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/

上次修改日期：07/21/08上次修改者：Daniel F. Dementhon摘要，图像和视频数量的增长速度越来越大，提取它们中包含的信息的主要瓶颈是他们的分析（索引）和检索。如今，图像和视频搜索引擎基于文本描述，因为视觉提示的水平太低，无法在处理各种图像和视频时提供有用的检索结果。例如，如果人类提交了一个查询图像，并要求查找类似图像的请求，则她专注于查询图像中的某个对象或一组对象。因此，相似性的含义由包含相似对象的图像给出。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）真正进步的关键因素。但是，对象提取属于计算机视觉（CV）中未解决的问题。这一事实导致了许多尝试在没有物体提取的情况下尝试进行CBIR的大量方法。但是，尽管这种方法可能在某些受限制的应用程序域中取得成功，但是在这种情况下，低级别的特征可能足以替代对象提取，但在通用CBIR中，它们尚未成功。 PI认为解决对象提取问题将导致CBIR的突破。因此，PI建议在图像中进行对象提取。在CV中解决了对象提取问题，已经进行了大量尝试，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可靠的证据，表明对象提取的突破是可能的。在认知和几何建模方面，PIS建议使用更高层次的形状相似性知识以及对局部和全局对称性的中层知识作为对象提取的认知动机的约束。约束是必不可少的，因为已知对象提取是一个不良的逆问题。人类的视觉系统很好地解决了这个问题，我们对完成方式的完整了解。在计算侧，PI为同时估计内侧轴和轮廓的新框架提出了一个新的框架。所提出的方法的灵感来自在机器人映射领域中的猛击（同时定位和映射）方法。机器人映射中的最新突破解决方案是基于用粒子过滤器的猛击计算。 SLAM计算在现有部分映射（轨迹估计）中的机器人定位过程（轨迹估计）中进行了迭代，然后基于新观测值和估计的轨迹进行地图更新。 PI将内侧轴视为虚拟机器人的轨迹，而部分边界则是由与内侧轴相关的边段组成的图。 PI在初步结果中证明了该框架的第一个成功应用。项目URL：http：//knight.cis.temple.edu/~shape/