References of "Kleynen, Olivier"
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See detailAutomatic grading of Bi-colored apples by multispectral machine vision
Unay, Devrim; Gosselin, Bernard; Kleynen, Olivier et al

in Computers & Electronics in Agriculture (2011)

In this paper we present a novel application work for grading of apple fruits by machine vision. Following precise segmentation of defects by minimal confusion with stem/calyx areas on multispectral ... [more ▼]

In this paper we present a novel application work for grading of apple fruits by machine vision. Following precise segmentation of defects by minimal confusion with stem/calyx areas on multispectral images, statistical, textural and geometric features are extracted from the segmented area. Using these features, statistical and syntactical classifiers are trained for two- and multi-category grading of the fruits. Results showed that feature selection provided improved performance by retaining only the important features, and statistical classifiers outperformed their syntactical counterparts. Compared to the state-of-the-art, our two-category grading solution achieved better recognition rates (93.5% overall accuracy). In this work we further provided a more realistic multi-category grading solution, where different classification architectures are evaluated. Our observations showed that the single-classifier architecture is computationally less demanding, while the cascaded one is more accurate. [less ▲]

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See detailQuality evaluation of Apples
Leemans, Vincent ULg; Kleynen, Olivier

in Sun, Da-Wen (Ed.) Computer Vision Technology for Food Quality Evaluation (2008)

The chapter describe how to asses apple quality by using machine vision. The concept of quality of an apple is detailed. The manipulation of the fruits, the lighting and the acquisition techniques are ... [more ▼]

The chapter describe how to asses apple quality by using machine vision. The concept of quality of an apple is detailed. The manipulation of the fruits, the lighting and the acquisition techniques are reviewed. The shape and colour assessment are summarised. The evaluation of the surface defects is [less ▲]

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See detailDetermination of plant height for weed detection in stereoscopic images
Piron, Alexis ULg; Leemans, Vincent ULg; Kleynen, Olivier et al

in AGENG 2008 Conference - Agricultural & Biosystems Engineering for a Sustainable World (2008)

The aim of this study was twofold. The first goal was to acquire high accuracy stereoscopic images of small-scale field scenes, the second to examine the potential of plant height as a discriminant factor ... [more ▼]

The aim of this study was twofold. The first goal was to acquire high accuracy stereoscopic images of small-scale field scenes, the second to examine the potential of plant height as a discriminant factor between crop and weed, within carrot rows. Emphasis was put on how to determine actual plant height taking into account the variable distance from camera to ground and ground irregularities for in-field measurements. Multispectral stereoscopic images were taken over a period of 19 days starting one week after crop emergence and seven weed species were considered. Images were acquired with a mobile vision system consisting in a filter wheel based multispectral camera and a video projector. The stereoscopy technique used belonged to the coded structured light family. The stereoscopic acquisition method yielded good results despite the numerous stereoscopic difficulties exhibited by the scenes. A plant height parameter as opposed to distance from camera to plant pixels gave better results for classification (classification accuracy of up [less ▲]

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See detailDevelopment of a multi-spectral vision system for the detection of defects on apples
Kleynen, Olivier; Leemans, Vincent ULg; Destain, Marie-France ULg

in Journal of Food Engineering (2005), 69(1), 41-49

A method to sort 'Jonagold' apples based on the presence of defects was proposed. A multi-spectral vision system including four wavelength bands in the visible/NIR range was developed. Multi-spectral ... [more ▼]

A method to sort 'Jonagold' apples based on the presence of defects was proposed. A multi-spectral vision system including four wavelength bands in the visible/NIR range was developed. Multi-spectral images of sound and defective fruits were acquired tending to cover the whole colour variability of this bicolour apple variety. Defects were grouped into four categories: slight defects, more serious defects, defects leading to the rejection of the fruit and recent bruises. Stem-ends/calyxes were detected using a correlation pattern matching algorithm. The efficiency of this method depended on the orientation of the stem-end/calyx according to the optical axis of the camera. Defect segmentation consisted in a pixel classification procedure based on the Bayes' theorem and non-parametric models of the sound and defective tissue. Fruit classification tests were performed in order to evaluate the efficiency of the proposed method. No error was made on rejected fruits and high classification rates were reached for apples presenting serious defects and recent bruises. Fruits with slight defects presented a more important misclassification rate but those errors fitted however the quality tolerances of the European standard. Considering an actual ratio of sound fruits of 90%, less than 2% of defective fruits were classified into the sound ones. (c) 2004 Elsevier Ltd. All rights reserved. [less ▲]

