Open access peer-reviewed Edited volume

Graph Theory for Computer Vision and Artificial Intelligence Applications

Edited by Beril Sirmacek

University of Twente

Covering

Object Recognition Graph Matching Graph-Cut Algorithms Deep Learning 3D Graphs 3D Surface Neural Networks Classification Shape Analysis

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About the book

History of graph theory goes to 1700’s. Nevertheless, scientists and software developers use graph theory more and more in order to solve complex mathematical problems. Computer vision and especially 3D reconstruction applications highly benefit from graph theory for making the recognition and classification algorithms more robust using graph configurations such as weighted and deformable graphs. Graph theory provides opportunities to represent data in vertices and edge matrices which makes the optimization implementations possible. Therefore, graph theory can be used in artificial intelligence algorithms for finding the optimal solutions easily, especially when big data is used. In this book, the focus should be on computer vision and artificial intelligence applications of big data by addressing the state-of-the-art implementations and applications in this field. 

Publishing process

Book initiated and editor appointed

Date completed: July 24th 2018

Applications to edit the book are assessed and a suitable editor is selected, at which point the process begins.

Chapter proposals submitted and reviewed

Deadline Extended: Open for Submissions

Potential authors submit chapter proposals ready for review by the academic editor and our publishing review team.

Approved chapters written in full and submitted

Deadline for full chapters: October 13th 2018

Once approved by the academic editor and publishing review team, chapters are written and submitted according to pre-agreed parameters

Full chapters peer reviewed

Review results due: January 1st 2019

Full chapter manuscripts are screened for plagiarism and undergo a Main Editor Peer Review. Results are sent to authors within 30 days of submission, with suggestions for rounds of revisions.

Book compiled, published and promoted

Expected publication date: March 2nd 2019

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About the editor

Beril Sirmacek

University of Twente

Beril holds a PhD degree in computer vision and remote sensing field. In the past, she has worked on remote sensing data processing at Yeditepe University, German Aerospace Center, University of Osnabrueck, University of Augsburg, Delft University of Technology and she has visited several other institutions among which are University of Trento, University Konstanz, University Erlangen-Nuernberg and INRIA. With her remote sensing and augmented reality application, in 2016 she was selected as Copernicus Masters DLR healthy, energy and environment challenge finalist and in 2017 she won the Copernicus Masters B2B challenge. After building up the farmAR framework, she has joined University of Twente where she currently works on deep learning, simultaneous localization and mapping (SLAM), and augmented reality (AR). She is highly enthusiastic about creating useful software services for everyday life, in remote sensing, big data, deep learning, AI and AR fields.

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