Cheng-Jin Du

TL;DR: Recent advances in image processing techniques for food quality evaluation are reviewed, which include charge coupled device camera, ultrasound, magnetic resonance imaging, computed tomography, and electrical tomography for image acquisition; pixel and local pre- processing approaches for image pre-processing; thresholding- based, gradient-based, region-based; and classification-based methods for image segmentation.

TL;DR: The promise of learning techniques for food quality evaluation using computer vision is demonstrated, and some issues which need to be resolved or investigated further to expedite the application of learning algorithms are also discussed.

TL;DR: The polynomial SVM classifier combined with HSV colour space transformation proved to be a good approach for the classification of pizza toppings using computer vision.

TL;DR: Examination of the ribeye at high magnification and consideration of a broad range of colour and marbling fat features was used to attempt to provide better information on beef eating quality to improve computerised predictions of eating quality.

TL;DR: An automated classification system of pizza sauce spread using colour vision and support vector machine (SVM) was developed in this article, where a vector quantifier was designed to quantify the HS (hue and saturation) space to 256-dimension, and the quantified colour features of Pizza sauce spread were represented by colour histogram.