Collaborative Research: Simultaneous Contour Grouping and Medial Axis Estimation

最后修改日期：07/21/08最后修改人：丹尼尔F.德门通 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。现在的图像和视频搜索引擎是基于文本描述，因为视觉线索是在太低的水平，提供有用的检索结果时，处理大量的各种图像和视频。例如，如果一个人提交了一个查询图像，请求找到类似的图像，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）取得真正进展的关键因素。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方法的发展，这些方法试图在没有对象提取的情况下进行CBIR。然而，虽然这种方法可能是成功的，在一些有限的应用领域，在这种情况下，低级别的功能可能足以取代对象提取，他们还没有成功的通用CBIR。PI相信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。已经有大量的尝试来解决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提出的一种新的方法和计算框架提供了坚实的证据，表明对象提取的突破是可能的。在认知和几何建模方面，PI建议使用形状相似性的更高层次的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动机约束。约束是必不可少的，因为对象提取是一个不适定的逆问题。人类的视觉系统很好地解决了这个问题，我们正在接近全面了解这是如何做到的。在计算方面，PI提出了一个新的框架，同时估计中轴和轮廓。所提出的方法的灵感来自SLAM（同时定位和地图）方法在机器人地图领域。最近机器人地图的突破性解决方案是基于粒子滤波器的SLAM计算。SLAM计算迭代机器人在现有部分地图中的定位过程（轨迹估计），然后根据新的观察结果和估计的轨迹进行地图更新。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联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/

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/