Distinguishing shadows from surface boundaries using local achromatic cues

In order to accurately parse the visual scene into distinct surfaces, it is essential to determine whether a local luminance edge is caused by a boundary between two surfaces or a shadow cast across a single surface. Previous studies have demonstrated that local chromatic cues may help to distinguis...

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Bibliographic Details
Published in:	PLoS computational biology Vol. 18; no. 9; p. e1010473
Main Authors:	DiMattina, Christopher, Burnham, Josiah J, Guner, Betul N, Yerxa, Haley B
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 01-09-2022 Public Library of Science (PLoS)
Subjects:	Accuracy Achromatic devices Analysis Biology and Life Sciences Boundaries Classifiers Computer and Information Sciences Contrast Sensitivity Cues Filter banks Gabor filters Human performance Humans Image contrast Image databases Neural networks Neural Networks, Computer Occlusion Photic Stimulation - methods Physical Sciences Regression analysis Research and Analysis Methods Shades and shadows Shadows Social Sciences Texture Tuning United States
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Summary:	In order to accurately parse the visual scene into distinct surfaces, it is essential to determine whether a local luminance edge is caused by a boundary between two surfaces or a shadow cast across a single surface. Previous studies have demonstrated that local chromatic cues may help to distinguish edges caused by shadows from those caused by surface boundaries, but the information potentially available in local achromatic cues like contrast, texture, and penumbral blur remains poorly understood. In this study, we develop and analyze a large database of hand-labeled achromatic shadow edges to better understand what image properties distinguish them from occlusion edges. We find that both the highest contrast as well as the lowest contrast edges are more likely to be occlusions than shadows, extending previous observations based on a more limited image set. We also find that contrast cues alone can reliably distinguish the two edge categories with nearly 70% accuracy at 40x40 resolution. Logistic regression on a Gabor Filter bank (GFB) modeling a population of V1 simple cells separates the categories with nearly 80% accuracy, and furthermore exhibits tuning to penumbral blur. A Filter-Rectify Filter (FRF) style neural network extending the GFB model performed at better than 80% accuracy, and exhibited blur tuning and greater sensitivity to texture differences. We compare human performance on our edge classification task to that of the FRF and GFB models, finding the best human observers attaining the same performance as the machine classifiers. Several analyses demonstrate both classifiers exhibit significant positive correlation with human behavior, although we find a slightly better agreement on an image-by-image basis between human performance and the FRF model than the GFB model, suggesting an important role for texture.
Bibliography:	new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 The authors have declared that no competing interests exist.
ISSN:	1553-7358 1553-734X 1553-7358
DOI:	10.1371/journal.pcbi.1010473