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A Survey of Bayesian Techniques in Computer Vision
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Author(s): José Blasco (Instituto Valenciano de Investigaciones Agrarias (IVIA), Spain), Nuria Aleixos (Polytechnic University of Valencia, Spain), Juan Gómez-Sanchis (University of Valencia, Spain), Juan F. Guerrero (University of Valencia, Spain)and Enrique Moltó (Instituto Valenciano de Investigaciones Agrarias (IVIA), Spain)
Copyright: 2010
Pages: 17
Source title:
Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
Source Author(s)/Editor(s): Emilio Soria Olivas (University of Valencia, Spain), José David Martín Guerrero (University of Valencia, Spain), Marcelino Martinez-Sober (University of Valencia, Spain), Jose Rafael Magdalena-Benedito (University of Valencia, Spain)and Antonio José Serrano López (University of Valencia, Spain)
DOI: 10.4018/978-1-60566-766-9.ch023
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Abstract
The Bayesian approach to classification is intended to solve questions concerning how to assign a class to an observed pattern using probability estimations. Red, green and blue (RGB) or hue, saturation and lightness (HSL) values of pixels in digital colour images can be considered as feature vectors to be classified, thus leading to Bayesian colour image segmentation. Bayesian classifiers are also used to sort objects but, in this case, reduction of the dimensionality of the feature vector is often required prior to the analysis. This chapter shows some applications of Bayesian learning techniques in computer vision in the agriculture and agri-food sectors. Inspection and classification of fruit and vegetables, robotics, insect identification and process automation are some of the examples shown. Problems related with the natural variability of colour, sizes and shapes of biological products, and natural illuminants are also discussed. Moreover, implementations that lead to real-time implementation are explained.
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