國立虎尾科技大學 |

Inpainting and Denoising Challenges

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Inpainting and Denoising Challenges/ edited by Sergio Escalera, Stephane Ayache, Jun Wan, Meysam Madadi, Umut Güçlü, Xavier Baró.
other author:	Escalera, Sergio.
Description:	VIII, 144 p. 65 illus., 56 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence. -
Online resource:	https://doi.org/10.1007/978-3-030-25614-2
ISBN:	9783030256142

Inpainting and Denoising Challenges
Inpainting and Denoising Challenges[electronic resource] /edited by Sergio Escalera, Stephane Ayache, Jun Wan, Meysam Madadi, Umut Güçlü, Xavier Baró. - 1st ed. 2019. - VIII, 144 p. 65 illus., 56 illus. in color.online resource. - The Springer Series on Challenges in Machine Learning,2520-131X. - The 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

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

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Inpainting and Denoising Challenges
LDR:03636nam a22004095i 4500 001 1015585
003 DE-He213
005 20200703032308.0
007 cr nn 008mamaa
008 210106s2019 gw | s |||| 0|eng d
020 $a 9783030256142 $9 978-3-030-25614-2
024 7 $a 10.1007/978-3-030-25614-2 $2 doi
035 $a 978-3-030-25614-2
050 4 $a Q334-342
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
245 1 0 $a Inpainting and Denoising Challenges $h [electronic resource] / $c edited by Sergio Escalera, Stephane Ayache, Jun Wan, Meysam Madadi, Umut Güçlü, Xavier Baró.
250 $a 1st ed. 2019.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a VIII, 144 p. 65 illus., 56 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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 Artificial intelligence. $3 559380
650 0 $a Optical data processing. $3 639187
650 0 $a Pattern recognition. $3 1253525
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Image Processing and Computer Vision. $3 670819
650 2 4 $a Pattern Recognition. $3 669796
700 1 $a Escalera, Sergio. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 815991
700 1 $a Ayache, Stephane. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1309747
700 1 $a Wan, Jun. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1309748
700 1 $a Madadi, Meysam. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1309749
700 1 $a Güçlü, Umut. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1281360
700 1 $a Baró, Xavier. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1281358
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030256135
776 0 8 $i Printed edition: $z 9783030256159
776 0 8 $i Printed edition: $z 9783030256166
830 0 $a The Springer Series on Challenges in Machine Learning, $x 2520-131X $3 1280562
856 4 0 $u https://doi.org/10.1007/978-3-030-25614-2
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)