Active object recognition based on Fourier descriptors clustering

This paper presents a new 3D object recognition/pose strategy based on Fourier descriptors clustering for silhouettes. The method consists of two parts. Firstly, an off-line process calculates and stores a clustered Fourier descriptors database corresponding to the silhouettes of the synthetic model of the object viewed from multiple viewpoints. Next, an on-line process solves the recognition/pose problem for an object that is sensed by a camera placed at the end of a robotic arm. The method solves the ambiguity problem – due to object symmetries or similar projections belonging to different objects – by taking a minimum number of additional views of the scene which are selected through a heuristic next best view (NBV) algorithm. The method works in reduced computational time conditions and provides identification and pose of the object. A validation test of this method has been carried out in our lab yielding excellent results.