REU/ CAREER: Deformable Shape Models for Image Understanding

研究计划是开发用于图像和视频理解的形状表示。这种方法是根据原型或极端视图的非刚性变形来编码形状和运动。这项研究建立在以前的模式匹配工作的基础上，模式匹配是一种正交形状分解，用于匹配、描述和比较形状，而不考虑传感器的变化和非刚性变形。形状系数和运动系数用可变形形状模型的本征振型表示。该项目在四个主要方向扩展了这个模式匹配框架：比较度量、基于原型的形状表示(包括强度信息)以及对运动和形状进行编码。主要的系统评估是基于专门设计用来描述关于噪声和变形的算法限制的合成数据集。系统性能将使用由万维网爬虫收集的大型图像数据库进行评估。为此，一个网络机器人正在建造中。该教育计划涉及多媒体计算的课程开发和教学，让本科生积极参与研究，并从传统上代表性较低的计算机科学群体中招收更多学生。

The research plan is to develop shape representations for image and video ulderstanding. The approach is to encode shape and motion in terms of nonrigid deformations from prototypical or extremal views. This research builds on previous work in modal matching, an orthogonal shape decomposition for matching, describing, and comparing shapes despite sensor variations and nonrigid deformations. Shape and motion coefficients are expressed in terms of the eigenmodes of deformable shape models. The project extends this modal matching framework in four main directions; comparison metrics, prototype-based shape representations, including intensity information, and encoding motion and shape. Primary system evaluation is on synthetic data sets specifically designed to characterize algorithm limits with respect to noise and deformation. System performance is to be evaluated using a large database of images collected by a world wide web crawler. Towards this end, a web robot is being built. The educational plan addresses course development and teaching in multimedia computing, active engagement of undergraduates in research, and outreach for recruiting a greater number of students from traditionally underrepresented groups in computer science.