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<br clear="none">Deep Learning in Computer Vision: Theories and Applications
<br clear="none">
<br clear="none">Aims and Scope:
<br clear="none">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:
<br clear="none">
<br clear="none">==Deep Learning Theories
<br clear="none">--Deep Learning Algorithms
<br clear="none">--Deep Learning Networks
<br clear="none">--Deep Feature Learning
<br clear="none">--Deep Metric Learning
<br clear="none">--Deep Learning Toolboxes
<br clear="none">--Performance Evaluation
<br clear="none">--Deep Learning Optimization
<br clear="none">
<br clear="none">==Deep Learning Applications
<br clear="none">--Deep Learning for Object Segmentation and Shape Models
<br clear="none">--Deep Learning for Object Detection and Recognition
<br clear="none">--Deep Learning for Image Understanding
<br clear="none">--Deep Learning for Human Actions Recognition
<br clear="none">--Deep Learning for Facial Recognition
<br clear="none">--Deep Learning for Visual Tracking
<br clear="none">--Deep Learning for Image and Video Retrieval
<br clear="none">--Deep Learning for Image Classification
<br clear="none">--Deep Learning for Scene Understanding
<br clear="none">--Deep Learning for Visual Saliency
<br clear="none">--Deep Learning for Visual Understanding
<br clear="none">--Deep Learning for Medical Image Recognition
<br clear="none">
<br clear="none">
<br clear="none">Publication Schedule:
<br clear="none">The tentative schedule of the book publication is as follows:
<br clear="none">-- Deadline for chapter submission: May 30, 2018
<br clear="none">-- Author notification: July 30, 2018
<br clear="none">-- Camera-ready submission: August 30, 2018
<br clear="none">-- notification: September 15, 2018
<br clear="none">-- Publication date: Fourth quarter of 2018
<br clear="none">
<br clear="none">
<br clear="none">Submission Procedure:
<br clear="none">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:
<br clear="none">-- The optimum length of the manuscript is 20-30 A4 pages.
<br clear="none">-- The publication of the selected chapters will be free of charge.
<br clear="none">-- Submitted manuscripts should conform to the author’s guidelines of the CRC Press mentioned in the following two points.
<br clear="none">-- Latex is the preferable word processing tool for preparing the chapters (See the book website for instructions).
<br clear="none">-- MS Word is an acceptable word processing tool for preparing the chapters (See the book website for instructions).
<br clear="none">
<br clear="none">Book Editors:
<br clear="none">Dr.: M. Hassaballah,
<br clear="none">Department of Computer Science,
<br clear="none">Faculty of Computers and Information
<br clear="none">South Valley University, Luxor, Egypt
<br clear="none">E-mail: m.hassaballah[at]svu.edu.eg
<br clear="none">
<br clear="none">
<br clear="none">Dr.: Ali Ismail Awad
<br clear="none">Department of Computer Science, Electrical and Space Engineering
<br clear="none">Luleå University of Technology
<br clear="none">Luleå, Sweden
<br clear="none">E-mail: ali.awad[at]ltu.se
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