Visualization of Crossmodal Effects Induced by Vision

–Quantification of Effects of Food Appearance to Texture and Taste–

Abstract

Food appearance is critical information for estimating edibility, freshness and softness of foods. We developed some image processing techniques to modify the food appearance naturally. The first method is Luminance Distribution Manipulation (LDM). We found that LDM can modify the moistness and softness of foods in appearance. The second method is Visual Texture Exchange (VTE). VTE enables us to change the visual texture of a food from the original surface to other actual one, e.g. from Tuna to Salmon, and from Black Coffee to Café Latte in real time. The third method is Gloss/Shade Filter Operation (GSFO) which can create any Oily/Dried and Burned/Raw foods from an original food image in appearance. By applying such image processing methods and Augmented Reality technology, we are investigating crossmodal effects of food appearance to the taste while keeping the ingredients intact, indicating that we can artificially control the taste by modifying food appearance with image processing. Finally, I will introduce a novel approach to reduce salt by modulating saltiness by changing food appearances with Augmented Reality. We quantified the salt reduction effect by converting the perceived responses to match the amount of salt.