[visionlist] IEEE TPAMI Inpainting and Denoising Special Issue 15th Feb.

[Sergio Escalera]

*Call for papers IEEE Transactions on Pattern Analysis and Machine
Intelligence*

*special issue on Image and Video Inpainting and Denoising*



Image and video Inpainting and Denoising is a hot and challenging research
topic that has received much attention from the computer vision and pattern
recognition communities in the past. The inpainting problem of dealing with
missing data or incomplete data in machine learning and computer vision
arises in many applications. Owing to the development of deep learning and
big data, recent advances have gained. However, it is still challenging to
aim at fast, accurate, and robust removal of occlusions (text, objects or
stain) in images and video sequences. This special issue focuses on image
and video inpainting and denoising tasks, that might benefit from novel
methods such as Generative Adversarial Networks (GANs) or Residual
connections.

We invite paper submissions for the special issue on Image and Video
Inpainting and Denoising in Deep Learning Age to be published in IEEE
Transactions on Pattern Analysis and Machine Intelligence (TPAMI). We
welcome original research papers making theoretical and practical
substantial contributions on *inpainting and denoising* in connection to
other computer vision topics, including, but not limited to:

●          Generative Adversarial Networks for image/video inpainting

●          Pose estimation recovery

●          Video de-captioning

●          Inpainting/denoising for latent fingerprint recognition

●          Unsupervised learning in image/video inpainting

●          Future frame video prediction

●          Structural image/video inpainting

●          Textural image/video inpainting

●          Combined structural and textural inpainting

●          Multimodal image/video inpaint (i.e., RGB-D)



*Paper submission and review:*

Authors are required to submit contributions online through the TPAMI site
at,

https://mc.manuscriptcentral.com/tpami-cs

selecting the choice that indicates this special issue. Peer reviewing will
follow the standard rigorous TPAMI review process. Full length manuscripts
are expected to follow the TPAMI guidelines in

http://www.computer.org/portal/web/peerreviewjournals/author



*Important Dates:*

*Paper submission extended deadline: February 15th, 2018*

First review decision: March, 15th, 2019

Revision deadline: May, 15th, 2019

Final manuscript submission: July, 1st, 2019



*Guest editors:*

Sergio Escalera (Primary contact), UAB and University of Barcelona,
sergio at maia.ub.es

Hugo Jair Escalante, INAOE, Mexico and ChaLearn, Berkeley, California,
hugo.jair at gmail.com

Isabelle Guyon, ChaLearn, Berkeley, California, guyon at chalearn.org

Jun Wan, NLPR, Institute of Automation, Chinese Academy of Science, China,
jun.wan at ia.ac.cn

Stephane Ayache, Aix Marseille Univ, Université de Toulon, CNRS, LIS,
Marseille, France

Umut Güçlü, Donders Institute for Brain, Cognition and Behaviour, Nijmegen,
Netherlands, u.guclu at donders.ru.nl

Yağmur Güçlütürk, Donders Institute for Brain, Cognition and Behaviour,
Nijmegen, Netherlands, y.gucluturk at donders.ru.nl

Meysam Madadi, Computer Vision Center and Universitat Autonoma de
Barcelona, mmadadi at cvcv.uab.es

Xavier Baró, Universitat Oberta de Catalunya & Computer Vision Center,
xbaro at uoc.edu




-- 

*Dr. Sergio Escalera Guerrero*Head of Human Pose Recovery and Behavior
Analysis group / Project Manager at the Computer Vision Center
Vice-president of ChaLearn Challenges in Machine Learning, Berkeley
Associate professor at Universitat de Barcelona / Universitat Oberta de
Catalunya / Aalborg University /
Dalhousie University
Email: sergio.escalera.guerrero at gmail.com / Webpage:
http://www.sergioescalera.com/ <http://www.maia.ub.es/~sergio/>  / Phone:+34
934020853
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://visionscience.com/pipermail/visionlist_visionscience.com/attachments/20190108/40d174eb/attachment-0001.html>