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020 _a9783030256142
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024 7 _a10.1007/978-3-030-25614-2
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
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072 7 _aUYQ
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082 0 4 _a006.3
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245 1 0 _aInpainting and Denoising Challenges
_h[electronic resource] /
_cedited by Sergio Escalera, Stephane Ayache, Jun Wan, Meysam Madadi, Umut Güçlü, Xavier Baró.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aVIII, 144 p. 65 illus., 56 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aThe Springer Series on Challenges in Machine Learning,
_x2520-1328
505 0 _a1. 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 _aThe 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 _aArtificial intelligence.
650 0 _aComputer vision.
650 0 _aPattern recognition systems.
650 1 4 _aArtificial Intelligence.
650 2 4 _aComputer Vision.
650 2 4 _aAutomated Pattern Recognition.
700 1 _aEscalera, Sergio.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aAyache, Stephane.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aWan, Jun.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aMadadi, Meysam.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aGüçlü, Umut.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aBaró, Xavier.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030256135
776 0 8 _iPrinted edition:
_z9783030256159
776 0 8 _iPrinted edition:
_z9783030256166
830 0 _aThe Springer Series on Challenges in Machine Learning,
_x2520-1328
856 4 0 _uhttps://doi.org/10.1007/978-3-030-25614-2
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
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