Journal
JOURNAL OF FOOD ENGINEERING
Volume 72, Issue 1, Pages 39-55Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jfoodeng.2004.11.017
Keywords
ANN; classification; computer vision; decision tree; feature selection; food; fuzzy logic; genetic algorithm; image segmentation; learning; prediction; quality evaluation; SL
Categories
Ask authors/readers for more resources
Learning techniques have been applied increasingly for food quality evaluation using computer vision in recent years. This paper reviews recent advances in learning techniques for food quality evaluation using computer vision, which include artificial neural network, statistical learning, fuzzy logic, genetic algorithm, and decision tree. Artificial neural network (ANN) and statistical learning (SL) remain the primary learning methods in the field of computer vision for food quality evaluation. Among the applications of learning algorithms in computer vision for food quality evaluation, most of them are for classification and prediction, however, there are also some for image segmentation and feature selection. In this paper, 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. (c) 2004 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available