Neural net range image segmentation for object recognition

Leda Villalobos; Francis L. Merat

doi:10.1117/12.152554

2 September 1993 Neural net range image segmentation for object recognition

Leda Villalobos, Francis L. Merat

Proceedings Volume 1965, Applications of Artificial Neural Networks IV; (1993) https://doi.org/10.1117/12.152554
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

A technique for performing surface-based segmentation of range images using neural nets is introduced. In this approach, multilayered neural nets are used to classify local image patches according to the type of surface they belong to, based on features extracted from range and surface normal information. Central component to the efficiency and robustness is a near orientational invariant local data organization which takes place before features are extracted. This data organization reduces internal complexity by shifting the orientation invariance burden from the dimensionality of the feature spaces and/or from the internal architecture of the networks, to a much simpler sequencing of local data. The result is a well segmented image in which surfaces are properly labeled and delimited, without over segmentation. The approach shows to be robust in front of noise.

Citation Download Citation

Leda Villalobos and Francis L. Merat "Neural net range image segmentation for object recognition", Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); https://doi.org/10.1117/12.152554

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