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.

该项目开发的技术用于理解人或动物的形状和部分，以及它们与表面外观的关系。通过建立模型，捕捉人类和动物的形状和姿势的变化，就有可能理解一个人或动物的外貌是如何随着胳膊和腿的移动或头部的转动而变化的。然后，这些模型可以与图像对齐，帮助识别人物并确定他们的姿势。理解人体姿势和活动是计算机视觉的一个基本问题，在监控、视频检索和自动视频注释方面有许多有趣的应用。能够识别动物种类的自动化系统可以构成自动化野外指南的基础，可用于生物多样性的教育和研究。本研究开发了新的算法来匹配图像特征和注册三维模型与有界失真映射。研究小组使用捕捉关节的骨骼，以及可变形的皮肤模型和由可以识别动物身体部位的分类器编码的外观模型来模拟人和动物。给定图像，系统计算一个最优的有界失真变换，将模型与图像配准。该系统识别人或动物相对于模型的姿势和形状变化，并提供一种对模型的可能检测进行排序的方法。这个研究小组探讨了鉴别动物种类的问题。研究小组进一步应用算法来确定图像中人类的姿势。