A modular consistent, disciminative framework for structured output learning in computer vision. KU Leuven
State of the art computer vision systems are fundamentally reliant on statistical learning to optimize performance on a specific application. Currently, statistical frameworks in computer vision are typically based on classification and regression, probabilistic graphical models, or discriminative structured prediction frameworks such as the Structured Output Support Vector Machine (SOSVM). Although some of the best performing ...