Segmenting surface boundaries using luminance cues

Abstract Segmenting scenes into distinct surfaces is a basic visual perception task, and luminance differences between adjacent surfaces often provide an important segmentation cue. However, mean luminance differences between two surfaces may exist without any sharp change in albedo at their boundar...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Christopher DiMattina, Curtis L. Baker
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/09e74818c766421389116e2993303243
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:09e74818c766421389116e2993303243
record_format	dspace
spelling	oai:doaj.org-article:09e74818c766421389116e29933032432021-12-02T17:16:14ZSegmenting surface boundaries using luminance cues10.1038/s41598-021-89277-22045-2322https://doaj.org/article/09e74818c766421389116e29933032432021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89277-2https://doaj.org/toc/2045-2322Abstract Segmenting scenes into distinct surfaces is a basic visual perception task, and luminance differences between adjacent surfaces often provide an important segmentation cue. However, mean luminance differences between two surfaces may exist without any sharp change in albedo at their boundary, but rather from differences in the proportion of small light and dark areas within each surface, e.g. texture elements, which we refer to as a luminance texture boundary. Here we investigate the performance of human observers segmenting luminance texture boundaries. We demonstrate that a simple model involving a single stage of filtering cannot explain observer performance, unless it incorporates contrast normalization. Performing additional experiments in which observers segment luminance texture boundaries while ignoring super-imposed luminance step boundaries, we demonstrate that the one-stage model, even with contrast normalization, cannot explain performance. We then present a Filter–Rectify–Filter model positing two cascaded stages of filtering, which fits our data well, and explains observers' ability to segment luminance texture boundary stimuli in the presence of interfering luminance step boundaries. We propose that such computations may be useful for boundary segmentation in natural scenes, where shadows often give rise to luminance step edges which do not correspond to surface boundaries.Christopher DiMattinaCurtis L. BakerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Christopher DiMattina Curtis L. Baker Segmenting surface boundaries using luminance cues
description	Abstract Segmenting scenes into distinct surfaces is a basic visual perception task, and luminance differences between adjacent surfaces often provide an important segmentation cue. However, mean luminance differences between two surfaces may exist without any sharp change in albedo at their boundary, but rather from differences in the proportion of small light and dark areas within each surface, e.g. texture elements, which we refer to as a luminance texture boundary. Here we investigate the performance of human observers segmenting luminance texture boundaries. We demonstrate that a simple model involving a single stage of filtering cannot explain observer performance, unless it incorporates contrast normalization. Performing additional experiments in which observers segment luminance texture boundaries while ignoring super-imposed luminance step boundaries, we demonstrate that the one-stage model, even with contrast normalization, cannot explain performance. We then present a Filter–Rectify–Filter model positing two cascaded stages of filtering, which fits our data well, and explains observers' ability to segment luminance texture boundary stimuli in the presence of interfering luminance step boundaries. We propose that such computations may be useful for boundary segmentation in natural scenes, where shadows often give rise to luminance step edges which do not correspond to surface boundaries.
format	article
author	Christopher DiMattina Curtis L. Baker
author_facet	Christopher DiMattina Curtis L. Baker
author_sort	Christopher DiMattina
title	Segmenting surface boundaries using luminance cues
title_short	Segmenting surface boundaries using luminance cues
title_full	Segmenting surface boundaries using luminance cues
title_fullStr	Segmenting surface boundaries using luminance cues
title_full_unstemmed	Segmenting surface boundaries using luminance cues
title_sort	segmenting surface boundaries using luminance cues
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/09e74818c766421389116e2993303243
work_keys_str_mv	AT christopherdimattina segmentingsurfaceboundariesusingluminancecues AT curtislbaker segmentingsurfaceboundariesusingluminancecues
_version_	1718381183348244480

Segmenting surface boundaries using luminance cues

Ejemplares similares