Model-based recognition using 3D invariants and stereo imaging

M. T. Rahman; M. S. Alam

doi:10.1117/12.666284

18 May 2006 Model-based recognition using 3D invariants and stereo imaging

M. T. Rahman, M. S. Alam

Proceedings Volume 6234, Automatic Target Recognition XVI; 62340D (2006) https://doi.org/10.1117/12.666284
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

In this paper, we proposed a three dimensional matching algorithm using geometrical invariants. Invariant relations between 3D objects and 2D images for object recognition has been already developed in Ref. [1]. We proposed a geometrical invariant approach for finding relation between 3D model and stereo image pair. Since the depth information is lost in a single 2D image, we cannot recognize an object perfectly. By constructing a 3D invariant space we can represent a 3D model as a set of points in the invariant space. While matching with the 2D image we can draw a set of invariant light rays in 3D, each ray passing through a 3D invariant model point. If enough rays intersect the model in 3D invariant space we can assume that the model is present in the image. But for a single image the method is not that much reliable as the depth information is never considered. In the proposed method, as the matching is performed using stereo image pair, it is more reliable and accurate.

Citation Download Citation

M. T. Rahman and M. S. Alam "Model-based recognition using 3D invariants and stereo imaging", Proc. SPIE 6234, Automatic Target Recognition XVI, 62340D (18 May 2006); https://doi.org/10.1117/12.666284

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