Transcript 슬라이드 1 - SEJONG

Trellis-based Parallel Stereo Matching
2007. 4. 9.
Media Processor Lab. Sejong univ.
E-mail : [email protected]
Dong-seok Kim
Media Processor Lab.
Contents







Introduction
Stereo Vision Model
Center-referenced space
Constraints on disparity
Estimating optimal disparity
Experimental results
Conclusion
Media Processor Lab.
# 2.
Introduction


Stereo vision is an inverse process that
attempts to restore the original scene from a
pair of images.
In this paper a new basis for disparity based
on center-referenced coordinates is presented
that is concise and complete in terms of
constraint representation.
Media Processor Lab.
# 3.
Stereo Vision Model (1)

Projection Model
 Assumption : coplanar image planes, parallel optical axes,
equal focal lengths l, and matching epipolar lines
 The inverse match space I is the finite set of points,
represented by solid dots, that are reconstructable by
matching image pixels.
 Left image scan line : fl = [fl1 ··· flN]
 Right image scan line : fr = [fr1 ··· frN]
Media Processor Lab.
# 4.
Stereo Vision Model (2)

Representation of Correspondence
 Each element of each scan line can


have a corresponding element in the other image scan line,
denoted (fli, frj)
be occluded in the other image scan line, denoted (fli, Ø) for a left
image element (right occlusion) and (Ø, fri) for a right image
element (left occlusion)
 Left-referenced disparity map : dl = [dl1 ··· dlN]

Disparity value : dli ⇔ (fli, fri+dli)
 Right-referenced disparity map : dr = [dr1 ··· drN]

Disparity value : drj ⇔ (fli+drj ,frj)
.
Media Processor Lab.
# 5.
Center-referenced space (1)


Using only left- or right-referenced disparity, it is
difficult to represent common matching constraints
with respect to both images.
An alternate center-referenced projection
 The focal point pc located at the midpoint between the
focal points for the left and right image plane
 Plane with 2N + 1 and focal length of 2l
 The projection lines intersect with the horizontal isodisparity lines forms the inverse space D.
Media Processor Lab.
# 6.
Center-referenced space (2)

Center-referenced disparity vector d = [d0 ··· d2N]
 disparity value di indicates the depth index of a real world
point along the projection line from site i on the center
image plane
 If di is a match point : (fl(i – dj + 1)/2 , fr(i + dj + 1)/2)
 (fli , frj) is denoted by the disparity di + j – 1 = j – i
 The odd function o(x) is
used to indicate if di is a
match point, that is
o(i + di) = 1
when di is a match point.
Media Processor Lab.
# 7.
Center-referenced space (3)

Represent occlusions by
assigning the highest
possible disparity (Fig. 3)

The correspondence
(fl5 , fr8) creates a right
occlusion for which the real object could lie anywhere in the
triangular Right Occlusion Region (ROR)
If only I is used, then the match points (solid dots) in the ROR
are used.
D contains additional occlusion points (open dots) in the ROR
that are further to the right, which are used instead.


Media Processor Lab.
# 8.
Constraints on disparity




Parallel axes : di ≥ 0
Endpoints : d0 = d2N = 0
Cohesiveness : di – di – 1 ∈ {–1, 0, 1}
Uniqueness : o(i + di) = 1⇒ di–1 = di = di+1
Media Processor Lab.
# 9.
Estimating optimal disparity

DP techniques progressing through the trellis from left to right (site i = 0,
…, 2N). In recursive form, the shortest path algorithm for disparity is
formally given by:

Initialization : Endpoint has zero disparity

Recursion : At each site i = 1, … 2N, find the best path into each node j. if i+j
is even,
otherwise

Termination : i = 2N and j = 0.

Reconstruction : Backtrack the decisions.
.
Media Processor Lab.
# 10.
Experimental results
Media Processor Lab.
# 11.
Conclusion



We have used a center-referenced projection
to represent the discrete inverse space for
stereo correspondence.
This space D contains additional occlusion
points which we exploit to create a concise
representation of correspondence and
occlusion.
The algorithm was tested on both real and
synthetic image pairs with good results.
Media Processor Lab.
# 12.