Authors:
Severino Gomes-Neto
and
Bruno M. Carvalho
Affiliation:
UFRN, Brazil
Keyword(s):
Preemptive RANSAC, Generalized Preemptive RANSAC, Preemption Function, BRUMA, Computer Vision.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Robotics
;
Software Engineering
;
Stereo Vision and Structure from Motion
Abstract:
This paper examines a generalized version of Preemptive RANSAC for visual motion estimation. The approach
described employs the BRUMA function for dealing with varying block sizes and the percentages
of hypotheses to be removed during the hypotheses rejection phase. The generation of a flexible number of
hypotheses is also performed in order to balance the preemption scheme. Experiments were performed for
both forward and side-wise motions in synthetic environment by using simulation and the ground-truth used
to compare the Standard Preemptive RANSAC and its generalized version. Simulations confirmed that the
quality of the results produced by the Standard Preemptive RANSAC degrade as the hardware resources used
are decreased, as opposed to the results produced by the Generalized Preemptive RANSAC, with the results
of the Standard Preemptive RANSAC having errors up to eleven times larger than the Generalized Preemptive
RANSAC.