment of obesity.