Retinex Image Processing--Publications--SPIE 6978, Orlando, Florida (2008)
Conference
Adaptive Methods of Two-Scale Edge Detection in Post-Enhancement Visual Pattern Processing
Zia-ur Rahman, Daniel J. Jobson, Glenn A. Woodell
Abstract
Adaptive methods are defined and experimentally studied for a two-scale
edge detection process that mimics human visual perception of edges and
is inspired by the parvo-cellular (P) and magno-cellular (M)
physiological subsystems of natural vision. This two-channel processing
consists of a high spatial acuity/coarse contrast channel (P) and a
coarse acuity/fine contrast (M) channel. We perform edge detection
after a very strong non-linear image enhancement that uses smart Retinex
image processing. Two conditions that arise from this enhancement
demand adaptiveness in edge detection. These conditions are the
presence of random noise further exacerbated by the enhancement process,
and the equally random occurrence of dense textural visual information.
We examine how to best deal with both phenomena with an automatic
adaptive computation that treats both high noise and dense textures as
too much information, and gracefully shifts from a small-scale to
medium-scale edge pattern priorities. This shift is accomplished by
using different edge-enhancement schemes that correspond with the (P)
and (M) channels of the human visual system. We also examine the case
of adapting to a third image condition, namely too little visual
information, and automatically adjust edge detection sensitivities when
sparse feature information is encountered. When this methodology is
applied to a sequence of images of the same scene but with varying
exposures and lighting conditions, this edge-detection process produces
pattern constancy that is very useful for several imaging applications
that rely on image classification in variable imaging conditions.
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