Object recognition and pose estimation using color cooccurrence histograms and geometric modeling

By Staffan Ekvall, Danica Kragic, Frank Hoffmann. 2005

61 citations recorded by the source corpus.

Robust techniques for object recognition and pose estimation are essential for robotic manipulation and object grasping. In this paper, a novel approach for object recognition and pose estimation based on color cooccurrence histograms and geometric modelling is presented. The particular problems addressed are: (i) robust recognition of objects in natural scenes, (ii) estimation of partial pose using an appearance based approach, and (iii) complete 6DOF model based pose estimation and tracking using geometric models. Our recognition scheme is based on the color cooccurrence histograms embedded in a classical learning framework that facilitates a 'winner-takes-all' strategy across different views and scales. The hypotheses generated in the recognition stage provide the basis for estimating the orientation of the object around the vertical axis. This prior, incomplete pose information is subsequently made precise by a technique that facilitates a geometric model of the object to estimate and continuously track the complete 6DOF pose of the object. Major contributions of the proposed system are the ability to automatically initiate an object tracking process, its robustness and invariance towards scaling and translations as well as the computational efficiency since both recognition and pose estimation rely on the same representation of the object. The performance of the system is evaluated in a domestic environment with changing lighting and background conditions on a set of everyday objects.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

Domain randomization for transferring deep neural networks from simulation to the real world (2017, methodology): “, Scale-Invariant Feature Transform [SIFT] features [14] or color co-occurrence histograms [12]) are matched with the 3D models (or features from the 3D models).”
Object recognition and full pose registration from a single image for robotic manipulation (2009, methodology): “[7], uses color co-occurrence histograms and geometric modeling to estimate the 6-DOF pose of objects.”
iCub World: Friendly Robots Help Building Good Vision Data-Sets (2013, background): “In this work, we shift our attention to the more restricted, yet challenging, world of the iCub humanoid robot [17].”