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

该项目开发用于理解人或动物的形状和部位以及这些与其表面外观的关系的技术。通过构建捕捉人类和动物的形状和姿势变化的模型，可以了解人或动物的外观随着其手臂和腿移动或头部转动而变化的方式。 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。理解人类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为可用于生物多样性教育和研究的自动化野外指南奠定基础。本研究开发了新的算法，用于匹配图像特征和配准3D模型与有界失真映射。研究小组使用捕捉他们的关节的骨架，沿着可变形的皮肤模型和由分类器编码的外观模型来模拟人和动物，这些分类器可以识别动物的身体部位。给定图像，系统计算最优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，并提供一种对模型的可能检测进行排名的方法。研究小组探索了识别动物种类的问题。研究小组进一步应用算法来确定图像中人类的姿势。

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.