Collaborative Research: Simultaneous Contour Grouping and Medial Axis Estimation

最后修改日期：07/21/08最后修改人：Daniel F.DeMenthon摘要随着图像和视频数量的不断增长，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。如今的图像和视频搜索引擎是基于文本描述的，因为在处理大量的图像和视频时，视觉线索的级别太低，无法提供有用的检索结果。例如，如果一个人提交了一个带有查找相似图像的请求的查询图像，则她将重点放在查询图像中的某个对象或一组对象上。因此，相似性的含义由包含相似对象的图像来给出。因此，图像(和视频)中目标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领域中尚未解决的问题。这一事实导致了大量方法的发展，这些方法试图在不提取对象的情况下进行CBIR。然而，尽管这种方法在一些受限的应用领域可能是成功的，在这种情况下，低层特征可能足以取代对象提取，但它们在通用CBIR中并不成功。PI相信，解决目标提取问题将导致CBIR的突破。因此，PI提出了在图像中提取对象的工作。已经有大量的尝试来解决CV中的对象提取问题，但没有一个能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？PIS提出的一种新的方法和计算框架提供了确凿的证据，证明在目标提取方面取得突破是可能的。在认知和几何建模方面，PI建议使用较高层次的形状相似性知识和中层的局部和全局对称性知识作为认知动机的约束来提取对象。约束是必不可少的，因为众所周知，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，我们正在接近完全理解这是如何做到的。在计算方面，PI提出了一种同时估计中轴和等高线的新框架。该方法是受机器人地图绘制领域中SLAM(同时定位和地图绘制)方法的启发。最近在机器人地图绘制方面的突破性解决方案是基于粒子过滤器的SLAM计算。SLAM计算迭代现有部分地图中机器人的定位过程(轨迹估计)，然后基于新的观测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，将局部边界视为由与内侧轴关联的边缘段组成的地图。初步结果显示，PIS首次成功地应用了这一框架。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/