Výsledky vyhledávání - "J.C. Noordam"

AUTOMATED ROSE CUTTING IN GREENHOUSES WITH 3D VISION AND ROBOTICS: ANALYSIS OF 3D VISION TECHNIQUES FOR STEM DETECTION

Autor: J.C. Noordam, C.J.E. van Heerde, F.B.T.F. Golbach, E. Wekking, Jochen Hemming, R. van Soest

Publikováno v: Acta Horticulturae 691 (2005)
Acta Horticulturae, 691, 885-889

The reduction of labour cost is the major motivation to develop a system for robot harvesting of roses in greenhouses that at least can compete with manual harvesting. Due to overlapping leaves, one of the most complicated tasks in robotic rose cutti

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::de6387cc40f7def714dfe1f8b6c30e9a
https://doi.org/10.17660/actahortic.2005.691.110

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A new procedure for the modelling and representation of classes in multivariate images

Autor: Paul Geladi, W.H.A.M. van den Broek, Lutgarde M. C. Buydens, J.C. Noordam

Publikováno v: Chemometrics and Intelligent Laboratory Systems, 75(2), 115-126
Chemometrics and Intelligent Laboratory Systems, 75, 115-126
Chemometrics and Intelligent Laboratory Systems, 75, 2, pp. 115-126
Chemometrics and Intelligent Laboratory Systems 75 (2005) 2

This paper describes a new procedure for the estimation of classes in multivariate images. The Feedback Multivariate Model Selection (FEMOS) procedure combines unsupervised and supervised classifiers with a model evaluation criterion to extract class

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e44e8ae18264350d30a1c1dc8b0b5e6
https://doi.org/10.1016/j.chemolab.2004.06.002

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Multivariate Imaging for Automated Process Control in the Agro Industry

Autor: J.C. Noordam, A. Pauli, W.A. van den Broek

Publikováno v: Agricontrol 2000 : international conference on modelling and control in agriculture, horticulture and post-harvested processing, 10-12 July, at Wageningen (Netherlands)

This article shows how multivariate exploration and classification techniques can be applied on multispectral images for automated process control. The most important step is the image segmentation by supervised or unsupervised classification of the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2779b8d19f59f90eb55197513c17a853
https://doi.org/10.1016/s1474-6670(17)40928-1

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Thorax biometrics from millimetre-wave images

Autor: Alefs, B.G., Hollander, R.J.M. den, Nennie, F.A., Houwen, E.H. van der, Bruijn, M., Mark, W. van der, J.C. Noordam, J.C.

Publikováno v: Pattern Recognition Letters, November, 15, 31, 2357-2363

Millimetre-wave imaging is an emerging technique for detection and discrimination of objects based on their radiometric signature. Recent applications focus on near range detection of metallic objects, such as weapons concealed under clothes and far

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::eb34342e8960ad3bde9788dd37e65862
http://resolver.tudelft.nl/uuid:eb32e64a-0937-44b1-b7e4-8f0df0862d64

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Clustering and Classification in Multispectral Imaging for Quality Inspection of Postharvest Products

Autor: W.H.A.M. van den Broek, J.C. Noordam

Publikováno v: Techniques and Applications of Hyperspectral Image Analysis

This chapter describes the applicability of multispectral images for quality inspection of agricultural products. In general, to grade a product based on a particular quality, (supervised or unsupervised) classification of the image is required to fi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::71c735580788c07dca98af22afaa08d6
https://doi.org/10.1002/9780470010884.ch3

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Detection and classification of latent defects and diseases on raw French fries with multispectral imaging

Autor: Lutqarde M. C. Buydens, Willie H.A.M. van den Broek, J.C. Noordam

Publikováno v: Journal of the Science of Food and Agriculture, 85(13), 2249-2259
Journal of the Science of Food and Agriculture 85 (2005) 13
Journal of the Science of Food and Agriculture, 85, 13, pp. 2249-2259
Journal of the Science of Food and Agriculture, 85, 2249-2259

This paper describes an application of both multispectral imaging and red/green/blue (RGB) colour imaging for the discrimination between different defect and diseases on raw French fries. Four different potato cultivars generally used for French frie

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::36ab8111bd9f1024365604bb27a4c173
https://research.wur.nl/en/publications/detection-and-classification-of-latent-defects-and-diseases-on-ra

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Unsupervised segmentation of predefined shapes in multivariate images

Autor: Lutgarde M. C. Buydens, J.C. Noordam, W.H.A.M. van den Broek

Publikováno v: Journal of Chemometrics, 17, 216-224
Journal of Chemometrics, 17, 4, pp. 216-224
Journal of Chemometrics, 17(4), 216-224
Journal of Chemometrics 17 (2003) 4

Fuzzy C-means (FCM) is an unsupervised clustering technique that is often used for the unsupervised segmentation of multivariate images. In traditional FCM the clustering is based on spectral information only and the geometrical relationship between

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1558ecb9f4293c1cbe0233652b8c2a4f
http://hdl.handle.net/2066/112357

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Multivariate image segmentation with cluster size insensitive fuzzy C-means

Autor: W.H.A.M. van den Broek, J.C. Noordam, L.M.C. Buydens

Publikováno v: Chemometrics and Intelligent Laboratory Systems, 64, 1, pp. 65-78
Chemometrics and Intelligent Laboratory Systems, 64, 65-78
Chemometrics and Intelligent Laboratory Systems 64 (2002) 1
Chemometrics and Intelligent Laboratory Systems, 64(1), 65-78

This paper describes a technique to overcome the sensitivity of fuzzy C-means clustering for unequal cluster sizes in multivariate images. As FCM tends to balance the number of points in each cluster, cluster centres of smaller clusters are drawn to

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3cd387b4b22798950487f208f3ad8b3e
https://doi.org/10.1016/S0169-7439(02)00052-7

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Multivariate image segmentation based on geometrically guided fuzzy C-means clustering

Autor: J.C. Noordam, W.H.A.M. van den Broek

Publikováno v: Journal of Chemometrics, 16(1), 1-11
Journal of Chemometrics 16 (2002) 1

This paper describes a new approach to geometrically guided fuzzy clustering. A modified version of fuzzy C-means (FCM) clustering, conditional FCM, is extended to incorporate a priori geometrical information from the spatial domain in order to impro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::14b9802fe411dd353676141bf209497a
https://research.wur.nl/en/publications/multivariate-image-segmentation-based-on-geometrically-guided-fuz

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Geometrically guided fuzzy c-means clustering for multivariate image segmentation

Autor: W.H.A.M. van den Broek, J.C. Noordam, L.M.C. Buydens

Publikováno v: ICPR 2000 : the 15th international conference on pattern recognition, 3-8 September, at Barcelona (Spain)
Sanfeliu, A. (ed.), ICPR-2000: 15th International Conference on Pattern Recognition. Barcelona, Spain. September 3-7, 2000. Proceedings, pp. 462-465
ICPR

Fuzzy C-means (FCM) clustering is an unsupervised clustering technique and is often used for the unsupervised segmentation of multivariate images. The segmentation of the image in meaningful regions with FCM is based on spectral information only. The

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::22cc38343eaeaebf6a6c2eddda308ea4
https://research.wur.nl/en/publications/geometrically-guided-fuzzy-c-means-clustering-for-multivariate-im

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