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Interacting with Autostereograms See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/336204498 Interacting with Autostereograms Conference Paper · October 2019 DOI: 10.1145/3338286.3340141 CITATIONS READS 0 39 5 authors, including: William Delamare Pourang Irani Kochi University of Technology University of Manitoba 14 PUBLICATIONS 55 CITATIONS 184 PUBLICATIONS 2,641 CITATIONS SEE PROFILE SEE PROFILE Xiangshi Ren Kochi University of Technology 182 PUBLICATIONS 1,280 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: Color Perception in Augmented Reality HMDs View project Collaboration Meets Interactive Spaces: A Springer Book View project All content following this page was uploaded by William Delamare on 21 October 2019. The user has requested enhancement of the downloaded file. Interacting with Autostereograms William Delamare∗ Junhyeok Kim Kochi University of Technology University of Waterloo Kochi, Japan Ontario, Canada University of Manitoba University of Manitoba Winnipeg, Canada Winnipeg, Canada [email protected] [email protected] Daichi Harada Pourang Irani Xiangshi Ren Kochi University of Technology University of Manitoba Kochi University of Technology Kochi, Japan Winnipeg, Canada Kochi, Japan [email protected] [email protected] [email protected] Figure 1: Illustrative examples using autostereograms. a) Password input. b) Wearable e-mail notification. c) Private space in collaborative conditions. d) 3D video game. e) Bar gamified special menu. Black elements represent the hidden 3D scene content. ABSTRACT practice. This learning effect transfers across display devices Autostereograms are 2D images that can reveal 3D content (smartphone to desktop screen). when viewed with a specific eye convergence, without using CCS CONCEPTS extra-apparatus. We contribute to autostereogram studies from an HCI perspective. We explore touch inputs and output •Human-centeredcomputing→Graphicsinputdevices; design options when interacting with autostereograms on Interaction techniques; Graphical user interfaces; smartphones. We found that an interactive help (i.e. to control KEYWORDS the autostereogram stereo-separation), a color-based feed- back (i.e. highlight of the screen), and a direct touch input can Autostereograms; Interactive; Hidden 3D; Magic Eye; SIRDS; provide support for faster and more accurate interaction than Help;Stereopsis;Input;Output;Mobile;Direct;Indirect;Touch a static help (i.e. static dots indicating the stereo-separation), ACM Reference Format: an animated feedback (i.e., a ‘pressed’ effect), and an indirect William Delamare, Junhyeok Kim, Daichi Harada, Pourang Irani, input. In addition, results reveal that participants learn to per- and Xiangshi Ren. 2019. Interacting with Autostereograms. In 21st ceive smaller and smaller autostereogram content faster with International Conference on Human-Computer Interaction with Mo- bile Devices and Services (MobileHCI ’19), October 1–4, 2019, Taipei, ∗ JSPS International Research Fellows Taiwan. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/ Permission to make digital or hard copies of all or part of this work for 3338286.3340141 personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear 1 INTRODUCTION this notice and the full citation on the first page. Copyrights for components Stereopsis - the ability to perceive depth and 3D stereoscopic of this work owned by others than ACM must be honored. Abstracting with images-comesfromthehorizontaldisparityofthetwoimages credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request captured by our eyes [31]. This disparity is then processed by permissions from [email protected]. the brain to create a depth perception. Since the development MobileHCI ’19, October 1–4, 2019, Taipei, Taiwan of random-dot stereograms (RDS) by Julesz in 1960 [22], stere- © 2019 Association for Computing Machinery. ograms have been used as a stimulus in several stereopsis ACM ISBN 978-1-4503-6825-4/19/10...