Specular highlight removal for image-based dietary assessment
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Traditional dietary assessment methods, consisting of written and orally reported methods, are not widely acceptable or feasible for everyday monitoring. The development of built-in cameras for mobile devices provides a new way of collecting dietary information by acquiring images of foods and beverages. The ability of image analysis techniques to automatically segment and identify food items from food images becomes imperative. Food images, usually consisting of plates, bowls and glasses, are often affected by lighting and specular highlights which present difficulties for image analysis. In this paper, we propose a novel single-image specular highlight removal method to detect and remove specular highlights in food images. We use independent components analysis (ICA) to separate the specular and diffuse components from the original image using only one image. This paper describes the details of the proposed model and also presents experimental results on food images to demonstrate the effectiveness of our approach.
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