Literature DB >> 26968866

Testing models of peripheral encoding using metamerism in an oddity paradigm.

Thomas S A Wallis, Matthias Bethge, Felix A Wichmann.   

Abstract

Most of the visual field is peripheral, and the periphery encodes visual input with less fidelity compared to the fovea. What information is encoded, and what is lost in the visual periphery? A systematic way to answer this question is to determine how sensitive the visual system is to different kinds of lossy image changes compared to the unmodified natural scene. If modified images are indiscriminable from the original scene, then the information discarded by the modification is not important for perception under the experimental conditions used. We measured the detectability of modifications of natural image structure using a temporal three-alternative oddity task, in which observers compared modified images to original natural scenes. We consider two lossy image transformations, Gaussian blur and Portilla and Simoncelli texture synthesis. Although our paradigm demonstrates metamerism (physically different images that appear the same) under some conditions, in general we find that humans can be capable of impressive sensitivity to deviations from natural appearance. The representations we examine here do not preserve all the information necessary to match the appearance of natural scenes in the periphery.

Entities:  

Mesh:

Year:  2016        PMID: 26968866     DOI: 10.1167/16.2.4

Source DB:  PubMed          Journal:  J Vis        ISSN: 1534-7362            Impact factor:   2.240


  12 in total

1.  Image content is more important than Bouma's Law for scene metamers.

Authors:  Thomas Sa Wallis; Christina M Funke; Alexander S Ecker; Leon A Gatys; Felix A Wichmann; Matthias Bethge
Journal:  Elife       Date:  2019-04-30       Impact factor: 8.140

2.  Texture-like representation of objects in human visual cortex.

Authors:  Akshay V Jagadeesh; Justin L Gardner
Journal:  Proc Natl Acad Sci U S A       Date:  2022-04-19       Impact factor: 12.779

3.  Seeing number using texture: How summary statistics account for reductions in perceived numerosity in the visual periphery.

Authors:  Benjamin Balas
Journal:  Atten Percept Psychophys       Date:  2016-11       Impact factor: 2.199

4.  Can (should) theories of crowding be unified?

Authors:  Mehmet N Agaoglu; Susana T L Chung
Journal:  J Vis       Date:  2016-12-01       Impact factor: 2.240

5.  Diagnosing the Periphery: Using the Rey-Osterrieth Complex Figure Drawing Test to Characterize Peripheral Visual Function.

Authors:  Daniel R Coates; Johan Wagemans; Bilge Sayim
Journal:  Iperception       Date:  2017-05-29

6.  Visual crowding is a combination of an increase of positional uncertainty, source confusion, and featural averaging.

Authors:  William J Harrison; Peter J Bex
Journal:  Sci Rep       Date:  2017-04-05       Impact factor: 4.379

7.  Are we underestimating the richness of visual experience?

Authors:  Andrew M Haun; Giulio Tononi; Christof Koch; Naotsugu Tsuchiya
Journal:  Neurosci Conscious       Date:  2017-02-05

8.  Children's use of visual summary statistics for material categorization.

Authors:  Benjamin Balas
Journal:  J Vis       Date:  2017-10-01       Impact factor: 2.240

9.  Challenges to pooling models of crowding: Implications for visual mechanisms.

Authors:  Ruth Rosenholtz; Dian Yu; Shaiyan Keshvari
Journal:  J Vis       Date:  2019-07-01       Impact factor: 2.240

10.  Redundancy masking: The loss of repeated items in crowded peripheral vision.

Authors:  Fazilet Zeynep Yildirim; Daniel R Coates; Bilge Sayim
Journal:  J Vis       Date:  2020-04-09       Impact factor: 2.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.