Proceedings ArticleDOI
Analysis of the least median of squares estimator for computer vision applications
D. Mintz,Peter Meer,Azriel Rosenfeld +2 more
- pp 621-623
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TLDR
It is shown that in the presence of significant noise, LMedS loses its high breakdown point property, and a different, two-stage approach in which the uncertainty due to noise is reduced before applying the simplest L medS procedure is proposed.Abstract:
The robust least-median-of-squares (LMedS) estimator, which can recover a model representing only half the data points, was recently introduced in computer vision. Image data, however, is usually also corrupted by a zero-mean random process (noise) accounting for the measurement uncertainties. It is shown that in the presence of significant noise, LMedS loses its high breakdown point property. A different, two-stage approach in which the uncertainty due to noise is reduced before applying the simplest LMedS procedure is proposed. The superior performance of the technique is proved by comparative graphs. >read more
Citations
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Journal ArticleDOI
Robust estimator for non-line-of-sight error mitigation in indoor localization
TL;DR: It is shown how the least-median-of-squares technique can be used to overcome the effects of NLOS errors, even in environments with little infrastructure, and its suitability is validated by comparing it to other methods described in the bibliography.
Proceedings ArticleDOI
Learning accurate kinematic control of cable-driven surgical robots using data cleaning and Gaussian Process Regression
Jeffrey Mahler,Sanjay Krishnan,Michael Laskey,Siddarth Sen,Adithyavairavan Murali,Ben Kehoe,Sachin Patil,Jiannan Wang,Michael J. Franklin,Pieter Abbeel,Ken Goldberg +10 more
TL;DR: This paper extends the use of GPR to learn a non-linear correction for cable-driven surgical robots by using velocity as a feature in the regression and removing corrupted training observations based on rotation limits and the magnitude of velocity.
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Hidden Issues in Deploying an Indoor Location System
TL;DR: Installing indoor location system prototypes yields practical lessons about how to design and deploy future ubiquitous technologies.
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Stochastic Approximation and Rate-Distortion Analysis for Robust Structure and Motion Estimation
TL;DR: This paper proposes a framework for 3D reconstruction from short monocular video sequences taking into account the statistical errors in reconstruction algorithms, and derives a precise relationship between the error in the reconstruction and theerror in the image correspondences.
Journal ArticleDOI
A new global alignment approach for underwater optical mapping
TL;DR: A new global alignment method which works on the mosaic frame and does not require non-linear optimisation is presented, and a simple image rectifying method is presented to reduce the down-scaling effect which might occur when minimising errors defined in the mosaicing frame.
References
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Journal ArticleDOI
Robust regression methods for computer vision: a review
TL;DR: The least-median-of-squares (LMedS) method, which yields the correct result even when half of the data is severely corrupted, is described and compared with the class of robust M-estimators.
Proceedings ArticleDOI
Robust consensus based edge detection
TL;DR: A robust algorithm for edge detection is presented that detects both roof- and step-type edges and allows the transformation of any window possibly containing a discontinuity to a binary window containing a step edge in the location of the discontinuity.