LEADER 04200nam 2200661Ia 450 001 9910437584203321 005 20200520144314.0 010 $a1-283-90995-2 010 $a1-4471-4652-2 024 7 $a10.1007/978-1-4471-4652-0 035 $a(CKB)2670000000278372 035 $a(EBL)1081756 035 $a(OCoLC)817224729 035 $a(SSID)ssj0000798785 035 $a(PQKBManifestationID)11440639 035 $a(PQKBTitleCode)TC0000798785 035 $a(PQKBWorkID)10757334 035 $a(PQKB)10946650 035 $a(DE-He213)978-1-4471-4652-0 035 $a(MiAaPQ)EBC1081756 035 $a(PPN)16829401X 035 $a(EXLCZ)992670000000278372 100 $a20121112d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aImaging spectroscopy for scene analysis /$fAntonio Robles-Kelly, Cong Phuoc Huynh 205 $a1st ed. 2013. 210 $aLondon ;$aNew York $cSpringer$dc2013 215 $a1 online resource (273 p.) 225 0$aAdvances in computer vision and pattern recognition 300 $aDescription based upon print version of record. 311 $a1-4471-5838-5 311 $a1-4471-4651-4 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Spectral Image Acquisition -- Spectral Image Formation Process -- Reflectance Modelling -- Illuminant Power Spectrum -- Photometric Invariance -- Spectrum Representation -- Material Discovery -- Reflection Geometry -- Polarisation of Light -- Shape and Refractive Index from Polarisation. 330 $aIn contrast with trichromatic image sensors, imaging spectroscopy can capture the properties of the materials in a scene. This implies that scene analysis using imaging spectroscopy has the capacity to robustly encode material signatures, infer object composition and recover photometric parameters. This landmark text/reference presents a detailed analysis of spectral imaging, describing how it can be used in elegant and efficient ways for the purposes of material identification, object recognition and scene understanding. The opportunities and challenges of combining spatial and spectral information are explored in depth, as are a wide range of applications from surveillance and computational photography, to biosecurity and resource exploration. Topics and features: Discusses spectral image acquisition by hyperspectral cameras, and the process of spectral image formation Examines models of surface reflectance, the recovery of photometric invariants, and the estimation of the illuminant power spectrum from spectral imagery Describes spectrum representations for the interpolation of reflectance and radiance values, and the classification of spectra Reviews the use of imaging spectroscopy for material identification Explores the recovery of reflection geometry from image reflectance Investigates spectro-polarimetric imagery, and the recovery of object shape and material properties using polarimetric images captured from a single view An essential resource for researchers and graduate students of computer vision and pattern recognition, this comprehensive introduction to imaging spectroscopy for scene analysis will also be of great use to practitioners interested in shape analysis employing polarimetric imaging, and material recognition and classification using hyperspectral or multispectral data. 410 0$aAdvances in Computer Vision and Pattern Recognition,$x2191-6586 606 $aComputer vision 606 $aPattern recognition systems 606 $aSpectrum analysis$xData processing 606 $aImage processing$xDigital techniques 615 0$aComputer vision. 615 0$aPattern recognition systems. 615 0$aSpectrum analysis$xData processing. 615 0$aImage processing$xDigital techniques. 676 $a621.367 700 $aRobles-Kelly$b Antonio$01063389 701 $aJuynh$b Cong Phuoc$01751931 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437584203321 996 $aImaging spectroscopy for scene analysis$94187073 997 $aUNINA LEADER 01553nam 22004933 450 001 9910947535003321 005 20250114080256.0 010 $a9783031733246 010 $a303173324X 035 $a(CKB)37178136200041 035 $a(MiAaPQ)EBC31876220 035 $a(Au-PeEL)EBL31876220 035 $a(OCoLC)1485005763 035 $a(EXLCZ)9937178136200041 100 $a20250114d2025 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIntelligent Computing and Optimization $eProceedings of the 7th International Conference on Intelligent Computing and Optimization 2023 (ICO2023), Volume 4 205 $a1st ed. 210 1$aCham :$cSpringer,$d2025. 210 4$dİ2024. 215 $a1 online resource (997 pages) 225 1 $aLecture Notes in Networks and Systems Series ;$vv.1169 311 08$a9783031733239 311 08$a3031733231 410 0$aLecture Notes in Networks and Systems Series 676 $a006.3 700 $aVasant$b Pandian$01073665 701 $aPanchenko$b Vladimir$01439020 701 $aMunapo$b Elias$01439022 701 $aWeber$b Gerhard-Wilhelm$01368889 701 $aThomas$b J. Joshua$01439021 701 $aIntan$b Rolly$01780038 701 $aShamsul Arefin$b Mohammad$01439019 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910947535003321 996 $aIntelligent Computing and Optimization$94303685 997 $aUNINA