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See detailDetection of defects on fruits by machine vision and unsupervised segmentation
Kleynen, Olivier; Destain, Marie-France ULg

in AgEng 2004 Conference (2004, September)

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 ... [more ▼]

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). [less ▲]

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See detailDetection of defects on fruits by machine vision and unsupervised segmentation
Kleynen, Olivier; Destain, Marie-France ULg

in AgEng 2004 Conference - Engineering the fuiture (2004)

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 ... [more ▼]

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). [less ▲]

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See detailExternal quality grading of Jonagold apples using a multi-spectral vision system
Kleynen, Olivier; Leemans, Vincent ULg; Destain, Marie-France ULg

in Balsa-Canto E., Mora J.; Onate E. (Eds.) II International Workshop - Information Technologies and Computing Techniques for the Agro-Food Sector (2003)

This paper presents a method to sort Jonagold apples using a four bands multi-spectral image acquisition device. Multi-spectral images of sound tissue and various defects were acquired. Defects could be ... [more ▼]

This paper presents a method to sort Jonagold apples using a four bands multi-spectral image acquisition device. Multi-spectral images of sound tissue and various defects were acquired. Defects could be divided into four classes: slight defects (e.g. small russet), more serious defects (scar tissue), defects leading to the rejection of the fruit (e.g. rot) and recent bruises (between one hour and two hours old). Image segmentation was based on the Bayes' theorem. Each pixel of the fruit was classified into 'healthy' or 'defect' classes according to the probability distribution of the spectral components of each class. Once segmented, the fruit was graded by linear discriminant analysis on the basis of the relative area of the defect and statistical parameters computed on the spectral components of the two tissues classes. Results (cross validation) showed 94% and 84% if the sound and defective fruits respectively well classified. Most of the misclassified defective fruits (89%) belonged to the slight defect category. [less ▲]

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See detailEvolution of pressure distribution during apple compression tests measured with tactile sensors
Kleynen, Olivier; de la Cierva, Sonia; Destain, Marie-France ULg

in Acta Horticulturae (2003), (604),

The paper analyses the ability of thin-film tactile sensors in providing information during static compression tests of ‘Jonagold’ apples (Malus pumila) of different ripeness stages. Such sensors are able ... [more ▼]

The paper analyses the ability of thin-film tactile sensors in providing information during static compression tests of ‘Jonagold’ apples (Malus pumila) of different ripeness stages. Such sensors are able to measure the contact surface and the interfacial pressure distribution during compression of fruits, this latter being characterised by suitable mathematical parameters. Results of compression tests between two flat steel plates are presented. The differentiated evolution of the pressure distribution according to the fruit maturity is pointed out. Ability of the sensor in evaluating the firmness is also discussed. [less ▲]

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See detailContrôle des produits agroalimentaires par analyse d'images
Leemans, Vincent ULg; Kleynen, Olivier; Destain, Marie-France ULg

in Nouveaux capteurs pour la maîtrise de la qualité des produits agroalimentaires - Etat de la recherche européenne (2002, November 22)

Présentation de méthodes utilisées dans un processus de contrôle de qualité basé sur l'analyse d'image, en se focalisant sur la couleur et la forme des produits et leur classification

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See detailEvaluation of tactile sensors in apple firmness measurement
Petit, Catherine; Leemans, Vincent ULg; Kleynen, Olivier et al

in Proceedings of the Conference 'Physical Methods in Agriculture, Appoach to precision and quality' (2001)

The objective of the work consists to develop a non-destructive fruit firmness measurement. The tactile sensor technology was chosen. The force measurement acting between the fruit-sensor as well as the ... [more ▼]

The objective of the work consists to develop a non-destructive fruit firmness measurement. The tactile sensor technology was chosen. The force measurement acting between the fruit-sensor as well as the contact area are coupled with other meaqsurements, like the color of the fruit. The results of the measurements are compared with the firmness reference values (Magness-Taylor test) and also with the acoustic impulse response technique. Several parameters correlated with firmness are proposed. [less ▲]

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