Abstract
Rheological properties of food products are strongly related to their microstructure. Microscopy is thus a preferred tool in food research. In food science, microscopy has long been used for visual inspection. Recently, however, quantitative analysis has become the new trend. In spite of this, only a few experts in computer vision are actively involved into image analysis projects, applied to food microscopy. Microscopists tend to use simple tools, without bothering whether they are appropriate for their application. As a consequence, most published work in food science journals lacks scientific rigour, when it comes to analysing images. On the other hand, image analysis experts tend to undervalue microscopists’ needs and opinions, which can be surprisingly different from what most people in the computer vision community might think. Drawing upon our experience, we try to highlight microscopists’ perspective on image segmentation and, at the same time, show a few examples of collaborative projects that compute interesting measures for the food science community, that do not rely on segmentation accuracy.
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Impoco, G. (2015). Image Analysis and Microscopy in Food Science: Computer Vision and Visual Inspection. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2015. Lecture Notes in Computer Science(), vol 9386. Springer, Cham. https://doi.org/10.1007/978-3-319-25903-1_56
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DOI: https://doi.org/10.1007/978-3-319-25903-1_56
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