RUI: Intermediate-level Vision: Grouping of generic features for image and video processing

RUI：中级视觉：图像和视频处理的通用特征分组

• 批准号: 1421734
• 负责人: Toshiro Kubota
• 金额: $ 18.74万
• 依托单位: Susquehanna University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421734&HistoricalAwards=false
• 关键词: RUI; Intermediate; level; Vision; Grouping

The human vision system is able to recognize objects and understand the scene from the boundary of the objects alone. It is astonishingly robust so that it can sustain this capability under distraction by non-boundary points and sparse sampling of the boundary points. How the system achieves this feat is largely unknown. This project investigates how a computer can replicate it algorithmically. The problem is fundamental and closely related to perceptual organization and intermediate level vision problems. Thus, this research has the potential to impact a wide range of computer vision applications. Since the input (a small set of isolated points) is small compared to the whole image and has no color information, the algorithm is efficient, robust against changes in illumination and contrast, and applicable to any imaging modalities.The main problem is to interpolate boundary points into a perceptually salient set of surfaces without being distracted by spurious non-boundary points. Interpolation by straight skeletons brings a time-reversible, multi-scale representation of a point set where salient boundary points tend to form a polygonal surface persistently while spurious non-boundary points tend to disappear quickly as the scale increases. Because of the time-reversible nature, a surface at each scale can be traced back to the original scale or the original point set. Thus, this technique can be used to form a set of salient surfaces from the original point set. This research develops a general purpose feature grouping algorithm using the straight skeleton interpolation and applies it to a number of intermediate vision problems in 2D and 3D domains. The investigator actively involves undergraduate students into the research and promotes STEM education in the northeastern part of the U.S.A through presentations, demonstrations, and outreach activities. The source code and toolboxes will be made publicly available and used to promote STEM education.

人类视觉系统能够仅从物体的边界识别物体并理解场景。它是非常健壮的，因此它可以在非边界点和边界点的稀疏采样的干扰下维持这种能力。该系统如何实现这一壮举在很大程度上是未知的。这个项目研究计算机如何通过算法复制它。这个问题是基本的，与感知组织和中间层次视觉问题密切相关。因此，这项研究有可能影响广泛的计算机视觉应用。由于输入（一小部分孤立点）相对于整个图像来说很小，并且没有颜色信息，所以该算法是高效的，对光照和对比度的变化具有鲁棒性，并且适用于任何成像modals.The主要问题是将边界点插值到感知上显著的表面集合中，而不被虚假的非边界点分散注意力。直骨架插值带来了一个时间可逆的，多尺度表示的点集，其中显着的边界点往往会形成一个多边形表面持久，而虚假的非边界点往往会迅速消失，随着规模的增加。由于时间可逆的性质，每个尺度下的曲面都可以追溯到原始尺度或原始点集。因此，该技术可以用于从原始点集形成一组显著表面。本研究发展一种使用直骨架内插的通用特徴分组演算法，并将其应用于二维与三维领域的中间视觉问题。研究人员积极让本科生参与研究，并通过演讲，演示和外展活动促进美国东北部的STEM教育。源代码和工具箱将公开提供，并用于促进STEM教育。