Pursuing the goals of absolute simplicity of a detection/recognition system, a pure learning approach to background-invariance and visual 3D object detection/recognition is proposed. The approach relies on learning from examples only, and does not encode any domain knowledge (e.g. in the form of intermediate representations, or by solving segmentation or correspondence problems). To make the pure learning approach practically feasible, we propose the BW training method for teaching an object recognition system background-invariance. The method consist of pedagogically training the system, once with a black background and once with a white background. The method is formulated within the framework of support vector learning. Evaluation is performed with the Columbia Image (COIL) database, that is extended with different classes of cluttered backgrounds. Using this pure learning approach, a system is proposed that is able to perform 3D object detection/recognition successfully in real-world scenes, with varying illuminations and backgrounds. The system is able to perform this task in real-time.
['Danny Roobaert', 'Michael Zillich', 'Jan-Olof Eklundh']
A pure learning approach to background-invariant object recognition using pedagogical support vector learning