Articles liés à Inpainting and Denoising Challenges
Synopsis
The problem of dealing with missing or incomplete data in machine learning and computer vision arises in many applications. Recent strategies make use of generative models to impute missing or corrupted data. Advances in computer vision using deep generative models have found applications in image/video processing, such as denoising, restoration, super-resolution, or inpainting.
Inpainting and Denoising Challenges comprises recent efforts dealing with image and video inpainting tasks. This includes winning solutions to the ChaLearn Looking at People inpainting and denoising challenges: human pose recovery, video de-captioning and fingerprint restoration.
This volume starts with a wide review on image denoising, retracing and comparing various methods from the pioneer signal processing methods, to machine learning approaches with sparse and low-rank models, and recent deep learning architectures with autoencoders and variants. The following chapterspresent results from the Challenge, including three competition tasks at WCCI and ECML 2018. The top best approaches submitted by participants are described, showing interesting contributions and innovating methods. The last two chapters propose novel contributions and highlight new applications that benefit from image/video inpainting.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Acheter neuf
Afficher cet article
EUR 52,37
EUR 3,41 expédition vers Etats-Unis
Destinations, frais et délaisAutres éditions populaires du même titre
Résultats de recherche pour Inpainting and Denoising Challenges
Image d'archives
Inpainting and Denoising Challenges (The Springer Series on Challenges in Machine Learning)
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur ABLIING23Mar3113020011375
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Inpainting and Denoising Challenges
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur 38639808-n
Quantité disponible : Plus de 20 disponibles
Image d'archives
Inpainting and Denoising Challenges (Hardcover)
Edité par
Springer Nature Switzerland AG, Cham, 2019
ISBN 10 : 3030256138
ISBN 13 : 9783030256135
Neuf
Couverture rigide
Vendeur : Grand Eagle Retail, Mason, OH, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Hardcover. Etat : new. Hardcover. The problem of dealing with missing or incomplete data in machine learning and computer vision arises in many applications. Recent strategies make use of generative models to impute missing or corrupted data. Advances in computer vision using deep generative models have found applications in image/video processing, such as denoising, restoration, super-resolution, or inpainting. Inpainting and Denoising Challenges comprises recent efforts dealing with image and video inpainting tasks. This includes winning solutions to the ChaLearn Looking at People inpainting and denoising challenges: human pose recovery, video de-captioning and fingerprint restoration. This volume starts with a wide review on image denoising, retracing and comparing various methods from the pioneer signal processing methods, to machine learning approaches with sparse and low-rank models, and recent deep learning architectures with autoencoders and variants. The following chapterspresent results from the Challenge, including three competition tasks at WCCI and ECML 2018. The top best approaches submitted by participants are described, showing interesting contributions and innovating methods. The last two chapters propose novel contributions and highlight new applications that benefit from image/video inpainting. This volume starts with a wide review on image denoising, retracing and comparing various methods from the pioneer signal processing methods, to machine learning approaches with sparse and low-rank models, and recent deep learning architectures with autoencoders and variants. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783030256135
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Inpainting and Denoising Challenges
Edité par
Springer, 2019
ISBN 10 : 3030256138
ISBN 13 : 9783030256135
Ancien ou d'occasion
Couverture rigide
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 38639808
Quantité disponible : Plus de 20 disponibles
Image d'archives
Inpainting and Denoising Challenges (The Springer Series on Challenges in Machine Learning)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. In. N° de réf. du vendeur ria9783030256135_new
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Inpainting and Denoising Challenges
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur 38639808-n
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Inpainting and Denoising Challenges
Edité par
Springer International Publishing Okt 2019, 2019
ISBN 10 : 3030256138
ISBN 13 : 9783030256135
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The problem of dealing with missing or incomplete data in machine learning and computer vision arises in many applications. Recent strategies make use of generative models to impute missing or corrupted data. Advances in computer vision using deep generative models have found applications in image/video processing, such as denoising, restoration, super-resolution, or inpainting.Inpainting and Denoising Challenges comprises recent efforts dealing with image and video inpainting tasks. This includes winning solutions to the ChaLearn Looking at People inpainting and denoising challenges: human pose recovery, video de-captioning and fingerprint restoration.This volume starts with a wide review on image denoising, retracing and comparing various methods from the pioneer signal processing methods, to machine learning approaches with sparse and low-rank models, and recent deep learning architectures with autoencoders and variants. The following chapters present results from the Challenge, including three competition tasks at WCCI and ECML 2018. The top best approaches submitted by participants are described, showing interesting contributions and innovating methods. The last two chapters propose novel contributions and highlight new applications that benefit from image/video inpainting. 152 pp. Englisch. N° de réf. du vendeur 9783030256135
Quantité disponible : 2 disponible(s)
Image d'archives
Inpainting and Denoising Challenges
Vendeur : Books Puddle, New York, NY, Etats-Unis
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. N° de réf. du vendeur 26376780627
Quantité disponible : 4 disponible(s)
Image d'archives
Inpainting and Denoising Challenges
impression à la demandeVendeur : Majestic Books, Hounslow, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. Print on Demand. N° de réf. du vendeur 369264780
Quantité disponible : 4 disponible(s)
Image fournie par le vendeur
Inpainting and Denoising Challenges
Edité par
Springer, 2019
ISBN 10 : 3030256138
ISBN 13 : 9783030256135
Ancien ou d'occasion
Couverture rigide
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 38639808
Quantité disponible : Plus de 20 disponibles