'University of Zagreb - University Computing Centre'
Abstract
This paper deals with the subject of using depth data to aid the process
of edge detection. Edge detection and stereo vision have had along and
abiding relationship, with several implementations of correspondence
algorithms depending on edge detection techniques for robustness. The
inverse however, has rarely been addressed. What if human vision, which
our discipline is attempting to model and recreate, utilizes stereoscopy
(and hence, depth information) in order to aid the process of edge
discovery, rather than the other way around?
Our goal in this paper is to investigate this hypothesis and to
establish whether the use of depth data can improve the performance of
existing edge detection techniques. More specifically, we show how we
used image distance estimates in order to improve the performance of the
ubiquitous Gaussian smoothing filter in its role as a pre processor for
various edge-detection operators, such as the one developed by Canny.
Our approach ensures that the Gaussian smoothing filter is applied in
such a way as to eliminate as much noise as possible, while preserving
adequate detail in all parts of the image. This is accomplished by the
‘layering’ of the image into multiple separate sub-images and their
subsequent independent smoothing. The layering process is guided by
depth data, which is presumed to have been derived through stereoscopy
or other means.
The images are then re-composited and analyzed for edge information by
an implementation of the Canny edge detector. Results are presented both
with and without the application of our extended smoothing preprocessor
for comparison
