THE VISUAL REQUIREMENTS OF EVERYDAY TASKS

将传播学理论应用于视觉的研究 系统将： O开发一张视觉敏感度图表，它可以“绕过”光学 眼睛。 噪声中的阈值对光学缺陷非常不敏感。 我们将测试并进一步开发噪声中的字母测试表 它利用这一事实来评估 存在受损的光学系统。理论和试点数据表明， 性能几乎完全不受光学缺陷的影响， 意味着噪声中字母低于正常的患者 敏感症必须有神经缺陷，要么是视网膜，要么是中枢。 测试表具有评估视力的潜力，即使在 致密性白内障的存在，并可能对 区分不同种类的自然赤字。 O测量视觉注意的信息容量。 我们假设视觉注意的信息容量 可以通过信息容量的大小来表征 需要在显示器上表现出最佳的专注力 任务由观察者完成。我们预测这一信息容量 对于所有可视任务，都将是恒定的，大约为100位 需要注意的。12个实验将检验威尔假说 通过测量各种类型的所需信息容量 视觉任务。 O从运动中提取结构。 我们将研究人类的运动和深度戒律 图像受低噪声限制时的机器视觉系统 分辨率低、对比度低、视野小或时间模糊。 我们将使用现有的运动算法来提取三维 结构由二维速度场构成。人类 在同一任务中的表现将与 算法。结果将测试现有的运动结构 作为人类运动和深度感知模型的算法 视觉系统。

Communication theory will be applied to the study of the visual system to: o Develop a visual sensitivity chart which "bypasses" the optics of the eye. Thresholds in noise are remarkably insensitive to optical deficits. We will test and further develop a letters-in-noise test chart which exploits this fact to assess visual performance in the presence of impaired optics. Theory and pilot data show that performance is almost totally unaffected by optical deficits, implying that patients with below normal letter-in-noise sensitivity must have neural deficits, either retinal or central. The test chart has potential for assessing vision even in the presence of dense cataracts, and may have diagnostic value in distinguishing different kinds of natural deficit. o Measure the information capacity of visual attention. We hypothesize that the information capacity of visual attention can be characterized by how much information capacity is required at the display for optimal performance of an attentive task by the observer. We predict that this information capacity will be constant, on the order of 100 bits, for all visual tasks requiring attention. Twelve experiments will test will hypothesis by measuring the required information capacity for a wide variety of visual tasks. o Extract structure from motion. We will investigate motion and depth preception of human and machine vision systems when images are restricted by low resolution, low contrast, small visual field, or temporal blurring. We will use existing motion algorithms to extract 3-dimensional structure from a two-dimensional velocity flow field. Human performance at the same task will be compared to that of the algorithm. The results will test existing structure-from-motion algorithms as models of motion and depth perception in the human visual system.