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(Book) DLCV 2018 : Deep Learning in Computer Vision: Theories and Applications

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Link: http://staff.www.ltu.se/~ismawa/dlcv/
 
When N/A
Where N/A
Submission Deadline May 30, 2018
Notification Due Jul 30, 2018
Final Version Due Aug 30, 2018
Categories    deep learning   computer vision   image processing   visual tracking
 

Call For Papers

Call for Chapters (http://staff.www.ltu.se/~ismawa/dlcv/)

Deep Learning in Computer Vision: Theories and Applications

Aims and Scope:
Recent advances in learning algorithms for deep architectures have made deep learning feasible and deep learning systems have achieved state-of-the-art performance and sometimes show superior performance on fully supervised learning tasks on several fields. Specifically, deep learning algorithms have brought a revolution to the computer vision community, introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved. Today, utilizing deep learning-based methods in computer vision is a very hot topic. For some tasks such as object recognition and image classification, tremendous progress has been made in applying deep learning techniques. On the other hand, there are some debates as to the reasons for the high success of the deep learning-based methods, and about the limitations of these methods. Besides, several questions are still open and need answers as to how these methods can be tailored to certain computer vision tasks such as videos-related applications and how to scale up the models and training data. Topics of interest include, but are not limited to:

==Deep Learning Theories
--Deep Learning Algorithms
--Deep Learning Networks
--Deep Feature Learning
--Deep Metric Learning
--Deep Learning Toolboxes
--Performance Evaluation
--Deep Learning Optimization

==Deep Learning Applications
--Deep Learning for Object Segmentation and Shape Models
--Deep Learning for Object Detection and Recognition
--Deep Learning for Image Understanding
--Deep Learning for Human Actions Recognition
--Deep Learning for Facial Recognition
--Deep Learning for Visual Tracking
--Deep Learning for Image and Video Retrieval
--Deep Learning for Image Classification
--Deep Learning for Scene Understanding
--Deep Learning for Visual Saliency
--Deep Learning for Visual Understanding
--Deep Learning for Medical Image Recognition


Publication Schedule:
The tentative schedule of the book publication is as follows:
-- Deadline for chapter submission: May 30, 2018
-- Author notification: July 30, 2018
-- Camera-ready submission: August 30, 2018
-- notification: September 15, 2018
-- Publication date: Fourth quarter of 2018


Submission Procedure:
Authors are invited to submit original, high quality, unpublished results of both deep learning theories and applications in the computer vision domain. Prospective authors need to electronically submit their contributions using EasyChair submission system (Link). Submitted manuscripts will be refereed by at least two independent and expert reviewers for quality, correctness, originality, and relevance. The accepted contributions will be published as a book in the prestigious Digital Imaging and Computer Vision Book Series by CRC Press. More information about the "Digital Imaging and Computer Vision Book Series" can be found in the (See the book website for instructions). Please consider the following points when preparing your manuscript:
-- The optimum length of the manuscript is 20-30 A4 pages.
-- The publication of the selected chapters will be free of charge.
-- Submitted manuscripts should conform to the author’s guidelines of the CRC Press mentioned in the following two points.
-- Latex is the preferable word processing tool for preparing the chapters (See the book website for instructions).
-- MS Word is an acceptable word processing tool for preparing the chapters (See the book website for instructions).

Book Editors:
Dr.: M. Hassaballah,
Department of Computer Science,
Faculty of Computers and Information
South Valley University, Luxor, Egypt
E-mail: m.hassaballah[at]svu.edu.eg


Dr.: Ali Ismail Awad
Department of Computer Science, Electrical and Space Engineering
Luleå University of Technology
Luleå, Sweden
E-mail: ali.awad[at]ltu.se

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