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

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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.