Optical Illusion Images Dataset

Authors

  • Robert Max Williams University of Louisville, Louisville, Kentucky, United States Author
  • Roman V. Yampolskiy University of Louisville, Louisville, Kentucky, United States Author https://orcid.org/0000-0001-9637-1161

DOI:

https://doi.org/10.51191/issn.2637-1898.2019.2.2.127

Keywords:

Computer Vision, Optical Illusions, Human Vision, machine learning, Neural Networks, Cognition

Abstract

Human vision is capable of performing many tasks not optimized for during its long evolution. Reading text and identifying artificial objects such as road signs are both tasks that mammalian brains never encountered in the wild but are very easy for us to perform. However, humans have discovered many very specific tricks or illusions that cause us to misjudge the color, size, alignment, and movement of what we are looking at. A better understanding of these phenomenon could reveal insights into how human perception achieves these extraordinary feats. In this paper we present a dataset of 6,725 illusion images gathered from two websites, and a smaller dataset of 500 hand-picked images. We will discuss the process of collecting this data, models trained on the data, and the work that needs to be done to make this information of value to computer vision researchers.

Author Biographies

  • Robert Max Williams, University of Louisville, Louisville, Kentucky, United States

    Robert Max Williams is a computer science student and machine learning researcher. Currently he is completing his computer science undergraduate at the University of Louisville. His fields of interest include software design, esoteric programming languages and neural networks. He also enjoys traditional dance and creating electronic music.

  • Roman V. Yampolskiy, University of Louisville, Louisville, Kentucky, United States

    Roman V. Yampolskiy holds a PhD degree from the Department of Computer Science and Engineering at the University at Buffalo. There he was a recipient of a four year NSF (National Science Foundation) IGERT (Integrative Graduate Education and Research Traineeship) fellowship. Before beginning his doctoral studies Dr. Yampolskiy received a BS/MS (High Honors) combined degree in Computer Science from Rochester Institute of Technology, NY, USA. After completing his PhD dissertation Dr. Yampolskiy held a position of an Affiliate Academic at the Center for Advanced Spatial Analysis, University of London, College of London. In 2008 Dr. Yampolskiy accepted an assistant professor position at the Speed School of Engineering, University of Louisville, KY. He had previously conducted research at the Laboratory for Applied Computing (currently known as Center for Advancing the Study of Infrastructure) at the Rochester Institute of Technology and at the Center for Unified Biometrics and Sensors at the University at Buffalo.

    Dr. Yampolskiy is also an alumnus of Singularity University (GSP2012) and a visiting fellow of the Singularity Institute. Dr. Yampolskiy’s main areas of interest are behavioral biometrics, digital forensics, pattern recognition, genetic algorithms, neural networks, artificial intelligence and games. Dr. Yampolskiy is an author of over 100 publications including multiple journal articles and books. His research has been cited by numerous scientists and profiled in popular magazines both American and foreign (New Scientist, Poker Magazine, Science World Magazine), dozens of websites (BBC, MSNBC, Yahoo! News) and on radio (German National Radio, Alex Jones Show). Reports about his work have attracted international attention and have been translated into many languages including Czech, Danish, Dutch, French, German, Hungarian, Italian, Polish, Romanian, and Spanish.

Cover of the INSAM Journal No. 2. Design by Milan Šuput

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Published

15.07.2019

Issue

No. 2 (2019): Artificial Intelligence in Music, Arts and Theory

Section

Articles

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Copyright (c) 2019 INSAM Journal of Contemporary Music, Art and Technology

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How to Cite

Optical Illusion Images Dataset. (2019). INSAM Journal of Contemporary Music, Art and Technology, 2, 127–139. https://doi.org/10.51191/issn.2637-1898.2019.2.2.127
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