Robust 3D part extraction from range images with deformable superquadric models

Yong-Lin Hu; William G. Wee

doi:10.1117/12.213046

5 July 1995 Robust 3D part extraction from range images with deformable superquadric models

Yong-Lin Hu, William G. Wee

Proceedings Volume 2484, Signal Processing, Sensor Fusion, and Target Recognition IV; (1995) https://doi.org/10.1117/12.213046
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

The extraction of 3-D geometric primitives is an important issue in model-based computer vision. The reliability of the primitives extraction is vital for further object recognition processing. In this paper, we develop a robust 3-D part extraction system. The deformable superquadrics are selected as 3-D part primitives, and a robust superquadric extraction method is developed. First, we introduce a novel adaptive weighted partial data minimization algorithm which can robustly extract superquadric from data containing both Gaussian and random noise. The convergence and the efficiency of the algorithm are discussed. The fuzzy logic techniques are introduced to further improve the algorithm to handle input containing multiple objects. Finally, a range image processing system is developed based on robust superquadric extraction method. This system can efficiently extract 3-D parts from range images. The testing results using both synthetic and real data are presented.

Citation Download Citation

Yong-Lin Hu and William G. Wee "Robust 3D part extraction from range images with deformable superquadric models", Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); https://doi.org/10.1117/12.213046

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