LEADER 04688nam 22006375 450 001 9910349272303321 005 20200703032308.0 010 $a3-030-25614-6 024 7 $a10.1007/978-3-030-25614-2 035 $a(CKB)4100000009590429 035 $a(MiAaPQ)EBC5962824 035 $a(DE-He213)978-3-030-25614-2 035 $a(PPN)242824951 035 $a(EXLCZ)994100000009590429 100 $a20191016d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aInpainting and Denoising Challenges /$fedited by Sergio Escalera, Stephane Ayache, Jun Wan, Meysam Madadi, Umut Güçlü, Xavier Baró 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (151 pages) 225 1 $aThe Springer Series on Challenges in Machine Learning,$x2520-131X 311 $a3-030-25613-8 327 $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. 330 $aThe problem of dealing with missing or incomplete data in machine learning and computer vision arises in many applications. Recent strategies make use of generative models to impute missing or corrupted data. Advances in computer vision using deep generative models have found applications in image/video processing, such as denoising, restoration, super-resolution, or inpainting. Inpainting and Denoising Challenges comprises recent efforts dealing with image and video inpainting tasks. This includes winning solutions to the ChaLearn Looking at People inpainting and denoising challenges: human pose recovery, video de-captioning and fingerprint restoration. This volume starts with a wide review on image denoising, retracing and comparing various methods from the pioneer signal processing methods, to machine learning approaches with sparse and low-rank models, and recent deep learning architectures with autoencoders and variants. The following chapters present results from the Challenge, including three competition tasks at WCCI and ECML 2018. The top best approaches submitted by participants are described, showing interesting contributions and innovating methods. The last two chapters propose novel contributions and highlight new applications that benefit from image/video inpainting. . 410 0$aThe Springer Series on Challenges in Machine Learning,$x2520-131X 606 $aArtificial intelligence 606 $aOptical data processing 606 $aPattern recognition 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 615 0$aArtificial intelligence. 615 0$aOptical data processing. 615 0$aPattern recognition. 615 14$aArtificial Intelligence. 615 24$aImage Processing and Computer Vision. 615 24$aPattern Recognition. 676 $a621.3822 676 $a621.3822 702 $aEscalera$b Sergio$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aAyache$b Stephane$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aWan$b Jun$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMadadi$b Meysam$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGüçlü$b Umut$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBaró$b Xavier$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910349272303321 996 $aInpainting and Denoising Challenges$92526876 997 $aUNINA