Collaborative Research: Simultaneous Contour Grouping and Medial Axis Estimation

协作研究：同时轮廓分组和中轴估计

• 批准号: 0812167
• 负责人: Zygmunt Pizlo
• 金额: --
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812167&HistoricalAwards=false
• 关键词: Collaborative; Research; Simultaneous; Contour; Grouping

Last Modified Date: 07/21/08 Last Modified By: Daniel F. DeMenthon Abstract With the ever faster growing number of images and videos, the main bottleneck in extracting the information contained in them is their analysis (indexing) and retrieval. Nowadays image and video search engines are based on textual descriptions, since visual cues are at too low level to provide useful retrieval results when dealing with a large variety of images and videos. For example, if a human submits a query image with the request to find similar images, she focuses on a certain object or a group of objects in the query image. Thus, the meaning of similarity is given by the images that contain similar objects. Therefore, extraction of objects in images (and videos) is a key factor for true progress in content based image/video retrieval (CBIR). However, object extraction belongs to unsolved problems in Computer Vision (CV). This fact led to the development of a huge number of approaches that try to do CBIR without object extraction. However, although such approaches may be successful in some restricted application domains, in which case low level features may be sufficient to replace object extraction, they have not been successful in general purpose CBIR. The PIs believe solving the object extraction problem will lead to a breakthrough in CBIR. Therefore, the PIs propose to work on object extraction in images. There have been a large number of attempts to solve the object extraction problem in CV, and none provided a satisfactory solution. Why will our approach provide a good solution? A new methodology and a computation framework proposed by the PIs provide solid evidence that the breakthrough in object extraction is possible. On the cognitive and geometric modeling side, the PIs propose to use a higher level knowledge of shape similarity and a mid level knowledge of local and global symmetry as cognitively motivated constraints for object extraction. Constraints are essential because object extraction is known to be an ill-posed inverse problem. The human visual system solves this problem very well and we are getting close to a full understanding of how this is done. On the computational side, the PIs propose a new framework for a simultaneous estimation of medial axes and the contours. The proposed approach is inspired by the SLAM (Simultaneous Localization and Mapping) approaches in the field of robot mapping. Recent breakthrough solutions in robot mapping are based on the SLAM computation with particle filters. SLAM computation iterates over the processes of localization of the robot in the existing partial map (trajectory estimation), followed by a map update based on new observations and the estimated trajectory. The PIs treat the medial axis as trajectory of a virtual robot and the partial boundary as the map that is composed of edge segments associated with the medial axis. A first successful application of this framework is demonstrated by the PIs in the preliminary results. Project URL: http://knight.cis.temple.edu/~shape/

最后修改日期: 07/21/08 最后修改者: Daniel F. DeMenthon 摘要 随着图像和视频数量的快速增长，提取其中包含的信息的主要瓶颈是它们的分析（索引）和检索。如今，图像和视频搜索引擎基于文本描述，因为在处理大量各种图像和视频时，视觉线索的水平太低，无法提供有用的检索结果。例如，如果一个人提交了带有查找相似图像的请求的查询图像，她会关注查询图像中的某个对象或一组对象。因此，相似性的含义是由包含相似对象的图像给出的。因此，图像（和视频）中对象的提取是基于内容的图像/视频检索（CBIR）真正取得进展的关键因素。然而，目标提取属于计算机视觉（CV）中尚未解决的问题。这一事实导致了大量尝试在不进行对象提取的情况下进行 CBIR 的方法的开发。然而，尽管此类方法可能在某些受限应用领域取得成功（在这种情况下，低级特征可能足以取代对象提取），但它们在通用 CBIR 中尚未取得成功。 PI 相信解决对象提取问题将带来 CBIR 的突破。因此，PI 建议致力于图像中的对象提取。人们已经进行了大量的尝试来解决 CV 中的对象提取问题，但没有一个提供令人满意的解决方案。为什么我们的方法会提供一个好的解决方案？ PI 提出的新方法和计算框架提供了坚实的证据，证明对象提取方面的突破是可能的。在认知和几何建模方面，PI 建议使用形状相似性的高级知识和局部和全局对称性的中级知识作为对象提取的认知激励约束。约束是必不可少的，因为已知对象提取是一个不适定的逆问题。人类视觉系统很好地解决了这个问题，我们正在接近完全理解这是如何完成的。在计算方面，PI 提出了一个新的框架，用于同时估计中轴和轮廓。该方法的灵感来自于机器人测绘领域的 SLAM（同步定位与建图）方法。机器人测绘领域最新的突破性解决方案基于带有粒子滤波器的 SLAM 计算。 SLAM 计算迭代现有部分地图中的机器人定位过程（轨迹估计），然后根据新的观测结果和估计轨迹进行地图更新。 PI 将中轴视为虚拟机器人的轨迹，将部分边界视为由与中轴相关的边缘段组成的地图。初步结果中的 PI 证明了该框架的首次成功应用。项目网址：http://knight.cis.temple.edu/~shape/