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001			291170
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007			cr nn 008mamaa
008			150903s2009 xxk| o |||| 0|eng d
020			_a9781848825093 _99781848825093
024	7		_a10.1007/9781848825093 _2doi
035			_avtls000344457
039		9	_a201509030407 _bVLOAD _c201405050307 _dVLOAD _y201402061257 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aTJ210.2-211.495
100	1		_aFehlman, William L. _eautor _9322336
245	1	0	_aMobile Robot Navigation with Intelligent Infrared Image Interpretation / _cby William L. Fehlman, Mark K. Hinders.
264		1	_aLondon : _bSpringer London, _c2009.
300			_axxx, 274 páginas 86 ilustraciones _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
500			_aSpringer eBooks
505	0		_aand Overview -- Data Acquisition -- Thermal Feature Generation -- Thermal Feature Selection -- Adaptive Bayesian Classification Model -- Conclusions and Future Research Directions.
520			_aMobile robots require the ability to make decisions such as "go through the hedges" or "go around the brick wall." Mobile Robot Navigation with Intelligent Infrared Image Interpretation describes in detail an alternative to GPS navigation: a physics-based adaptive Bayesian pattern classification model that uses a passive thermal infrared imaging system to automatically characterize non-heat generating objects in unstructured outdoor environments for mobile robots. The resulting classification model complements an autonomous robot’s situational awareness by providing the ability to classify smaller structures commonly found in the immediate operational environment. The approach described in this book is an application of Bayesian statistical pattern classification where learning involves labeled classes of data (supervised classification), assumes no formal structure regarding the density of the data in the classes (nonparametric density estimation), and makes direct use of prior knowledge regarding an object class’s existence in a robot’s immediate area of operation when making decisions regarding class assignments for unknown objects. The result is a novel classification model which not only displays exceptional performance in characterizing non-heat generating outdoor objects in thermal scenes, but also outperforms the traditional KNN and Parzen classifiers. Mobile Robot Navigation with Intelligent Infrared Image Interpretation will be of interest to researchers and developers of advanced mobile robots in academic, industrial and military sectors. Advanced undergraduates studying robot sensor interpretation, pattern classification or infrared physics will also appreciate this book.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aHinders, Mark K. _eautor _9322337
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781848825086
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-84882-509-3 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c291170 _d291170