Exploring objects for recognition in the real world

Perception in natural systems is a highly active process. In this paper, we adopt the strategy of natural systems to explore objects for 3D object recognition using robots. The exploration of objects enables the system to learn objects from different viewpoints, which is essential for 3D object recognition. Exploration furthermore simplifies the segmentation of the object from its background, which is important for object learning in real-world environments, which are usually highly cluttered. We use the Scale Invariant Feature Transform (SIFT) as the basis for our object recognition system. We discuss our active vision approach to learn and recognize 3D objects in cluttered and uncontrolled environments. Furthermore, we propose a model to reduce the number of SIFT keypoints stored in the object database. It is a known drawback of SIFT that the computational complexity of the algorithm increases rapidly with the number of keypoints. We discuss the use of a growing-when-required (GWR) network, which is based on the Kohonen Self Organizing Feature Map, for efficient clustering of the keypoints. The results show successful learning of 3D objects in a cluttered and uncontrolled environment. Moreover, the GWR-network strongly reduces the number of keypoints.