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

该项目开发的技术用于了解人或动物的形状和部位，以及这些与其表面外观的关系。通过建立捕捉人类和动物形状和姿势变化的模型，可以理解人或动物在手臂和腿移动或头部转动时外表的变化方式。然后，这些模型可以与图像对齐，帮助识别人物并确定其姿势。理解人体姿势和活动是计算机视觉中的一个基本问题，在监控、视频检索和自动视频标注中有许多有趣的应用。能够识别动物物种的自动化系统可以构成自动化野外指南的基础，这些指南可以用于生物多样性的教育和研究。这项研究开发了新的算法，用于匹配图像特征和配准具有有界失真映射的3D模型。研究小组使用捕捉人和动物关节的骨骼，以及可变形的皮肤模型和由分类器编码的外表模型来对人和动物进行建模，分类器可以识别动物的身体部位。在给定图像的情况下，系统计算最佳有界失真变换以将模型与图像配准。该系统识别人或动物相对于模型的姿势和形状变化，并提供对可能的模型检测进行排序的方法。研究小组探索了识别动物物种的问题。研究小组进一步应用算法来确定图像中人的姿势。

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.