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<p style="margin-right:0cm;margin-left:0cm;font-size:12pt;font-family:"Times New Roman",serif"><strong><span style="font-size:15pt" lang="EN-AU">Springer
book: Inpainting and Denoising, call for book chapters<span></span></span></strong></p>
<p style="text-align:justify;margin-right:0cm;margin-left:0cm;font-size:12pt;font-family:"Times New Roman",serif"><span style="font-size:11pt;font-family:"Arial",sans-serif;color:black" lang="EN-AU">Contact:
<a href="mailto:sergio.escalera.guerrero@gmail.com">sergio.escalera.guerrero@gmail.com</a> </span><span lang="EN-AU"><span></span></span></p>
<p style="margin-right:0cm;margin-left:0cm;font-size:12pt;font-family:"Times New Roman",serif"><span style="font-size:11pt;font-family:"Arial",sans-serif;color:black" lang="EN-AU">************************************************************************</span><span lang="EN-AU"><span></span></span></p>
<p style="margin-right:0cm;margin-left:0cm;font-size:12pt;font-family:"Times New Roman",serif"><strong><u><span lang="EN-AU">Aims and scope</span></u></strong><strong><span lang="EN-AU">: </span></strong><span lang="EN-AU"> The problem of dealing with missing data
or incomplete data in machine learning 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 [1],
restoration [2], super-resolution [3], or inpainting [4,5]. We focus on image
and video inpainting tasks, that might benefit from novel methods such as
Generative Adversarial Networks (GANs) [6,7] or Residual connections [8,9].
Solutions to the inpainting problem may be useful in a wide variety of computer
vision tasks.<span></span></span></p>
<p style="margin-right:0cm;margin-left:0cm;font-size:12pt;font-family:"Times New Roman",serif"><strong><u><span lang="EN-AU">Book chapter
contribution</span></u></strong><strong><span lang="EN-AU">: </span></strong><span lang="EN-AU">The
scope comprises all aspects of image and video inpainting and denoising.
Including but not limited to the following topics:<span></span></span></p>
<ul style="margin-bottom:0cm" type="disc">
<li class="MsoNormal" style="line-height:normal;margin:0cm 0cm 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span lang="EN-AU">2D/3D human pose recovery under
occlusion,<span></span></span></li>
<li class="MsoNormal" style="line-height:normal;margin:0cm 0cm 8pt;font-size:11pt;font-family:"Calibri",sans-serif">human
inpainting,<span></span></li>
<li class="MsoNormal" style="line-height:normal;margin:0cm 0cm 8pt;font-size:11pt;font-family:"Calibri",sans-serif">human
retexturing,<span></span></li>
<li class="MsoNormal" style="line-height:normal;margin:0cm 0cm 8pt;font-size:11pt;font-family:"Calibri",sans-serif">video
decaptioning,<span></span></li>
<li class="MsoNormal" style="line-height:normal;margin:0cm 0cm 8pt;font-size:11pt;font-family:"Calibri",sans-serif">temporal
occlusion recovery,<span></span></li>
<li class="MsoNormal" style="line-height:normal;margin:0cm 0cm 8pt;font-size:11pt;font-family:"Calibri",sans-serif">object
recognition under occlusion,<span></span></li>
<li class="MsoNormal" style="line-height:normal;margin:0cm 0cm 8pt;font-size:11pt;font-family:"Calibri",sans-serif">fingerprint
recognition,<span></span></li>
<li class="MsoNormal" style="line-height:normal;margin:0cm 0cm 8pt;font-size:11pt;font-family:"Calibri",sans-serif">fingerprint
denoising,<span></span></li>
<li class="MsoNormal" style="line-height:normal;margin:0cm 0cm 8pt;font-size:11pt;font-family:"Calibri",sans-serif">future
frame video prediction,<span></span></li>
<li class="MsoNormal" style="line-height:normal;margin:0cm 0cm 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span lang="EN-AU">unsupervised learning for
missing data recovery and/or denoising,<span></span></span></li>
<li class="MsoNormal" style="line-height:normal;margin:0cm 0cm 8pt;font-size:11pt;font-family:"Calibri",sans-serif"><span lang="EN-AU">new data and applications of
inpainting and/or denoising.<span></span></span></li>
</ul>
<p style="margin-right:0cm;margin-left:0cm;font-size:12pt;font-family:"Times New Roman",serif"><span lang="EN-AU">Book chapter submission
instructions: <span></span></span></p>
<p style="margin-right:0cm;margin-left:0cm;font-size:12pt;font-family:"Times New Roman",serif"><span lang="EN-AU"><a href="http://chalearnlap.cvc.uab.es/workshop/29/schedule/" style="color:rgb(5,99,193);text-decoration:underline">http://chalearnlap.cvc.uab.es/workshop/29/schedule/</a><span></span></span></p>
