“The Light”: An Experimental Moving Image which uses Color, Trace, and Interactivity of Light to Measure Quantitative Presence


KIPS Transactions on Software and Data Engineering, Vol. 6, No. 12, pp. 587-592, Dec. 2017
10.3745/KTSDE.2017.6.12.587,   PDF Download:
Keywords: Experimental Moving Image, Presence, PQS, EEG, Interactivity
Abstract

In this paper, we raise a question: what makes presence in artistic works. Presence has been defined as a physiological and emotional feeling which feels that he or she is immerged in a specific artworks when he/she is very interested in the artwork. We design and implemented an abstract media art “The Light” which uses the color, trace, and interactivity of light to measure quantitative presence. Frequency spectrum of light on specific object which we perceive makes color; Motion of light and its impact on object makes shape which perceived in our human visual system; Interactivity or perceived distance between object and observer makes intensity of perception. We experimented our images with subjective survey which includes PQS (presence questionnaire survey) and objective test using brain signals (EEG). From our interactive experimental moving images tested on 30 subject, we conclude that we can make more presence as we interact more with images. Photo-realistic images is just pass-by and it is transformed to abstract images, as we more focused on the images, and the essential components of the abstraction includes color, trace, and interaction with subjects.


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Cite this article
[IEEE Style]
S. Jeon and S. W. Kim, "“The Light”: An Experimental Moving Image which uses Color, Trace, and Interactivity of Light to Measure Quantitative Presence," KIPS Transactions on Software and Data Engineering, vol. 6, no. 12, pp. 587-592, 2017. DOI: 10.3745/KTSDE.2017.6.12.587.

[ACM Style]
Seongsin Jeon and Seong Whan Kim. 2017. “The Light”: An Experimental Moving Image which uses Color, Trace, and Interactivity of Light to Measure Quantitative Presence. KIPS Transactions on Software and Data Engineering, 6, 12, (2017), 587-592. DOI: 10.3745/KTSDE.2017.6.12.587.