...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Computer Vision >ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images
【24h】

ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images

机译：ROML：一种用于匹配一组图像中对象的鲁棒特征对应方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Feature-based object matching is a fundamental problem for many applications in computer vision, such as object recognition, 3D reconstruction, tracking, and motion segmentation. In this work, we consider simultaneously matching object instances in a set of images, where both inlier and outlier features are extracted. The task is to identify the inlier features and establish their consistent correspondences across the image set. This is a challenging combinatorial problem, and the problem complexity grows exponentially with the image number. To this end, we propose a novel framework, termed Robust Object Matching using Low-rank constraint (ROML), to address this problem. ROML optimizes simultaneously a partial permutation matrix (PPM) for each image, and feature correspondences are established by the obtained PPMs. Two of our key contributions are summarized as follows. (1) We formulate the problem as rank and sparsity minimization for PPM optimization, and treat simultaneous optimization of multiple PPMs as a regularized consensus problem in the context of distributed optimization. (2) We use the alternating direction method of multipliers method to solve the thus formulated ROML problem, in which a subproblem associated with a single PPM optimization appears to be a difficult integer quadratic program (IQP). We prove that under wildly applicable conditions, this IQP is equivalent to a linear sum assignment problem, which can be efficiently solved to an exact solution. Extensive experiments on rigidon-rigid object matching, matching instances of a common object category, and common object localization show the efficacy of our proposed method.
机译：基于特征的对象匹配是计算机视觉中许多应用程序的基本问题，例如对象识别，3D重建，跟踪和运动分割。在这项工作中，我们考虑同时匹配一组图像中的对象实例，在这些图像中提取了内部和外部特征。任务是识别内部特征，并在整个图像集中建立一致的对应关系。这是一个具有挑战性的组合问题，并且问题的复杂性随图像数量呈指数增长。为此，我们提出了一种新颖的框架，称为使用低秩约束（ROML）的鲁棒对象匹配，以解决此问题。 ROML同时为每个图像优化部分置换矩阵（PPM），并通过获得的PPM建立特征对应关系。我们的两个主要贡献概述如下。 （1）我们将问题定义为PPM优化的秩和稀疏度最小化，并在分布式优化的背景下将多个PPM的同时优化视为规则化共识问题。 （2）我们使用乘数的交替方向方法来解决由此提出的ROML问题，其中与单个PPM优化相关的子问题似乎是一个困难的整数二次程序（IQP）。我们证明，在普遍适用的条件下，此IQP等效于线性和分配问题，可以有效地解决该问题。在刚性/非刚性对象匹配，常见对象类别的匹配实例以及常见对象本地化方面的大量实验证明了我们提出的方法的有效性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号