Improving Food Detection For Images From a Wearable Egocentric Camera
This addresses a specific technical issue for image-based dietary assessment systems, but it is incremental as it focuses on pre-processing rather than novel detection methods.
The paper tackles the problem of blurry images from a wearable egocentric camera degrading food detection performance by proposing an approach to pre-process and reject extremely blurry images to improve detection.
Diet is an important aspect of our health. Good dietary habits can contribute to the prevention of many diseases and improve the overall quality of life. To better understand the relationship between diet and health, image-based dietary assessment systems have been developed to collect dietary information. We introduce the Automatic Ingestion Monitor (AIM), a device that can be attached to one's eye glasses. It provides an automated hands-free approach to capture eating scene images. While AIM has several advantages, images captured by the AIM are sometimes blurry. Blurry images can significantly degrade the performance of food image analysis such as food detection. In this paper, we propose an approach to pre-process images collected by the AIM imaging sensor by rejecting extremely blurry images to improve the performance of food detection.