Performance evaluation of a class of M-estimators for surface parameter estimation in noisy range data

Muhammad Javed Mirza; Kim L. Boyer

doi:10.1117/12.58573

1 March 1992 Performance evaluation of a class of M-estimators for surface parameter estimation in noisy range data

Muhammad Javed Mirza, Kim L. Boyer

Proceedings Volume 1708, Applications of Artificial Intelligence X: Machine Vision and Robotics; (1992) https://doi.org/10.1117/12.58573
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

Depth maps are frequently analyzed as if, to an adequate approximation, the errors are normally, identically, and independently distributed. This noise model does not consider at least two types of anomalies encountered in sampling: A few large deviations in the data, often thought of as outliers; and a uniformly distributed error component arising from rounding and quantization. The theory of robust statistics formally addresses these problems and is efficiently used in a robust sequential estimator (RSE) of our own design. The specific implementation was based on a t-distribution error model, and this work extends this concept to several well known M-estimators. We evaluate the performance of these estimators under different noise conditions and highlight the effects of tuning constants and the necessity of simultaneous scale and parameter estimation.

Citation Download Citation

Muhammad Javed Mirza and Kim L. Boyer "Performance evaluation of a class of M-estimators for surface parameter estimation in noisy range data", Proc. SPIE 1708, Applications of Artificial Intelligence X: Machine Vision and Robotics, (1 March 1992); https://doi.org/10.1117/12.58573

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