Reference : Detection of defects on fruits by machine vision and unsupervised segmentation
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/2268/81214
Detection of defects on fruits by machine vision and unsupervised segmentation
English
Kleynen, Olivier [ > > ]
Destain, Marie-France mailto [Université de Liège - ULiège > Sciences et technologie de l'environnement > Mécanique et construction >]
Sep-2004
AgEng 2004 Conference
Technological Institute
1006-1007
Yes
No
International
90-76019-258
Belgium
AgEng 2004 Conference - Engineering the future
12-16 Semptember 2004
Technologisch Instituut
Leuven
Belgium
[en] mean shift ; multispectral 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-spectral imager) and secondly by implementing unsupervised segmentation methods (method derived from the 'mean shift' procedure of Camaniciu and Meer, 2002).
Région wallonne : Direction générale des Technologies, de la Recherche et de l'Energie - DGTRE
CAPA
Researchers ; Professionals
http://hdl.handle.net/2268/81214

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