Distinguishing shadows from surface boundaries using local achromatic cues.
Distinguishing shadows from surface boundaries using local achromatic cues.
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DOI：
10.1371/journal.pcbi.1010473
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发表时间：
2022-09
影响因子：
4.3
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中科院分区：
文献类型：
--
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为了准确地将视觉场景解析为不同的表面，必须确定局部亮度边缘是由两个表面之间的边界还是由单个表面上投射的阴影引起的。先前的研究已经表明，局部彩色线索可能有助于区分由阴影引起的边缘与由表面边界引起的边缘，但是在局部非彩色线索中潜在可用的信息，如对比度、纹理和半影模糊仍然知之甚少。在这项研究中，我们开发和分析了一个大型数据库的手工标记的无色阴影边缘，以更好地了解什么图像属性区分他们从闭塞边缘。我们发现，最高的对比度以及最低的对比度的边缘更有可能是闭塞比阴影，扩展以前的观察的基础上更有限的图像集。我们还发现，单独的对比度线索可以可靠地区分两个边缘类别，在40 x40分辨率下的准确率接近70%。逻辑回归的Gabor滤波器银行（GFB）建模的人口V1简单的细胞分离的类别，近80%的准确性，并进一步表现出调整半影模糊。一个滤波器-整流滤波器（FRF）风格的神经网络扩展GFB模型的准确率超过80%，并表现出模糊调整和更大的敏感性纹理差异。我们将人类在边缘分类任务上的表现与FRF和GFB模型进行了比较，找到了最好的人类观察者，其性能与机器分类器相同。几项分析表明，这两种分类器都与人类行为表现出显着的正相关性，尽管我们发现人类行为与FRF模型之间的逐图像一致性略好于GFB模型，这表明纹理的重要作用。区分由光照变化引起的边缘和由表面边界引起的边缘是精确解析视觉场景的基本计算。以前的心理物理学调查研究的效用，各种本地可用的线索，分类边缘阴影或表面边界主要集中在颜色，表面边界往往会引起更显着的变化比阴影的颜色。然而，即使在灰度图像中，我们也可以很容易地将阴影与表面边界区分开来，这表明除了颜色之外，非彩色线索也起着重要作用。我们证明使用自然阴影和表面边界边缘的统计分析，本地可用的非彩色线索可以利用机器分类器来可靠地区分这两个边缘类别。这些分类器表现出对模糊和局部纹理差异的敏感性，并且表现出与人类将边缘分类为阴影或表面边界的相当好的一致性。由于三色视觉在动物王国中相对罕见，我们的工作表明缺乏丰富颜色视觉的生物体仍然可以利用其他线索来避免将照明变化误认为表面变化。
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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