分层图形匹配与图形编辑

Liang Lin, Song-Chun Zhu, Yongtian Wang
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引用次数: 22

摘要

许多视觉任务被提出为图划分(着色)或图匹配(对应)问题。前者包括分割和分组,后者包括宽基线立体、大运动、目标跟踪和识别。本文在贝叶斯框架下,利用一种有效的采样算法,给出了图匹配和图划分的综合解决方案。给定两个待匹配的图像,我们使用原始素描算法[4]提取两个图。图节点是linelets和primitives(连接点)。两个图被自动划分为未知数量的K + 1层子图,这样K对子图被匹配,剩下的一层包含不匹配的背景。每个匹配对代表一个“移动对象”,使用TPS(薄板样条)变换来解释其变形,并使用一组图算子来编辑子图对以实现完美的结构匹配。两个子图之间的匹配能量包括几何变形、外观不相似和图编辑算子的代价。我们展示了它在两个任务上的应用:(i)有遮挡的大运动,以及(ii)一对图像中共同物体的自动检测和识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Layered Graph Match with Graph Editing
Many vision tasks are posed as either graph partitioning (coloring) or graph matching (correspondence) problems. The former include segmentation and grouping, and the latter include wide baseline stereo, large motion, object tracking and recognition. In this paper, we present an integrated solution for both graph matching and graph partition using an effective sampling algorithm in a Bayesian framework. Given two images for matching, we extract two graphs using a primal sketch algorithm [4]. The graph nodes are linelets and primitives (junctions). Both graphs are automatically partitioned into an unknown number of K + 1 layers of subgraphs so that K pairs of subgraphs are matched and the remaining layer contains unmatched backgrounds. Each matched pair represent a "moving object" with a TPS (thin-plate-spline) transform to account for its deformations and a set of graph operators to edit the pair of subgraphs to achieve perfect structural match. The matching energy between two subgraphs includes geometric deformations, appearance dissimilarities, and the cost of graph editing operators. We demonstrate its application on two tasks: (i) large motion with occlusion, and (ii) automatic detection and recognition of common objects in a pair of images.
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