Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
  Seeking summary statistics that match peripheral visual appearance using naturalistic textures generated by Deep Neural Networks

Wallis, T., Ecker, A., Gatys, L., Funke, C., Wichmann, F., & Bethge, M. (2016). Seeking summary statistics that match peripheral visual appearance using naturalistic textures generated by Deep Neural Networks. Poster presented at 16th Annual Meeting of the Vision Sciences Society (VSS 2016), St. Pete Beach, FL, USA.

Item is

Externe Referenzen

einblenden:
ausblenden:
externe Referenz:
Link (beliebiger Volltext)
Beschreibung:
-
OA-Status:

Urheber

einblenden:
ausblenden:
 Urheber:
Wallis, TSA1, Autor
Ecker, AS1, Autor           
Gatys, LA1, Autor
Funke, CM1, Autor
Wichmann, FA1, Autor           
Bethge, M1, Autor           
Affiliations:
1Werner Reichardt Center for Integrative Neuroscience, Eberhard Karls Universität Tübingen, ou_persistent22              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: An important hypothesis that emerged from crowding research is that the perception of image structure in the periphery is texture-like. We investigate this hypothesis by measuring perceptual properties of a family of naturalistic textures generated using Deep Neural Networks (DNNs), a class of algorithms that can identify objects in images with near-human performance. DNNs function by stacking repeated convolutional operations in a layered feedforward hierarchy. Our group has recently shown how to generate shift-invariant textures that reproduce the statistical structure of natural images increasingly well, by matching the DNN representation at an increasing number of layers. Here, observers discriminated original photographic images from DNN-synthesised images in a spatial oddity paradigm. In this paradigm, low psychophysical performance means that the model is good at matching the appearance of the original scenes. For photographs of natural textures (a subset of the MIT VisTex dataset), discrimination performance decreased as the DNN representations were matched to higher convolutional layers. For photographs of natural scenes (containing inhomogeneous structure), discrimination performance was nearly perfect until the highest layers were matched, whereby performance declined (but never to chance). Performance was only weakly related to retinal eccentricity (from 1.5 to 10 degrees) and strongly depended on individual source images (some images were always hard, others always easy). Surprisingly, performance showed little relationship to size: within a layer-matching condition, images further from the fovea were somewhat harder to discriminate but this result was invariant to a three-fold change in image size (changed via up/down sampling). The DNN stimuli we examine here can match texture appearance but are not yet sufficient to match the peripheral appearance of inhomogeneous scenes. In the future, we can leverage the flexibility of DNN texture synthesis for testing different sets of summary statistics to further refine what information can be discarded without affecting appearance.

Details

einblenden:
ausblenden:
Sprache(n):
 Datum: 2016-08
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1167/16.12.230
BibTex Citekey: WallisEGFWB2016
 Art des Abschluß: -

Veranstaltung

einblenden:
ausblenden:
Titel: 16th Annual Meeting of the Vision Sciences Society (VSS 2016)
Veranstaltungsort: St. Pete Beach, FL, USA
Start-/Enddatum: 2016-05-13 - 2016-05-18

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: Journal of Vision
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Charlottesville, VA : Scholar One, Inc.
Seiten: - Band / Heft: 16 (12) Artikelnummer: - Start- / Endseite: 230 Identifikator: ISSN: 1534-7362
CoNE: https://pure.mpg.de/cone/journals/resource/111061245811050