Human-assisted computer vision

人类辅助计算机视觉

• 批准号: 238782-2011
• 负责人: Langer, Michael
• 金额: $ 1.46万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=524446
• 关键词: Human; assisted; computer; vision

A longstanding goal of computer vision has been to create software that can interpret scene content from images with a performance that is similar or even better than the human visual system. My research has been concerned with estimating scene properties such as the 3-D layout and shapes of surfaces, their material properties such as color and reflectance, and the scene illumination. The traditional goal has been to design computer vision algorithms that are as autonomous as possible, in particular, that rely as little as possible on human assistance to constrain the solution. Many specific computer vision problems can indeed can be solved with very little human assistance, though there remain many important problems that cannot. The proposed research will address a new and different goal, which is to achieve better computer vision performance by allowing for greater levels of human assistance than have been used in the past. Such user involvement is appropriate for many tasks, that do not require an autonomous system and for which the goal is as accurate solution as possible. Basic research questions will be addressed, such as what type and how much human assistance is required to achieve a given level of computer vision performance. The research will develop new methods that combine human and computer vision. We will use established psychophysical methods, namely shape probes, to measure the scene geometry that is perceived by humans, and we will apply these perceptual measurements as constraints on the solution of the computer vision algorithms. This is a significantly different approach to doing computer vision. Rather than asking what performance level can be attained using no (or minimal) human assistance, we will open up the problem and examine how much various levels and types of human assistance can improve the performance of the algorithms. The vision problems we will examine are very challenging, and include 3D stereo reconstruction of very cluttered scenes such as trees and bushes, as well as 3D shape and layout reconstruction in natural scenes using combined focus and texture cues.

计算机视觉的一个长期目标是创建能够从图像中解释场景内容的软件，其性能与人类视觉系统相似甚至更好。 我的研究一直关注估计场景属性，如三维布局和表面形状，其材料属性，如颜色和反射率，以及场景照明。 传统的目标是设计尽可能自主的计算机视觉算法，特别是尽可能少地依赖人类帮助来约束解决方案。 许多具体的计算机视觉问题确实可以在很少的人类帮助下解决，尽管仍然有许多重要的问题无法解决。 拟议的研究将解决一个新的和不同的目标，即通过允许比过去使用的更高水平的人类协助来实现更好的计算机视觉性能。 这种用户参与适用于许多任务，这些任务不需要自治系统，并且目标是尽可能准确的解决方案。 基本的研究问题将得到解决，例如需要什么类型和多少人力协助才能实现给定的计算机视觉性能水平。 这项研究将开发出联合收割机结合人类和计算机视觉的新方法。 我们将使用已建立的心理物理学方法，即形状探测器，来测量人类感知的场景几何形状，我们将应用这些感知测量作为计算机视觉算法解决方案的约束条件。 这是一种非常不同的计算机视觉方法。 而不是问什么性能水平可以达到没有（或最小）的人类援助，我们将打开这个问题，并检查有多少不同级别和类型的人类援助可以提高算法的性能。 我们将研究的视觉问题非常具有挑战性，包括树木和灌木丛等非常杂乱的场景的3D立体重建，以及使用组合焦点和纹理线索的自然场景中的3D形状和布局重建。