Region segmentation based on 3D geometry from range images

Dongming Zhao; Jenny J. Chen; Xingjun Wang; Sean X. Zhang

doi:10.1117/12.285561

18 September 1997 Region segmentation based on 3D geometry from range images

Dongming Zhao, Jenny J. Chen, Xingjun Wang, Sean X. Zhang

Proceedings Volume 3205, Machine Vision Applications, Architectures, and Systems Integration VI; (1997) https://doi.org/10.1117/12.285561
Event: Intelligent Systems and Advanced Manufacturing, 1997, Pittsburgh, PA, United States

Abstract

Region segmentation provides a valid information check point and background knowledge for higher level machine vision tasks that rely on the fundamental image processing and analysis results such as segmentations. Our study of segmenting regions of interest on range images shows that knowing local surface geometries becomes the key to successfully solving segmentation problems. In this paper, three topics are presented and the experiments are shown: (1) segmentation based on morphological watershed algorithm; (2) a multiscale approach using morphology; and (3) a segmentation method based on surface normals. The examples used in this study are from the range images taken by a laser radar imager. The applications are focused in the area of manufacturing automation and inspection.

Citation Download Citation

Dongming Zhao, Jenny J. Chen, Xingjun Wang, and Sean X. Zhang "Region segmentation based on 3D geometry from range images", Proc. SPIE 3205, Machine Vision Applications, Architectures, and Systems Integration VI, (18 September 1997); https://doi.org/10.1117/12.285561

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