The Contribution Of Stereoposis And Motion Parallax To Depth Segmentation

dc.contributor.advisorWilcox, Laurie
dc.contributor.authorNeagu, Teodora
dc.date.accessioned2025-04-10T10:40:06Z
dc.date.available2025-04-10T10:40:06Z
dc.date.copyright2024-08-14
dc.date.issued2025-04-10
dc.date.updated2025-04-10T10:40:06Z
dc.degree.disciplineBiology
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractStudies comparing how stereopsis and motion parallax inform depth yield mixed results, with some showing poorer performance from motion parallax. We propose that this stems from use of depth magnitude tasks, which bias observers towards using stereopsis. In Experiment 1, we used a depth segmentation task to evaluate performance of stereopsis and self-generated motion parallax individually and in combination. Observers performed equally well with individual cues but appeared to rely on stereopsis when both were available. Follow-up experiments showed that increasing the range of self-motion did not improve performance, but the use of object motion did. Subsequently, we replicated Experiment 1 using object motion with experts and non-experts and found segmentation from object motion was always better than stereopsis, and non-experts needed more stereopsis than experts to perform the task. Thus, we suggest that the ability to use depth information depends on several factors including task demands and observers’ experience.
dc.identifier.urihttps://hdl.handle.net/10315/42732
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subject.keywordsStereopsis
dc.subject.keywordsMotion parallax
dc.subject.keywordsVR
dc.subject.keywordsDepth perception
dc.titleThe Contribution Of Stereoposis And Motion Parallax To Depth Segmentation
dc.typeElectronic Thesis or Dissertation

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Teodora_Neagu_2024_MSc.pdf
Size:
802.06 KB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
license.txt
Size:
1.87 KB
Format:
Plain Text
Description:
No Thumbnail Available
Name:
YorkU_ETDlicense.txt
Size:
3.39 KB
Format:
Plain Text
Description:

Collections