Bis zu 50 % günstiger als neu
3 Jahre rebuy Garantie
Professionelles Refurbishment
ElektronikMedien
Tipps & News
AppleAlle anzeigen
TabletsAlle anzeigen
HandyAlle anzeigen
Fairphone
AppleAlle anzeigen
iPhone Air Generation
GoogleAlle anzeigen
Pixel Fold
HonorAlle anzeigen
HuaweiAlle anzeigen
Honor Serie
NothingAlle anzeigen
OnePlusAlle anzeigen
OnePlus 11 GenerationOnePlus 12 Generation
SamsungAlle anzeigen
Galaxy XcoverWeitere Modelle
SonyAlle anzeigen
Weitere Modelle
XiaomiAlle anzeigen
Weitere Modelle
Tablets & eBook ReaderAlle anzeigen
Google
AppleAlle anzeigen
HuaweiAlle anzeigen
MatePad Pro Serie
MicrosoftAlle anzeigen
XiaomiAlle anzeigen
Kameras & ZubehörAlle anzeigen
ObjektiveAlle anzeigen
System & SpiegelreflexAlle anzeigen
WearablesAlle anzeigen
Fitness TrackerAlle anzeigen
SmartwatchesAlle anzeigen
Xiaomi
Konsolen & ZubehörAlle anzeigen
Lenovo Legion GoMSI Claw
NintendoAlle anzeigen
Nintendo Switch Lite
PlayStationAlle anzeigen
XboxAlle anzeigen
Audio & HiFiAlle anzeigen
KopfhörerAlle anzeigen
FairphoneGoogle
LautsprecherAlle anzeigen
GoogleYamahatonies
iPodAlle anzeigen

Handgeprüfte Gebrauchtware

Bis zu 50 % günstiger als neu

Der Umwelt zuliebe

Optischer Zustand
Beschreibung
Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blind source separation, super-resolution, and classification. The primary goal of this book is to present the theory and algorithmic considerations in using sparse models for image understanding and computer vision applications. To this end, algorithms for obtaining sparse representations and their performance guarantees are discussed in the initial chapters. Furthermore, approaches for designing overcomplete, data-adapted dictionaries to model natural images are described. The development of theory behind dictionary learning involves exploring its connection to unsupervised clustering and analyzing its generalization characteristics using principles from statistical learning theory. An exciting application area that has benefited extensively from the theory of sparse representations is compressed sensing of image and video data. Theory and algorithms pertinent to measurement design, recovery, and model-based compressed sensing are presented. The paradigm of sparse models, when suitably integrated with powerful machine learning frameworks, can lead to advances in computer vision applications such as object recognition, clustering, segmentation, and activity recognition. Frameworks that enhance the performance of sparse models in such applications by imposing constraints based on the prior discriminatory information and the underlying geometrical structure, and kernelizing the sparse coding and dictionary learning methods are presented. In addition to presenting theoretical fundamentals in sparse learning, this book provides a platform for interested readers to explore the vastly growing application domains of sparse representations.
35,30 €
Broschiert | Neu

oder

Auf Lager Versandbereit in 1-2 Werktagen
zzgl.

Du kannst wie immer einen Kaufalarm setzen, wenn du auf das gebrauchte Buch warten möchtest.

Auf Lager Versandbereit in 1-2 Werktagen
zzgl.

Handgeprüfte Gebrauchtware

Bis zu 50 % günstiger als neu

Der Umwelt zuliebe

Technische Daten


Erscheinungsdatum
24.04.2014
Sprache
Englisch
EAN
9783031011221
Herausgeber
Springer International Publishing
Serien- oder Bandtitel
Synthesis Lectures on Image, Video, and Multimedia Processing
Sonderedition
Nein
Autor
Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Pavan Turaga, Andreas Spanias
Seitenanzahl
106
Einbandart
Broschiert
Autorenporträt
Jayaraman J. Thiagarajan received his M.S. and Ph.D. degrees in Electrical Engineering from Arizona State University. He is currently a postdoctoral researcher in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. His research interests are in the areas of machine learning, computer vision, and data analysis and visualization. He has served as a reviewer for several IEEE, Elsevier, and Springer journals and conferences.Karthikeyan Natesan Ramamurthy is a research staff member in the Business Solutions and Mathematical Sciences department at the IBM Thomas J. Watson Research Center in Yorktown Heights, NY. He received his M.S. and Ph.D. degrees in Electrical Engineering from Arizona State University. His research interests are in the areas of low-dimensional signal models, machine learning, data analytics, and computer vision. He has been a reviewer for a number of IEEE and Elsevier journals and conferences.Pavan Turaga is an AssistantProfessor with the School of Arts, Media, and Engineering and the School of Electrical, Computer, and Energy Engineering at Arizona State University, since 2011. Prior to that, he was a Research Associate at the Center for Automation Research, University of Maryland, College Park, MD, from 2009-11. He received M.S. and Ph.D. degrees in Electrical Engineering from the University of Maryland, College Park, MD, in 2008 and 2009 respectively, and the B.Tech. degree in Electronics and Communication Engineering from the Indian Institute of Technology, Guwahati, India, in 2004. His research interests are in computer vision, applied statistics, and machine learning with applications to human activity analysis, video summarization, and dynamic scene analysis. He was awarded the Distinguished Dissertation Fellowship in 2009. He was selected to participate in the Emerging Leaders in Multimedia Workshop by IBM, New York, in 2008.Andreas Spanias is Professor in the School of Electrical,Computer, and Energy Engineering at Arizona State University (ASU). He is also the founder and director of the SenSIP industry consortium. His research interests are in the areas of adaptive signal processing, speech processing, and audio sensing. He and his student team developed the computer simulation software Java-DSP. He is author of two text books: Audio Processing and Coding by Wiley and DSP: An Interactive Approach. He served as Associate Editor of the IEEE Transactions on Signal Processing and as General Co-chair of IEEE ICASSP-99. He also served as the IEEE Signal Processing Vice-President for Conferences. Andreas Spanias is co-recipient of the 2002 IEEE Donald G. Fink paper prize award and was elected Fellow of the IEEE in 2003. He served as Distinguished lecturer for the IEEE Signal processing society in 2004.
Thema-Inhalt
TBC - Ingenieurswesen, Maschinenbau allgemein THR - Elektrotechnik TJF - Elektronik UYS - Digitale Signalverarbeitung (DSP)
Höhe
235 mm
Breite
19.1 cm

Hersteller: Springer, Europaplatz 3, Heidelberg, Deutschland, 69115, ProductSafety@springernature.com, Springer Nature Customer Service Center GmbH

Warnhinweise und Sicherheitsinformationen

Informationen nach EU Data Act

-.-
Leider noch keine Bewertungen
Leider noch keine Bewertungen
Schreib die erste Bewertung für dieses Produkt!
Wenn du eine Bewertung für dieses Produkt schreibst, hilfst du allen Kund:innen, die noch überlegen, ob sie das Produkt kaufen wollen. Vielen Dank, dass du mitmachst!