Recent developments in the applications of image processing techniques for food quality evaluation

By C. Du, Da‐Wen Sun. 2004

636 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d6a1366b343ac6ec9c6229f047addf5036fa2090

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In Analytical Chemistry

In Biomedical Engineering

In Molecular Biology

Citations in the corpus

Bread crumb quality assessment: a plural physical approach (2009, background): “These systems not only offer a methodology for measurements of uneven coloration but it can also be applied for the assessment of many other attributes of whole appearance [16].”
Deep learning and machine vision for food processing: A survey (2021, background): “Image processing guides the operation of the machinery and has played a key role in the multi-tasking of machine vision systems (Du et al., 2004).”
Identification of broad-leaved dock (Rumex obtusifolius L.) on grassland by means of digital image processing (2006, neutral): “Consequently, we aimed to identify geometrical features describing these shapes ( Du & Sun, 2004 ).”
Color measurement in L ¿ a ¿ b ¿ units from RGB digital images (2006, methodology): “…measure the color of different foods since they provide some obvious advantages over a conventional colorimeter, namely, the possibility of analyzing of each pixel of the entire surface of the food, and quantifying surface characteristics and defects (Brosnan & Sun, 2004; Du & Sun, 2004).”
Colour Measurement and Analysis in Fresh and Processed Foods: A Review (2012, methodology): “Computer vision has been used to objectively measure the colour of different foods since they provide some obvious advantages over a conventional colorimeter, namely, the possibility of analysing each pixel of the entire surface of the food and quantifying surface characteristics and defects (Brosna”
Application of Hyperspectral Imaging in Food Safety Inspection and Control: A Review (2012, background): “Working in visible range, the features obtained by computer vision include shape, color, size, and texture (Sun, 2000; Du and Sun, 2004 ; Zheng et al, 2006a ; 2006b; Wu et al, 2008).”