Literature DB >> 22387319

Combination of texture and color cues in visual segmentation.

Toni P Saarela1, Michael S Landy.   

Abstract

The visual system can use various cues to segment the visual scene into figure and background. We studied how human observers combine two of these cues, texture and color, in visual segmentation. In our task, the observers identified the orientation of an edge that was defined by a texture difference, a color difference, or both (cue combination). In a fourth condition, both texture and color information were available, but the texture and color edges were not spatially aligned (cue conflict). Performance markedly improved when the edges were defined by two cues, compared to the single-cue conditions. Observers only benefited from the two cues, however, when they were spatially aligned. A simple signal-detection model that incorporates interactions between texture and color processing accounts for the performance in all conditions. In a second experiment, we studied whether the observers are able to ignore a task-irrelevant cue in the segmentation task or whether it interferes with performance. Observers identified the orientation of an edge defined by one cue and were instructed to ignore the other cue. Three types of trial were intermixed: neutral trials, in which the second cue was absent; congruent trials, in which the second cue signaled the same edge as the target cue; and conflict trials, in which the second cue signaled an edge orthogonal to the target cue. Performance improved when the second cue was congruent with the target cue. Performance was impaired when the second cue was in conflict with the target cue, indicating that observers could not discount the second cue. We conclude that texture and color are not processed independently in visual segmentation.
Copyright © 2012 Elsevier Ltd. All rights reserved.

Entities:  

Mesh:

Year:  2012        PMID: 22387319      PMCID: PMC3448013          DOI: 10.1016/j.visres.2012.01.019

Source DB:  PubMed          Journal:  Vision Res        ISSN: 0042-6989            Impact factor:   1.886


  41 in total

1.  The integration of colour and motion by the human visual brain.

Authors:  Matthew W Self; S Zeki
Journal:  Cereb Cortex       Date:  2004-12-22       Impact factor: 5.357

2.  Slant from texture and disparity cues: optimal cue combination.

Authors:  James M Hillis; Simon J Watt; Michael S Landy; Martin S Banks
Journal:  J Vis       Date:  2004-12-01       Impact factor: 2.240

3.  Higher level chromatic mechanisms for image segmentation.

Authors:  Thorsten Hansen; Karl R Gegenfurtner
Journal:  J Vis       Date:  2006-03-13       Impact factor: 2.240

4.  Synergy of features enables detection of texture defined figures.

Authors:  Malte Persike; Günter Meinhardt
Journal:  Spat Vis       Date:  2006

5.  Detection of multidimensional targets in visual search.

Authors:  Patrick Monnier
Journal:  Vision Res       Date:  2006-09-27       Impact factor: 1.886

6.  Texture segregation and visual search: a comparison of the effects of random variations along irrelevant dimensions.

Authors:  R J Snowden
Journal:  J Exp Psychol Hum Percept Perform       Date:  1998-10       Impact factor: 3.332

7.  Cue-invariant activation in object-related areas of the human occipital lobe.

Authors:  K Grill-Spector; T Kushnir; S Edelman; Y Itzchak; R Malach
Journal:  Neuron       Date:  1998-07       Impact factor: 17.173

8.  Localizing contours defined by more than one attribute.

Authors:  J Rivest; P Cavanagh
Journal:  Vision Res       Date:  1996-01       Impact factor: 1.886

9.  Interactions between colour and motion in image segmentation.

Authors:  P Moller; A Hurlbert
Journal:  Curr Biol       Date:  1997-02-01       Impact factor: 10.834

10.  Postreceptoral chromatic detection mechanisms revealed by noise masking in three-dimensional cone contrast space.

Authors:  M J Sankeralli; K T Mullen
Journal:  J Opt Soc Am A Opt Image Sci Vis       Date:  1997-10       Impact factor: 2.129

View more
  12 in total

1.  A perceptual space of local image statistics.

Authors:  Jonathan D Victor; Daniel J Thengone; Syed M Rizvi; Mary M Conte
Journal:  Vision Res       Date:  2015-09-16       Impact factor: 1.886

2.  Perception of second- and third-order orientation signals and their interactions.

Authors:  Jonathan D Victor; Daniel J Thengone; Mary M Conte
Journal:  J Vis       Date:  2013-03-26       Impact factor: 2.240

3.  Image segmentation driven by elements of form.

Authors:  Jonathan D Victor; Syed M Rizvi; Mary M Conte
Journal:  Vision Res       Date:  2019-04-01       Impact factor: 1.886

Review 4.  Surround suppression supports second-order feature encoding by macaque V1 and V2 neurons.

Authors:  Luke E Hallum; J Anthony Movshon
Journal:  Vision Res       Date:  2014-10-23       Impact factor: 1.886

5.  Integration trumps selection in object recognition.

Authors:  Toni P Saarela; Michael S Landy
Journal:  Curr Biol       Date:  2015-03-19       Impact factor: 10.834

6.  Flexibly regularized mixture models and application to image segmentation.

Authors:  Jonathan Vacher; Claire Launay; Ruben Coen-Cagli
Journal:  Neural Netw       Date:  2022-02-15

7.  Luminance texture boundaries and luminance step boundaries are segmented using different mechanisms.

Authors:  Christopher DiMattina
Journal:  Vision Res       Date:  2021-11-15       Impact factor: 1.886

8.  Redundancy between spectral and higher-order texture statistics for natural image segmentation.

Authors:  Daniel Herrera-Esposito; Leonel Gómez-Sena; Ruben Coen-Cagli
Journal:  Vision Res       Date:  2021-06-30       Impact factor: 1.984

9.  Transsaccadic integration is dominated by early, independent noise.

Authors:  Emma E M Stewart; Alexander C Schütz
Journal:  J Vis       Date:  2019-06-03       Impact factor: 2.240

10.  Spatial competition on the master-saliency map.

Authors:  Ursula Schade; Cristina Meinecke
Journal:  Front Psychol       Date:  2013-07-02
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.