Reference : Detection of defects on fruits by machine vision and unsupervised segmentation
Scientific journals : Article
Engineering, computing & technology : Multidisciplinary, general & others
Detection of defects on fruits by machine vision and unsupervised segmentation
Kleynen, Olivier [ > > ]
Destain, Marie-France mailto [Université de Liège - ULiège > Sciences et technologie de l'environnement > Mécanique et construction >]
AgEng 2004 Conference - Engineering the fuiture
Technologisch Instituut
[en] mean shift ; mltispectral images ; fruits
[en] Defect detection on fruits by machine vision is a complex task. Indeed, the sound tissue colour is not uniform and the defects present a wide variability in colour, shape and texture. Mostly often, images are acquired by conventional RGB cameras and defect segmentation is performed by algorithms based on Bayes'rules. The efficiency of these methods can be improved firstly by acquiring images with a dedicated vision system (multi-spectal imager) and secondly by implementing unsupervised segmentation methods (based on the 'mean shift' procedure; Comaniciu and Meer, 2002).
Région wallonne : Direction générale des Technologies, de la Recherche et de l'Energie - DGTRE
Researchers ; Professionals

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