Digital Signal Processing Reference
In-Depth Information
assumptions are more powerful,
but require the preprocessing to
supply
the
applicable
model
and
/
or
partial
object
information.
E
IMAGE
E
image
expresses
the surface attraction
toward
visual
cues
that
mark
the video object boundary in a given isolated frame.
A general form of
E
image
is shown below:
∫
E
image
(
S
(
*
,t'
)
) =
S
(*,t')
- R
(
1
(
x, y, t
) |
t=t'
,
s
)
ds
→
(4.5)
where the function
R
returns the likelihood that an video object bound-
ary exists at a given position
s
from the intensities of the frame
t'.
In
→
our system,
E
image
is composed of two analyses:
edge detection, the core
technology that
finds spatial image discontinuities, and Viterbi Rings,
a
refinement for edge detection that
uses edge connectivity.
1.
Edge Detection
The primary cue for object boundary detection are edges. A surface
that lies upon many edges is likely to be a good object boundary esti-
mate. In our system, a Canny edge filter with a built-in edge follower
suffices for grayscale images [Canny, 1986], although future work can
generalize the concept of an edge to include color and texture.
2.
Viterbi Rings
Since all edges derived from edge detection do not necessarily be-
long to an object boundary, further analysis can distinguish between
edges that are on the object boundary and those that are not. Given
a known object center estimate, edges on object boundary tend to
belong to a closed contour that go around the object center and to
be oriented such that they face to the object center. We call this sub-
set of edges with these properties,
Viterbi Rings.
However, we shall
defer the determination of Viterbi Rings to the implementation (see
Section 4.13) since its analysis also introduces important concepts for
our surface optimization.
E
MOTION
E
motion
expresses the attraction of the surface to discontinuities in the
motion field. The surface
S
has a lower energy when it lies upon many
motion discontinuities.
E
motion
has a general form in Eq. 4.6 below:
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