Exploring the Cognitive Correlates of Artistic Practice Using a Parameterized Non-Photorealistic Toolkit

Abstract

The Rembrandt Textural Agency theory (Paper 2) was my original idea and research; I took this hypothesis, with questions on how to test it empirically, to my committee member James Enns and on his advice, we began working together to design and run the four studies done in his UBC Vision Lab. I was a member of his lab. Much of the study design expertise, study techniques and results discussion in our papers on these studies (Paper 4 and 6) came from his significant experience. These four studies with their results and discussion are combined and the major part of the Cognitive Science Studies chapter (Chapter 7). James Enns and I also supervised a UBC undergraduate student Caitlin Riebe (who won a Department of Psychology Quinn Summer research award to work on the project). She was subsequently included as a co-author on Papers 4 and 6 to acknowledge her contribution in data collection and discussion of the ideas. For these four studies, the creation of the source iv Rembrandt sitter photos, and the many final manipulated Rembrandt facsimile output from them using my NPR toolkit was solely my responsibility based on advice from Enns. Approval for the experiments described in this dissertation (Paper 4 and 6) was obtained from the UBC Office of Research Services under certificate numbers B98-0398, H10-01707 and H11-02768. (UBC Vision Lab-James Enns) The painterly NPR system detailed in this thesis (Paper 1 and 3) was originally designed and coded solely by me. Over the years, I have paid a part time undergraduate Java software programmer to help implement parts of the system based on my designs and management. The undergraduate student was first Thomas Johnson, then Dustin Dunsmuir and then Brendan Vance, all from Simon Fraser University's School of Interactive Arts and Technology (SFU SIAT). The latter, Brendan Vance, was SFU SIAT's valedictorian, a very bright individual who made strong suggestions about implementation details based on my designs and management.

Saliency-Based Artistic Abstraction With Deep Learning and Regression Trees

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Using an AI creativity system to explore how aesthetic experiences are processed along the brain’s perceptual neural pathways

“…Our lab has combined Genetic Algorithms, Neural Networks and Deep Learning neural networks into our Painterly Rendering software framework with the particular aim of emulating the cognitive processes of portrait artists (DiPaola et al, 2018(DiPaola et al, , 2017(DiPaola et al, , 2016(DiPaola et al, , 2014(DiPaola et al, , 2009. We will now review some of the work we have done in this field in order to demonstrate the use of biological inspired architectures in this space and to give the reader an idea of what kind of systems we are utilizing to create artworks that can be used for the study of human art perception.…”