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Title :
[DST Lecture Series] Computational Aesthetics - Automatic Generation of Abstract Art
Date :
May.02,2018 - May.02,2018
Time :
15:00 - 17:00
Venue :
Speaker :
Language :
Detail :
Contact
DST
About Prof. Zhang:
Kang Zhang is Professor and Director of Visual Computing Lab, Department of Computer Science, and Professor of Arts and Technology, at the University of Texas at Dallas. He received his B.Eng. in Computer Engineering from University of Electronic Science and Technology of China in 1982, Ph.D. from the University of Brighton, UK, in 1990, and Executive MBA from the University of Texas at Dallas in 2011. Prior to joining UT-Dallas, he held academic positions in the UK, Australia, and China. Dr. Zhang's current research interests include generative art, visual languages, aesthetic computing, managerial aesthetics, and software engineering; and has published 7 books, and over 250 papers in these areas. He has authored and edited six books. Dr Zhang is on the Editorial Boards of Journal of Big Data, The Visual Computer, Journal of Visual Languages and Computing, International Journal of Software Engineering and Knowledge Engineering, and International Journal of Advanced Intelligence.
Abstract of the talk:
As modern computer graphics and printing technology become increasingly advanced, automatic generation of sophisticated abstract paintings becomes possible. This talk will first introduce the general concepts of aesthetic computing and computational aesthetics, and a 4-level classification in terms of computational power utilized for computer generated art. We will present a recent project on automatic generation of the well-known styles of abstract paintings, such as Pollock, Kandinsky and Miro, using the programming language Processing in details. We start with analysis of the given styles based on specific art theories and our own understanding and observation of their abstract paintings. The generation process is demonstrated with sample generated images styled on some of the best-known paintings. Using random generation, every styled image generated can be unique. Finally, we discuss the potential application of our work in information visualization and mention other related projects in our group.
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