Improving reconstruction of man-made objects from sensor images by machine learning

Roman Englert; Armin B. Cremers

doi:10.1117/12.281046

5 August 1997 Improving reconstruction of man-made objects from sensor images by machine learning

Roman Englert, Armin B. Cremers

Proceedings Volume 3072, Integrating Photogrammetric Techniques with Scene Analysis and Machine Vision III; (1997) https://doi.org/10.1117/12.281046
Event: AeroSense '97, 1997, Orlando, FL, United States

Abstract

In this paper we present a new approach for the acquisition and analysis of background knowledge which is used for 3D reconstruction of man-made objects -- in this case buildings. Buildings can be easily represented as parameterized graphs from which p-subisomorphic graphs will be computed. P-graphs will be defined and an upper bound complexity estimation of the computation of p-subisomorphims will be given. In order to reduce search space we will discuss several pruning mechanisms. Background knowledge requires a classification in order to receive a probability distribution which will serve as a priori knowledge for 3D building reconstruction. Therefore, we will apply an alternative view of nearest- neighbor classification to measured knowledge in order to learn based on a complete seed and a noise model a distribution of this knowledge. An application of an extensive scene consisting of 1846 building cluster which are represented as p-graphs in order to estimate a probability distribution of corner nodes demonstrates the effectiveness of our approach. An evaluation using the information coding theory determines the information gain which is provided by the estimated distribution in comparison with no available a priori knowledge.

Citation Download Citation

Roman Englert and Armin B. Cremers "Improving reconstruction of man-made objects from sensor images by machine learning", Proc. SPIE 3072, Integrating Photogrammetric Techniques with Scene Analysis and Machine Vision III, (5 August 1997); https://doi.org/10.1117/12.281046

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