  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.
