Title: Object recognition with pictorial structures
Abstract: This thesis presents a statistical framework for object recognition. The framework is motivated by the pictorial structure models introduced by Fischler and Elschlager nearly 30 years ago. The basic idea is to model an object by a collection of parts arranged in a deformable conguration. The appearance of each part is modeled separately, and the deformable con guration is represented by spring-like connections between pairs of parts. These models allow for qualitative descriptions of visual appearance, and are suitable for generic recognition problems. The problem of detecting an object in an image and the problem of learning an object model using training examples are naturally formulated under a statistical approach. We present eAEcient algorithms to solve these problems in our framework. We demonstrate our techniques by training models to represent faces and human bodies. The models are then used to locate the corresponding objects in novel images. Thesis Supervisor: W. Eric L. Grimson Title: Bernard Gordon Professor of Medical Engineering
Publication Year: 2001
Publication Date: 2001-05-01
Language: en
Type: dissertation
Access and Citation
Cited By Count: 5
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot