DiMattina C, Burnham JJ, Guner BN, Yerxa HB. Distinguishing shadows from surface boundaries using local achromatic cues.
PLoS Comput Biol 2022;
18:e1010473. [PMID:
36103558 PMCID:
PMC9512248 DOI:
10.1371/journal.pcbi.1010473]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 09/26/2022] [Accepted: 08/05/2022] [Indexed: 11/24/2022] Open
Abstract
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.
Distinguishing edges caused by changes in illumination from edges caused by surface boundaries is an essential computation for accurately parsing the visual scene. Previous psychophysical investigations examining the utility of various locally available cues to classify edges as shadows or surface boundaries have primarily focused on color, as surface boundaries often give rise to more significant change in color than shadows. However, even in grayscale images we can readily distinguish shadows from surface boundaries, suggesting an important role for achromatic cues in addition to color. We demonstrate using statistical analysis of natural shadow and surface boundary edges that locally available achromatic cues can be exploited by machine classifiers to reliably distinguish these two edge categories. These classifiers exhibit sensitivity to blur and local texture differences, and exhibit reasonably good agreement with humans classifying edges as shadows or surface boundaries. As trichromatic vision is relatively rare in the animal kingdom, our work suggests how organisms lacking rich color vision can still exploit other cues to avoid mistaking illumination changes for surface changes.
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