<p style="margin-right:0cm;margin-left:0cm;font-size:12pt;font-family:"Times New Roman",serif"><span lang="EN-AU">There is no page limit. Authors
have to use </span><a href="https://www.springer.com/gp/authors-editors/book-authors-editors/manuscript-preparation/5636" style="color:rgb(5,99,193);text-decoration:underline"><span lang="EN-AU">this template</span></a><span lang="EN-AU">. Contributions will be published
within a volume in this series: </span><a href="http://www.springer.com/series/15602" style="color:rgb(5,99,193);text-decoration:underline"><span lang="EN-AU">http://www.springer.com/series/15602</span></a><span lang="EN-AU">. <span></span></span></p>
<p style="margin-right:0cm;margin-left:0cm;font-size:12pt;font-family:"Times New Roman",serif"><strong><span lang="EN-AU">References:</span></strong><span lang="EN-AU"><span></span></span></p>
<p style="margin-right:0cm;margin-left:0cm;font-size:12pt;font-family:"Times New Roman",serif"><span lang="EN-AU">[1] V. Jain and
S. Seung, “Natural image denoising with convolutional networks,” in Advances in
Neural Information Processing Systems, 2009, pp. 769–776.<br>
[2] L. Xu, J. S. Ren, C. Liu, and J. Jia, “Deep convolutional neural
network for image deconvolution,” in Advances in Neural Information
Processing Systems 27, Z. Ghahramani, M. Welling, C. Cortes, N.
D. Lawrence, and K. Q. Weinberger, Eds. Curran Associates, Inc.,
2014, pp. 1790–1798.<br>
[3] C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using
deep convolutional networks,” IEEE transactions on pattern analysis and
machine intelligence, vol. 38, no. 2, pp. 295–307, 2016.<br>
[4] J. Xie, L. Xu, and E. Chen, “Image denoising and inpainting with deep
neural networks,” in Advances in Neural Information Processing Systems,
2012, pp. 341–349.<span></span></span></p>
<p style="margin-right:0cm;margin-left:0cm;font-size:12pt;font-family:"Times New Roman",serif"><span lang="EN-AU">[5] A. Newson,
A. Almansa, M. Fradet, Y. Gousseau, and P. P´erez, “Video inpainting of complex
scenes,” SIAM Journal on Imaging Sciences, vol. 7, no. 4, pp. 1993–2019,
2014.<br>
[6] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S.
Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in
Advances in neural information processing systems, 2014, pp. 2672–2680.<br>
[7] D. Pathak, P. Kr¨ahenb¨uhl, J. Donahue, T. Darrell, and A. Efros,
“Context encoders: Feature learning by inpainting,” in Computer Vision and
Pattern Recognition (CVPR), 2016.<br>
[8] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for
image recognition,” in The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), June 2016.<br>
[9] X.-J. Mao, C. Shen, and Y.-B. Yang, “Image Restoration Using
Convolutional Auto-encoders with Symmetric Skip Connections,” ArXiv
e-prints, Jun. 2016.<span></span></span></p>
<br clear="all"><br>-- <br><div class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div><font size="1"><span style="color:rgb(68,68,68)"><b><span style="color:rgb(102,102,102)">Dr. Sergio Escalera Guerrero</span><br></b></span><span style="color:rgb(153,153,153)">Head of Human Pose Recovery and Behavior Analysis Lab<br></span></font></div><span style="color:rgb(153,153,153)"><font size="1">Project Manager at the Computer Vision Center<br></font></span></div><span style="color:rgb(153,153,153)"><font size="1">Director of ChaLearn Challenges in Machine Learning<br></font></span><span style="color:rgb(153,153,153)"><font size="1"><span><span><span style="color:rgb(153,153,153)"><font size="1">Associate professor</font></span></span></span> at University of Barcelona / Universitat Oberta de Catalunya / Aalborg Univ. / </font></span><br><span style="color:rgb(153,153,153)"><font size="1">Dalhousie University<br>Phone:+34</font></span><font size="1"><span style="color:rgb(153,153,153)"><font size="1"><span dir="ltr"><span dir="ltr"><span><span>934020853<br></span></span></span></span></font>Email: <a href="mailto:sergio.escalera.guerrero@gmail.com" target="_blank">sergio.escalera.guerrero@gmail.com</a> / Webpage: <a href="http://www.maia.ub.es/~sergio/" target="_blank">http://www.sergioescalera.com/</a></span></font><br><div><span></span></div></div></div></div></div></div></div></div></div></div></div></div></div>
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