RI: Small: Bounded Distortion Models for Articulated and Deformable Object Recognition

RI：小：用于铰接和可变形物体识别的有界畸变模型

• 批准号: 1526234
• 负责人: David Jacobs
• 金额: $ 43.56万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526234&HistoricalAwards=false
• 关键词: RI; Small; Bounded; Distortion; Models

This project develops technologies for understanding shapes and parts of a person or animal and how these relate to their surface appearance. By building models that capture the variations in shape and pose of humans and animals, it becomes possible to understand the way that a person or animal's appearance changes as its arms and legs move or its head turns. These models can then be aligned with images, assisting in the recognition of figures and the determination of their pose. Understanding human pose and activity is a fundamental problem in computer vision with a host of interesting applications in surveillance, video retrieval, and automated video annotation. Automated systems that can identify the species of animals can form the basis for automated field guides that can be used in education and studies of biodiversity. This research develops new algorithms for matching image features and registering 3D models with bounded distortion mappings. The research team models people and animals using a skeleton capturing their articulations, along with a deformable skin model and an appearance model encoded by classifiers that can identify body parts of an animal. Given an image, the system computes an optimal bounded distortion transformation to register the model with the image. The system identifies both the pose and shape change of the person or animal with respect to the model and provides a way to rank possible detections of the model. The research team explores the problem of identifying the species of animals. The research team further applies algorithms to determine the pose of humans in images.

该项目开发了了解人或动物的形状和部分的技术，以及它们与它们的表面外观如何相关。通过构建捕获人类和动物形状和姿势变化的模型，可以理解一个人或动物的外观随着手臂和腿的移动或头部转弯而变化的方式。 然后可以将这些模型与图像对齐，从而有助于识别数字及其姿势的确定。了解人类的姿势和活动是计算机视觉中的一个基本问题，并在监视，视频检索和自动化视频注释中有许多有趣的应用。可以识别动物物种的自动化系统可以构成可用于教育和生物多样性研究的自动化现场指南的基础。这项研究开发了用于匹配图像特征的新算法，并注册具有有界失真映射的3D模型。研究团队使用骨骼捕获其发音的骨骼以及可变形的皮肤模型以及可以识别动物身体部位的分类器编码的外观模型来对人和动物进行建模。给定图像，系统计算出最佳的有界失真转换，以用图像注册模型。该系统识别相对于模型的人或动物的姿势和形状变化，并提供了一种对模型可能检测的方法。研究小组探讨了识别动物物种的问题。研究团队进一步应用算法来确定图像中人类的姿势。