Monteiro sildomar (11 résultats)

Paperback. Etat : New.

Etat : New.

PF. Etat : New.

Etat : New. In English.

Taschenbuch. Etat : Neu. Automatic Hyperspectral Data Analysis | A machine learning approach to high dimensional feature extraction | Sildomar Monteiro | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2010 | VDM Verlag Dr. Müller | EAN 9783639255164 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu. …

Paperback. Etat : New.

Paperback. Etat : Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Monteiro SildomarSildomar Monteiro is Research Fellow in the Australian Centre for Field Robotics at Sydney University. He was awarded a JSPS postdoctoral fellowship in 2007. His research areas include machine learning, computer visi.…

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Advances in spectroscopy sensors have allowed the acquisition of ever-increasing volumes of data from scenes, either remotely, by air- or space-borne devices, or locally, by hand-held spectrometers or stand-alone cameras. With this boom in the amount of data available has also come a greater need for extracting useful information efficiently and for developing automated methods for novel applications. Traditional approaches to spectral analysis often require a great deal of human effort and prior knowledge, and have difficulty in processing high dimensional data sets provided by new sensors. This book, therefore, provides an alternative approach to select relevant features from hyperspectral data utilizing machine learning to automate the analysis. The methods are developed in the context of two applications: in biomedical imaging and in precision agriculture. The techniques discussed should be useful to graduate students and researchers in computer science and engineering interested in hyperspectral imaging, remote sensing or optimization for high dimensional data.…