Computer Vision Technology for Food Quality Evaluation (Food by Da-Wen Sun

By Da-Wen Sun

The 1st booklet during this quickly increasing quarter, Computer imaginative and prescient know-how for foodstuff caliber Evaluation completely discusses the newest advances in snapshot processing and research. machine imaginative and prescient has attracted a lot learn and improvement realization in recent times and, consequently, major medical and technological advances were made in caliber inspection, category and assessment of quite a lot of foodstuff and agricultural items. This distinctive paintings offers engineers and technologists operating in learn, improvement, and operations within the nutrients with serious, finished and with no trouble available details at the artwork and technological know-how of computing device imaginative and prescient know-how. Undergraduate and postgraduate scholars and researchers in universities and examine associations also will locate this an important reference resource.

· Discusses novel know-how for spotting gadgets and extracting quantitative details from electronic photographs that allows you to supply target, quick, non-contact and non-destructive caliber evaluate.
· overseas authors with either educational credentials tackle intimately one point of the proper expertise in step with bankruptcy making this perfect for textbook use
· Divided into 3 components, it starts with an overview of the basics of the expertise, through complete insurance of the appliance within the such a lot researched parts of meats and different meals, end result, greens and grains.

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Transactions of the ASAE, 44 (2), 337–345. Chtioui Y, Panigrahi S, Backer LF (2003) Self-organizing map combined with a fuzzy clustering for color image segmentation. Transactions of the ASAE, 46 (3), 831–838. Du C-J, Sun D-W (2004) Shape extraction and classification of pizza base using computer vision. Journal of Food Engineering, 64 (4), 489–496. Du C-J, Sun D-W (2006a) Automatic measurement of pores and porosity in pork ham and their correlations with processing time, water content and texture.

The large calculation means that multilevel (more than tri-) thresholding is unfeasible, and therefore only bi-level and tri-level thresholding are used in practice. It is obvious that the threshold for the segmentation described above is a fixed value (called the global threshold) across the whole image. There is another kind of threshold, called the local threshold, which is an adaptive value determined by the local characteristics of pixels. However, only the global threshold is popularly used in the food industry, mainly because the global threshold is selected from the image histogram rather than the image itself.

Brooks RA, Di Chiro G (1976) Principles of computer assisted tomography (CAT) in radiographic and radioisotope imaging. Physics in Medical Biology, 21 (5), 689–732. Camarena F, Martínez-Mora JA (2005) Potential of ultrasound to evaluate turgidity and hydration of orange peel. Journal of Food Engineering, 75 (4), 503–507. Campbell SA (2001) The Science and Engineering of Microelectronic Fabrication. New York: Oxford University Press. Daley W, Carey R, Thompson C (1993) Poultry grading inspection using colour imaging.

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