國立高雄大學圖資館 |

Language: English

Back

Inpainting and denoising challenges

Escalera, Sergio.

Inpainting and denoising challenges

Record Type:	Electronic resources : Monograph/item
Title/Author:	Inpainting and denoising challengesedited by Sergio Escalera ... [et al.].
other author:	Escalera, Sergio.
Published:	Cham :Springer International Publishing :2019.
Description:	viii, 144 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Signal processingDigital techniques.
Online resource:	https://doi.org/10.1007/978-3-030-25614-2
ISBN:	9783030256142$q(electronic bk.)

Inpainting and denoising challenges
Inpainting and denoising challenges[electronic resource] /edited by Sergio Escalera ... [et al.]. - Cham :Springer International Publishing :2019. - viii, 144 p. :ill. (some col.), digital ;24 cm. - The Springer series on challenges in machine learning,2520-131X. - Springer series on challenges in machine learning..

1. A Brief Review of Image Denoising Algorithms and Beyond -- 2. ChaLearn Looking at People: Inpainting and Denoising Challenges -- 3. U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting -- 4. FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural Networks -- 5. Iterative Application of Autoencoders for Video Inpainting and Fingerprint Denoising -- 6. Video DeCaptioning using U-Net with Stacked Dilated Convolutional Layers -- 7. Joint Caption Detection and Inpainting using Generative Network -- 8. Generative Image Inpainting for Person Pose Generation -- 9. Person Inpainting with Generative Adversarial Networks -- 10. Road Layout Understanding by Generative Adversarial Inpainting -- 11. Photo-realistic and Robust Inpainting of Faces using Refinement GANs.

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.

ISBN: 9783030256142$q(electronic bk.)

Standard No.: 10.1007/978-3-030-25614-2doiSubjects--Topical Terms:

182126
Signal processing
--Digital techniques.

LC Class. No.: TK5102.9 / .I56 2019

Dewey Class. No.: 621.3822

Inpainting and denoising challenges
LDR:03212nmm a2200337 a 4500 001 567893
003 DE-He213
005 20191027182334.0
006 m d
007 cr nn 008maaau
008 200611s2019 sz s 0 eng d
020 $a 9783030256142$q(electronic bk.)
020 $a 9783030256135$q(paper)
024 7 $a 10.1007/978-3-030-25614-2 $2 doi
035 $a 978-3-030-25614-2
040 $a GP $c GP
041 0 $a eng
050 4 $a TK5102.9 $b .I56 2019
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 621.3822 $2 23
090 $a TK5102.9 $b .I58 2019
245 0 0 $a Inpainting and denoising challenges $h [electronic resource] / $c edited by Sergio Escalera ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a viii, 144 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a The Springer series on challenges in machine learning, $x 2520-131X
505 0 $a 1. A Brief Review of Image Denoising Algorithms and Beyond -- 2. ChaLearn Looking at People: Inpainting and Denoising Challenges -- 3. U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting -- 4. FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural Networks -- 5. Iterative Application of Autoencoders for Video Inpainting and Fingerprint Denoising -- 6. Video DeCaptioning using U-Net with Stacked Dilated Convolutional Layers -- 7. Joint Caption Detection and Inpainting using Generative Network -- 8. Generative Image Inpainting for Person Pose Generation -- 9. Person Inpainting with Generative Adversarial Networks -- 10. Road Layout Understanding by Generative Adversarial Inpainting -- 11. Photo-realistic and Robust Inpainting of Faces using Refinement GANs.
520 $a 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.
650 0 $a Signal processing $x Digital techniques. $3 182126
650 0 $a Machine learning. $3 188639
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Image Processing and Computer Vision. $3 274051
650 2 4 $a Pattern Recognition. $3 273706
700 1 $a Escalera, Sergio. $3 530826
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Springer series on challenges in machine learning. $3 785220
856 4 0 $u https://doi.org/10.1007/978-3-030-25614-2
950 $a Computer Science (Springer-11645)