Visual Search and Detection in Natural Scenes

 描述(申请人提供)：视觉科学的最终目标是理解和预测在自然条件下的表现，可以说，没有什么任务比视觉搜索环境中感兴趣的对象更普遍和更基本的任务了。然而，人们对自然场景中的搜索知之甚少。我们的目标是测量、描述和模拟人类和非人类灵长类动物在自然场景中的搜索表现。我们将研究隐蔽搜索和公开搜索，前者是在有限的时间内呈现搜索场景，而后者的眼睛保持在注视目标上，后者涉及眼球运动。我们的方法结合了数学/计算分析，人和猴子的心理物理和眼动测量，以及表现良好的猴子的V1电压敏感染料成像(VSDI)。我们认为，对视觉搜索的严格理解必须从描述在没有任何关于目标位置的不确定性的情况下控制目标在整个视野中的可探测性的感觉因素开始。在初步的心理物理、神经生理学和计算研究中，我们开发了一个目标可检测性模型，该模型可以预测自然图像中的简单检测性能。在进一步的计算研究中(基于检测结果)，我们发现了一种生物学上看似合理的计算，它将允许大脑在自然场景中执行最佳的公开搜索。具体地说，我们证明了在视觉搜索过程中，通过(I)用局部图像对比度划分(归一化)图，(Ii)适当地模糊对比度归一化图，然后(Iii)选择模糊图的峰值作为下一个注视位置，从目标可能所在的当前信念图(后验概率图)中获得最优的下一个注视位置。这项拟议的研究将测量人类和猴子在自然图像中的检测和视觉搜索性能，并将测量猴子在注视和检测任务中的VSDI反应。这些研究将使我们能够测试和进一步开发我们的简单模型 在自然图像中进行检测和隐蔽搜索，并确定人类在自然图像中搜索时使用的最佳固定搜索组件。它们还可能导致训练人类使用更优化的搜索策略的方法。

 DESCRIPTION (provided by applicant): An ultimate goal of vision science is to understand and predict performance under natural conditions, and there is arguably no task more ubiquitous and fundamental than visually searching the environment for objects of interest. However, little is known about search in natural scenes. Our aim is to measure, characterize, and model search performance in natural scenes in both humans and non-human primates. We will study both covert search, where the search scene is presented for a limited time and the eyes remain on a fixation target, and overt search, which involves eye movements. Our approach combines mathematical/computational analysis, psychophysical and eye-movement measurements in humans and monkeys, and voltage sensitive dye imaging (VSDI) in V1 of behaving monkeys. We argue that a rigorous understanding of visual search must begin with a characterization of the sensory factors that control the detectability of the target across the visual field in absence of any uncertainty about the location of the target. In preliminary psychophysical, neurophysiological, and computational studies we developed a model of target detectability that predicts simple detection performance in natural images. In further computational studies (based on the detection results), we discovered a biologically-plausible computation that would allow the brain to perform optimal overt search in natural scenes. Specifically, we prove that during a visual search, the optimal next fixation location is obtained from the current belief map (posterior probability map) of where the target might be located, by (i) dividing (normalizing) the map by the local image contrast, (ii) blurring this contrast-normalized map appropriately, and then (iii) selecting the peak of the blurred map as the next fixation location. The proposed studies will measure human and monkey detection and visual search performance in natural images, and will measure VSDI responses in monkeys during fixation and detection tasks. The studies will let us test and further develop our models of simple detection and covert search in natural images, and determine which components of optimal fixation search humans use when searching in natural images. They may also lead to methods for training humans to use more optimal search strategies.