CompCog: Template Contrast and Saliency (TCAS) Toolbox: a tool to visualize parallel attentive evaluation of scenes

CompCog：模板对比度和显着性 (TCAS) 工具箱：一种可视化场景并行注意力评估的工具

基本信息

批准号：
1921735
负责人：
Simona Buetti
金额：
$ 65.69万
依托单位：
University of Illinois at Urbana-Champaign
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-15 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1921735&HistoricalAwards=false
关键词：
CompCog Template Contrast Saliency TCAS

项目摘要

One of the most common visual tasks humans do is use their eyes to find objects in the world around them. This task involves analyzing all the visual objects and backgrounds in the scene. This is a complicated task because the brain has to separate objects from the background. The brain also has to process the color, shape, and size of all objects. The aim of the research is to build a mathematical model that can find objects in scenes, despite the difficulty of the problem. The model is inspired by the visual system. It uses two ways to process information. First, it uses central vision to get a fine-grained analysis of the object it is looking at. Second, it also uses peripheral vision, which is the area around and away from central vision. Peripheral vision can analyze several objects at the same time but is less precise than central vision. The ultimate goal of the project is to develop a free, open-source software toolbox that anyone can use. The toolbox will visualize how the visual system processes complex scenes. It will determine which regions in a scene should be ignored and which regions the eyes should focus on. One strength of the proposal is that it makes specific predictions that can be tested in various fields of neuroscience. It might also lead to improvements in visual aids for visually impaired individuals because it can guide users toward areas in a scene that are likely to contain the target object.The starting point for the proposed work is a mathematically explicit model of goal-directed visual processing. The model incorporates two components of visual complexity: a parameter that measures the visual difference between objects in the scene and the object the observer is looking for (the target) and a parameter that measures how similar objects in the scene are to one another. The preliminary work indicated that the model is very capable of predicting how long it will take observers to find targets in visually complex scenes. The first two goals of the present research aim at evaluating other components of visual complexity to improve the model and its ability to predict visual processing in more complex visual scenes. The experiments in Goals 1 and 2 will help determine how to combine the visual qualities of objects (such as color, shape and texture) as well as how to account for the contrast between objects and their background. Results from Goals 1 and 2 will directly guide the development of a computational toolbox. The toolbox will allow users to visualize visual processing of simple and complex scenes and make predictions about where observers are likely to move their eyes as a function of their current goals (freely inspect the scene or find a specific object within it). The proposed work combines behavioral psychophysics and computational simulations (Goals 1 and 2), toolbox implementation and eye-tracking validation (Goal 3). The merits of the toolbox include the fact that: 1) it combines different types of visual processing (visual conspicuity contrast and target template contrast), 2) it can predict eye movements over different time scales, and 3) it can evaluate the contribution of these two types of processing to performance. This implementation is important because the contribution of these two processes is known to vary as a function of search goals (free-view vs. goal-directed) and search strategy adopted by observers (active search vs. passive search). Finally, another innovation of the toolbox is that it will be able to make predictions when targets are only defined in abstract terms, that is, when observers only have vague descriptions about the item they are supposed to find in the scene, which is particularly challenging for current computer vision systems to achieve.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人类所做的最常见的视觉任务之一就是用眼睛在周围的世界中找到对象。此任务涉及分析场景中的所有视觉对象和背景。这是一个复杂的任务，因为大脑必须将对象与背景分开。大脑还必须处理所有物体的颜色，形状和大小。该研究的目的是建立一个数学模型，尽管问题很难，但仍可以在场景中找到对象。该模型灵感来自视觉系统。它使用两种处理信息的方法。首先，它使用中央视觉对其正在看的对象进行细粒度分析。其次，它还使用外围视觉，这是远离中央视觉的区域。外围视觉可以同时分析几个对象，但不如中央视觉精确。该项目的最终目标是开发任何人都可以使用的免费开源软件工具箱。该工具箱将可视化视觉系统如何处理复杂场景。它将确定应该忽略场景中的哪些区域，以及眼睛应该关注的区域。该提案的一项优势是，它做出了可以在神经科学的各个领域进行测试的特定预测。它还可能导致视力障碍个人的视觉辅助工具的改善，因为它可以指导用户进入可能包含目标对象的场景中的区域。拟议的工作的起点是数学上明确的目标视觉处理模型。该模型结合了两个视觉复杂性的组成部分：一个参数，该参数可以测量场景中的对象与观察者正在寻找的对象（目标）和一个参数，该参数衡量了场景中相似对象的相互作用。初步工作表明该模型非常有能力预测观察者在视觉上复杂的场景中找到目标需要多长时间。本研究的前两个目标旨在评估视觉复杂性的其他组成部分，以改善模型及其在更复杂的视觉场景中预测视觉处理的能力。目标1和2中的实验将有助于确定如何结合对象的视觉质量（例如颜色，形状和纹理），以及如何考虑对象及其背景之间的对比度。目标1和2的结果将直接指导计算工具箱的开发。该工具箱将允许用户可视化简单和复杂场景的可视化处理，并就观察者可能在哪里移动眼睛作为其当前目标的函数（自由检查场景或在其中找到特定对象）做出预测。提出的工作结合了行为心理物理学和计算模拟（目标1和2），工具箱实现和眼神招标验证（目标3）。该工具箱的优点包括以下事实：1）它结合了不同类型的视觉处理（视觉显着性对比度和目标模板对比度），2）它可以在不同的时间尺度上预测眼睛运动，3）它可以评估这两种处理对性能的贡献。该实现很重要，因为这两个过程的贡献是随着搜索目标（自由观看与目标定向）的函数而变化的，并且观察者采用了搜索策略（主动搜索与被动搜索）。最后，该工具箱的另一个创新是，仅当目标仅以抽象的方式定义目标时，它将能够做出预测，也就是说，当观察者只对他们应该在现场中找到的项目有模糊的描述，这对于当前的计算机视觉系统而言尤其具有挑战性。对于当前的计算机视觉系统而言，这是实现的奖项，这是NSF的法定任务，反映了通过评估的范围来弥补构成的范围，而这是众所周知的范围。