Articoli correlati a Image Understanding using Sparse Representations

Image Understanding using Sparse Representations - Brossura

 
9783031033780: Image Understanding using Sparse Representations

Al momento non sono disponibili copie per questo codice ISBN.

Sinossi

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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

  • EditoreSpringer
  • Data di pubblicazione2014
  • ISBN 10 3031033787
  • ISBN 13 9783031033780
  • RilegaturaCopertina flessibile
  • LinguaInglese
  • Numero di pagine120
  • Contatto del produttorenon disponibile

(nessuna copia disponibile)

Cerca:



Inserisci un desiderata

Non riesci a trovare il libro che stai cercando? Continueremo a cercarlo per te. Se uno dei nostri librai lo aggiunge ad AbeBooks, ti invieremo una notifica!

Inserisci un desiderata

Altre edizioni note dello stesso titolo

9781627053594: Image Understanding using Sparse Representations

Edizione in evidenza

ISBN 10:  162705359X ISBN 13:  9781627053594
Casa editrice: Morgan & Claypool Publishers, 2014
Brossura