Superquadric-based object modeling by an iterative segmentation-and-recovery algorithm

Hongbin Zha; Tsuyoshi Hoshide; Tsutomu Hasegawa

doi:10.1117/12.290323

26 September 1997 Superquadric-based object modeling by an iterative segmentation-and-recovery algorithm

Hongbin Zha, Tsuyoshi Hoshide, Tsutomu Hasegawa

Proceedings Volume 3208, Intelligent Robots and Computer Vision XVI: Algorithms, Techniques, Active Vision, and Materials Handling; (1997) https://doi.org/10.1117/12.290323
Event: Intelligent Systems and Advanced Manufacturing, 1997, Pittsburgh, PA, United States

Abstract

In the paper, we propose a systematic approach to object modeling by combining superquadric-fitting and segmentation into an interactive algorithm. It is assumed that the input data are a discrete description of the whole close-surface (CS) of the object, which can be acquired by range image registration and integration. Using the data as input, the method is a top-down, recursive procedure as follows: At first, it finds an initial approximation of the object by fitting a single superquadric to the whole CS data. The residual errors are examined to pick up data points locating in concave regions and far away from the fitted superquadric. A dividing plane is then extracted from the selected points to partition the original data set into two disjoint subsets, which are, respectively, approximated further by the same fitting-and-splitting process. This process is repeated until the whole data are decomposed into a number of primitive superquadrics each with a satisfactory accuracy. We present results of experiments using real range data for some complex objects.

Citation Download Citation

Hongbin Zha, Tsuyoshi Hoshide, and Tsutomu Hasegawa "Superquadric-based object modeling by an iterative segmentation-and-recovery algorithm", Proc. SPIE 3208, Intelligent Robots and Computer Vision XVI: Algorithms, Techniques, Active Vision, and Materials Handling, (26 September 1997); https://doi.org/10.1117/12.290323

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available