Lightness Constancy Through a Veiling Luminance

通过遮盖亮度保持亮度恒定

• 批准号: 1027093
• 负责人: Alan Gilchrist
• 金额: $ 10.06万
• 依托单位: Rutgers University Newark
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1027093&HistoricalAwards=false
• 关键词: Lightness; Constancy; Through; Veiling; Luminance

Dr. Alan Gilchrist at Rutgers University will conduct a series of experiments to discover how the human visual system can accurately determine the gray shade of visible surfaces even when the contrast in the image reaching the eye has been severely reduced. This occurs when a scene is viewed through a sheet of glass. Light reflected off the front surface of the glass (e.g., a reflection of the sky on a car windshield) combines with the pattern of light coming through the glass, reducing the contrast of that pattern. A bright glare source in the visual field can have the same effect, due to intense scattered light within the eyeball. The experiments will systematically isolate image features that are present in 3D scenes (where it is known that humans can successfully correct for such reflected light) and 2D patterns (where the correction fails). These features include shadows, light-absent crevices, and certain patterns where overlapping edges intersect. In addition, the perception of colored surfaces overlain with colored reflections will also be studied. The human visual system is able to determine the gray shade of objects despite changes in illumination level, changes in the background behind the object, and changes in the media that intervene between the viewer and the object. This latter problem has been almost totally neglected by vision research. A solution will advance our understanding of how the brain computes the gray shade of visible objects. It is widely agreed that in order to determine the gray level intensity of a surface, the human visual system relies crucially on the strength of contrast at the edges of that surface and neighboring surfaces. When the entire scene containing that surface is overlain with reflected light (or light scattered from a glare source), the strength of contrast at those edges is dramatically reduced. Nevertheless, when the scene is 3D and somewhat complex, the human eye can automatically disentangle the reflected light from the scene itself, and perceive the gray shade of the surface correctly. No machine visual system can do that. Knowledge about how the human visual system achieves this feat will contribute to the enhanced ability to program a machine to replicate it.

罗格斯大学的艾伦·吉尔克里斯特博士将进行一系列实验，以发现人类视觉系统如何准确地确定可见表面的灰色阴影，即使到达眼睛的图像的对比度已经严重降低。这发生在通过玻璃片观看场景时。 反射离开玻璃前表面的光（例如，天空在汽车挡风玻璃上的反射）与穿过玻璃的光的图案相结合，降低了该图案的对比度。由于眼球内的强烈散射光，视野中的明亮眩光源可以具有相同的效果。这些实验将系统地隔离3D场景（已知人类可以成功地校正这种反射光）和2D模式（校正失败）中存在的图像特征。这些特征包括阴影、无光裂缝和重叠边缘相交的某些图案。 此外，还将研究覆盖有彩色反射的彩色表面的感知。人类视觉系统能够确定对象的灰度，而不管照明水平的变化、对象后面的背景的变化以及介于观看者和对象之间的媒体的变化。后一个问题几乎完全被视觉研究所忽视。 一个解决方案将促进我们对大脑如何计算可见物体的灰色阴影的理解。 人们普遍认为，为了确定表面的灰度级强度，人类视觉系统关键地依赖于该表面和相邻表面的边缘处的对比度强度。当包含该表面的整个场景被反射光（或从眩光源散射的光）覆盖时，这些边缘处的对比度强度显著降低。尽管如此，当场景是3D的并且有些复杂时，人眼可以自动地从场景本身解开反射光，并且正确地感知表面的灰色阴影。机器视觉系统无法做到这一点。人类视觉系统如何实现这一壮举的知识将有助于增强编程机器复制它的能力。