面向非刚体形变的三维形状表示与分析关键技术研究

项目名称： 面向非刚体形变的三维形状表示与分析关键技术研究

项目编号： No.61301222

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 无线电电子学、电信技术

项目作者： 郝世杰

作者单位： 合肥工业大学

项目金额： 22万元

中文摘要： 形状是客观世界物体的基本属性之一。不同物体形状之间不但千差万别，即使对同一物体来说，其自身的非刚体形状变化也极其丰富。这给高效地管理与分析形状媒体带来了挑战。现有形状分析研究工作在非刚体形状表示、形状相似度衡量、形状对应关系计算等方面中还有很多问题亟待解决。针对这些问题，本课题拟深入研究非刚体形变下的三维形状表示方法，具体内容包括：通过联合整体与局部特征来构造更为鲁棒的形状表示模型；构建度量学习模型来增强形状相似度量的区分性；构造基于主动学习的图匹配算法，提高三维形状对应关系计算模型的性能；建立三维形状媒体管理与分析系统，实现对具有非刚体形变的三维形状进行有效的检索、分类与匹配。

中文关键词： 形状表示；形状分析；图结构学习；图像增强；视频内容分析

英文摘要： Shape is one of the fundamental attributes of objects in the real world. Shape variations not only exist in inter-object differences, but are also reflected by various non-rigid intra-object deformations. This phenomenon brings challenges to the task of effectively managing and analyzing shape media. Some problems still remain in aspects such as representing non-rigid deformed 3-D shapes, measuring shape similarity and computing shape correspondence. Aiming at solving these problems, in this proposal, we conduct research on the key issues in representing and analyzing non-rigid deformed 3-D shapes. First, we propose to robustly represent non-rigid deformed shapes by unifying global and local features. Then we build a metric learning model to enhance the shape similarity metric. Also an active learning based graph matching framework is proposed to improve the performance of the shape correspondence model. Finally, based on the aforementioned techniques, a system for managing and analyzing shape media is thereof built, where we can retrieve, classify and match non-rigid deformed 3-D shapes effectively and efficiently.

英文关键词： shape representation；shape analysis；graph based learning；image enhancement；video content analysis