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Title
Close-range imaging, long-range vision


Title: A fast self-organized iconic segmentation and grouping based in color
Javier Finat, Margarita Gonzalo-Tasis and Alejandro Viloria.
Javier Finat, Dept of Algebra and Geometry, ETS Computer Science Eng, Univ of Valladolid, 47011 Valladolid,
Spain, e-mail: jfinat@agt.uva.es
Keywords: Vision Sciences, Representation, Interfaces, Color, Visualization, Motion
Abstract
Color is a powerful tool for segmentation, recognition and tracking of mobile objects. In this work we address the clustering problem
to the iconic level for the static and dynamic case. After thresholding, averaged vector sums simplify the grouping of regions around
typical color values. Our iterative algorithm follows a spatial propagation scheme inspired on a WTA variant of a Self-Organizing
Map; so, we obtain a segmentation linear in the number of regions. The user can fix this number in advance too. The linear character
allows us to apply this approach to obtain a coarse depth map and a mobile segmentation at iconic level without extracting geometric
characteristics of boundaries. Nevertheless, our procedure compares merged regions located at successive images of a movie-file
taken near in time. This comparison 1) gives an optimal simplicial Delaunay decomposition in the color space, 2) evaluates some
characteristics of the movement, 3) discriminates between egomotion and movement of some object in the scene, and 4) captures
iluminance variations as color variation
1. Introduction
Color phenomena arise from an interaction of light with matter.
Low-level vision based in color gives us a local
homogeneization for a vector representation of RGB or IHS-
color associated to the matrix of intensity functions.
Segmentation based in homogeneous color regions ([Oht80])
simplifies the tracking through sequences of images ([Fer92],
[Hea92]). Geometric aspects linked to the relative orientation
would must be as independent as possible of the illumination
conditions, but they are sensitive to shadows and reflections;
furthermore, they are blurred by global filters which degrade the
quality and continuity of boundaries. Luminance and IHS-color
properties give us a more realistic feeling, because Hue and
Saturation are less sensitive to illumination variations; in fact,
they are invariant to surface curvature and lighting conditions
([Hun95]).
Here, we present a bottom-up strategy to segmentation
problems. Our goal is to avoid as much as possible any
dependence w.r.. the real scene and the illuminationo.
Traditionally, a bottom-up approach to the image segmentation
includes the smoothing processes, chains of neighbour points,
connectivity detection, correction of aliasing effects, matching
the segment model in a database to generate the geometrical
model of the scene and to complete hidden zones. The color
segmentation uses color to separate objects without a priori
knowledge about specific surfaces. Inversely, the color
recognition attempts to recognize colors of known color
characteristics. Thus, the color segmentation seems more
appropriate for a bottom-up approach.
There are different approaches to the color segmentation
depending on geometrically- or physically-based methods.
Geometric methods are classified after the selected dimension
for grouping criteria OD or pixel level ([Che98]), 1D or edge
level ([Wu93]), and 2D or region level ([Gau99]). Furthermore,
there are hybrid approaches which are successful for complex
images ([Shi99]). Our hybrid strategy intends to integrate all
geometric levels of the analysis in a common data structure with
optimal and easily updatable properties. To achieve this gaol,
we have chosen a classical approach linked to the
superimposition of an automatically generated geometric
construction based in Delaunay decompositions as an optimal
support for color configurations. Optimal properties of
Delaunay decompositions are well-known ([Ede92]), but
seemingly this viewpoint has not been considered before.
2. Computational Background
There are several algorithms to retrieve and cluster regions
inside a image, but usual algorithms are of recursive nature, and
they represent a high comptuational cost, often. A region can be
defined as a set of adjacent pixels with similar color. This
region is labelled as a unit, with a localization (position-
orientation for its centroid), and an averaged region color in
some vector representation. All pixels belonging to a particular
region are labelled with the same value. There are no different
regions characterized with the same label.
The output of a low-level geometrical aproximation is given by
a collection of segments which must be grouped to obtain
boundaries and contours of the objects represented in the image.
Inversely, if regions are retrieved easily, the contour model can
be generated in a faster way. Often, it is not necessary to have a
precise mathematical knowledge of contours appearing at
images; it suffices an iconic information. Alternately, we can
use a clustered region model directly, instead of using a contour
model. In this case, the computational cost is lower, because we
don’t need to have precise information about the mathematical
primitives and their projections. We have chosen the second
approach to avoid the computational cost of boundaries
extraction in complex images and to use the discrimination
power of color, as in the human case.
Usual approaches are based on a two-step recursive (to identify
the averaged color) and iterative (for regions labelling)
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