+ Site Statistics
References:
54,258,434
Abstracts:
29,560,870
PMIDs:
28,072,757
+ Search Articles
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ PDF Full Text
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Translate
+ Recently Requested

Real-world scene perception and perceptual organization Lessons from Computer Vision



Real-world scene perception and perceptual organization Lessons from Computer Vision







Extensive research into the architecture of human scene perception and human figure-ground segmentation show that both local and configural processes play a role. Local factors include bottom-up edge segmentation enabling small regions to be fused into figural regions. Configural factors include top-down processes such as grouping and meaningfulness. Barghout (2009, 2011) suggested a natural-scene-perception architecture comprised of nested hierarchies of "spatial taxons" with the rank-frequency distribution predicted by a law of least effort, where attentional resources were minimized and utility optimized. Because computer vision models often provide insight into human perception, we decided to build a computer vision segmentation model that used spatial-taxon designation as a "meaningfulness" configural cue. The computer model used fuzzy-logic inference to simulate low-level visual processes and few rules of figure-ground perceptual organization. The model was required to conform to a spatial-taxona s "meaningfulness" cue. We collected 70 real images composed of three "generic scene types", each of which required a different combination of the perceptual organization rules built into our model. We then used our model to segment the generic scene types. Two human subjects rated image-segmentation quality on a scale from 1 to 5 (5 being the best). The majority of generic-scene-type image segmentations received a score of 4 or 5 (very good, perfect). ROC plots show that this model performs better on generic-scene-type images than normalized-cut ((Martin, Fowlkes, Tal, and Malik (2001).

(PDF emailed within 1 workday: $29.90)

Accession: 037285879

Download citation: RISBibTeXText


Related references

Human gaze control during real-world scene perception. Trends in Cognitive Sciences 7(11): 498-504, 2003

Scene perception in early vision: Figure-ground organization in the lateral geniculate nucleus. Proceedings of the National Academy of Sciences of the United States of America 112(22): 6784-6785, 2015

Eye guidance during real-world scene search: The role color plays in central and peripheral vision. Journal of Vision 16(2): 3-3, 2016

From image pair to a computer generated hologram for a real-world scene. Applied Optics 55(27): 7583-7592, 2016

Robust tracking of persons in real-world scenarios using a statistical computer vision approach. Image and Vision Computing 22(7): 571-582, 2004

Perceptual organization of color and non-color nighttime real-world imagery. Investigative Ophthalmology & Visual Science 38(4 PART 1-2): S641, 1997

Perceptual Annotation: Measuring Human Vision to Improve Computer Vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(8): 1679-1686, 2016

Scene perception, gaze behavior, and perceptual learning in virtual environments. Cyberpsychology & Behavior 8(6): 592-600, 2005

Subjective perceptual organization of a complex auditory scene. Journal of the Acoustical Society of America 141(1): 265-265, 2017

Auditory scene analysis the perceptual organization of sound. Bregman, A S Auditory Scene Analysis: The Perceptual Organization Of Sound Xv+773p Mit Press: Cambridge, Massachusetts, Usa; London, England, Uk Illus Xv+773p, 1990

Scene perception from central to peripheral vision. Journal of Vision 17(1): 6-6, 2017

Virtual plagues and real-world pandemics: reflecting on the potential for online computer role-playing games to inform real world epidemic research. Medical Humanities 39(2): 115-118, 2014

Relationship between scene complexity and perceptual performance for computer graphics simulations. Displays 11(4): 179-185, 1990

Benchmarking neuromorphic vision: lessons learnt from computer vision. Frontiers in Neuroscience 9: 374-374, 2015

A real-time music-scene-description system: predominant-F0 estimation for detecting melody and bass lines in real-world audio signals. Speech Communication 43(4): 311-329, 2004