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Browsing by Author "Liu, Siyun"

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    Data for Validating a Model of Architectural Hazard Visibility with Low-Vision Observers
    (2020-07-22) Liu, Siyun; Thompson, William B.; Liu, Yichen; Shakespeare, Robert A.; Kersten, Daniel J.; Legge, Gordon E.; liux4433@umn.edu; Liu, Siyun; Department of Psychology, University of Minnesota; School of Computing, University of Utah; Department of Theatre, Drama, and Contemporary Dance, Indiana University Bloomington
    Pedestrians with low vision are at risk of injury when hazards, such as steps and posts, have low visibility. This study aims at validating the software implementation of a computational model that estimates hazard visibility. The model takes as input a photorealistic 3-D rendering of an architectural space, and the acuity and contrast sensitivity of a low-vision observer, and outputs estimates of the visibility of hazards in the space. Our experiments explored whether the model can predict the likelihood of observers correctly identifying hazards. We tested fourteen normally sighted subjects with blur goggles that reduced acuity to 1.2 logMAR or 1.6 logMAR and ten low-vision subjects with acuities ranging from 0.8 logMAR to 1.6 logMAR. Subjects viewed computer-generated images of a walkway containing five possible targets ahead—large step up, large step-down, small step up, small step down, or a flat continuation. Each subject saw these stimuli with variations of lighting and viewpoint in 250 trials and indicated which of the five targets was present. The model generated a score on each trial that estimated the visibility of the target. If the model is valid, the scores should be predictive of how accurately the subjects identified the targets. We used logistic regression to examine the correlation between the scores and the participants’ responses. For twelve of the fourteen normally sighted subjects with artificial acuity reduction and all ten low-vision subjects, there was a significant relationship between the scores and the participant’s probability of correct identification. These experiments provide evidence for the validity of a computational model that predicts the visibility of architectural hazards. The software implementation of the model may be useful for architects to assess the visibility of hazards in their designs, thereby enhancing the accessibility of spaces for people with low vision.
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    Does Motion Parallax Enhance the Accuracy of Object Depth Perception?
    (2022-09) Liu, Siyun
    Depth perception is essential for safe and effective mobility. When an observer moves through an environment, the retinal images of surrounding objects shift in the field of view, creating motion parallax, which can be used to infer the depths of the objects. When an observer walks forward to approach an object, the image of the object expands in the field of view, inducing expansive motion parallax; when the motion direction of the observer contains a lateral component, the object image shifts laterally to the left or right, which is termed lateral motion parallax. This thesis investigates three questions regarding the effect of motion parallax on depth perception for people with intact and artificially reduced acuity: whether motion parallax increases depth perception accuracy compared to static viewing with pictorial depth cues present; whether expansive and lateral motion parallax differs from each other in assisting depth perception; and whether continuous motion provides more benefit to accurate depth perception than object image displacement. To control the level of acuity loss, the participants included in this thesis were normally sighted, and the acuity reduction was simulated with digital filters or blur goggles. Chapter 1 provides an overview of the three experiments described in the thesis. In all three experiments, the participants looked at two objects in a virtual or physical scene and estimated the depth separation between the objects either by moving a slider on the screen or by verbal report. Chapters 2 and 3 focus on the effect of visual signals from motion parallax. The results show that when estimating object depth in a virtual scene on a computer screen, for participants with both intact acuity and artificially reduced acuity, the accuracy in the static viewing condition was low, lateral motion parallax yielded higher accuracy than expansive motion parallax, and the continuous motion was more beneficial than discrete object image displacement. Chapter 4 examines the effect of motion parallax for observers who walked in a physical space. The accuracy of depth estimation for static viewing in the physical space was higher than that in the virtual space, and the effect of observer motion was weaker. Lateral motion parallax only increased the accuracy of depth estimation when the acuity reduction was severe and the pictorial cues in the scene were manipulated to be misleading. While motion parallax is an important source of depth information in a scene presented on a screen, in the physical world, pictorial cues may often be sufficient for estimating the depth of objects, reducing the importance of motion parallax. Further investigations are needed to evaluate the importance of motion parallax for people with impaired vision.