CAREER: Articulated Model Recognition

本研究针对从真实的图像中检测和识别人和动物等可变形关节形状的问题。其目的是开发一种自顶向下和自底向上的识别策略，其中自底向上检测特征，对它们进行分组并访问对象模型，而自顶向下使用对象模型来搜索特征。对象表示在一个树形图结构，从形状轴模型。 存储在节点中的信息包括概率，用于指定对象和成像功能的可能几何变形。 树图结构允许 为 根据树匹配描述遮挡和接合 运营 进一步降低 计算复杂性的任务，信息理论标准进行了研究，以确定功能，传达信息的对象在图像中。 PI还将开发和教授两门课程，其中一门是纽约大学的新课程，以支持计算机视觉的重点课程;这些课程将是“视觉导论”，以及“视觉和视觉”的专业课程。

This research addresses the problems of detection and recognition of deformable articulated shapes, such as people and animals, from real images. The aim is to develop a Top-down and Bottom-up recognition strategy, where the Bottom-up detects features, group them, and access the object model, while the Top-down uses the object model to search for features. Objects are represented in a tree-graph structure derived from a shape axis model. The information stored in the nodes include probabilities, used to specify likely geometric deformations of the object and of the imaging function. The tree-graph structure allows for a description of occlusions and articulations in terms of tree matching operations. To further reduce the computational complexity of the task, an information theory criteria is studied to determine features which convey information about the objects in the image. The PI will also develop and teach two courses, one of them new to NYU, to support a focused program in computer vision; these course will be``Introduction to Vision'', and a specialized course in ``Grouping and Recognition''.