$15.00 experiments to understand human [10, 28, 31] and primate https://doi.org/10.1145/3338286.3340141 [13, 28] stereo-vision and neurophysiological mechanisms of depth perception. Autostereograms - or Single Image Random part of the brain, not by the eyes [8, 10, 21, 31]. Researchers Dot Stereograms (SIRDS) - are 2D ‘flat’ images that can reveal have then used RDSs in numerous studies to better understand 3D content when perceived with a specific eye convergence various aspects of stereopsis [14, 17, 19, 23, 30]. [5, 19, 29]. In contrast to standard stereograms, autostere- Perhaps more relevant to our HCI approach are results ograms do not require any extra-apparatus such as polarized reporting a relatively large variability in depth perception or shutter glasses. with RDSs [12, 19, 21, 27–29]. It appears that around 68% of Autostereograms, also called "Hidden 3D" or "Magic Eye", the population have good to excellent stereo ability, while have also been used as an entertainment medium, such as 32% have poor to moderate stereo ability [21], and 5% of the in image-book collections [12, 32, 46]. Such entertaining and population is said to be blind to the stereo effect [12]. This can engaging aspect is partially due to the combination of the result in a long initial latency to perceive depth [9]. minimum oculomotor efforts required from the observer, and Fortunately, the time to perceive depth and figure in RDSs the surprising resulting 3D effect, making autostereograms decreases with repetitions [18, 28]. This learning effect ap- an attractive gamified solution to view 3D scenes [42, 43]. pears to be position specific on the retinae, based on selective However, autostereograms have received little attention spatial attention, and stimulus specific to name a few, but not from a human-computer interaction perspective. This work due to learning depth contours or a priori information about fills this gap by introducing the concept of Interactive Au- the hidden 3D [28]. Such perceptual learning can last weeks tostereograms. Indeed, both core concepts defining autostere- or month depending on the experimental procedure reported ograms, i.e. "hidden" and "3D", can be useful in various scenar- in previous work [16, 18, 27, 28]. In addition, learning also ios. For instance, autostereograms can solve the problem of concern motion detection in RDSs [18, 34]. privacy when inputting sensitive information by preventing Single-image-random-dot-stereograms (SIRDS) are a spe- shoulder-surfing attacks [49]. Yet, no previous attempt was cific type of RDS that display random dot patterns sothat made to interact with autostereograms. depth can be perceived without additional apparatus [5, 19, We report on an empirical study investigating how users 42]. This requires users to explicitly look at the image so that can interact with autostereograms. Results show that an inter- the two eyes capture the dot patterns with the correct dispar- active help leads novices to perceive autostereogram content ity to allow the brain to fuse the two images. In other words, faster than with other help methods. A color-coded feedback users have to decouple eye convergence (crossing-point of the provides more accuracy than other feedbacks. A direct input line-of-sight) from focus (depth of lens adjustment). This ocu- leads to faster and more accurate interaction than an indirect lomotor skill is known to be difficult at first [5, 19, 32, 42–47]. input. Participants can learn to see smaller and smaller au- Thus, perceiving depth in SIRDSs can take several minutes tostereogram content faster with practice. Lastly, this learning and several attempts [19]. Possible explanations include the ig- effect transfers across display apparatus. norance of the proper viewing strategy [19], or the automatic By introducing the concept of Interactive Autostereograms, rejection of the unusual depth impression [47]. However, once our contributions are twofolds: (1) the design of an interactive the correct decoupling is acquired, the percept is highly com- help method to perceive the hidden 3D content, and (2) an in- pelling [5], with convincing images with vivid depth [43], and vestigation into the impact of different touch input and output can also be modified without any further eye adjustments design options when interacting with autostereograms. [5, 32]. Like other RDSs, SIRDS benefit from a perceptual learning, but also from an additional kinesthetic learning of 2 RELATED WORK the convergence/focus decoupling oculomotor skill, which We briefly present results from studying the stereopsis pro- is also stable over time [5]. cess in the cognitive literature, and previous works linking computer sciences and autostereograms. Autostereograms in Computer Science Previous work introduced [12, 42], and classified autostere- Stereopsis ograms [44, 45, 47], encouraging the community to find new Front-facing eyes allow for slightly different captured images, scientific applications [12, 42, 47]. Classifications allow us to while still allowing for an overlap between the two images. differentiate autostereograms according to their rendering This horizontal disparity allows to derive depth information texture properties (ASCII or random dots, colored or not, etc). [21]. Random-dot stereograms
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