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A Multi-Resolution Technique for Comparing Images Using the Hausdorff Distance. Daniel P. Huttenlocher and William J. Rucklidge Nov 7 2000 Presented by Chang Ha Lee. Introduction. Registration methods Correlation and sequential methods Fourier methods Point mapping - PowerPoint PPT Presentation
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A Multi-Resolution Technique for Comparing Images Using the Hausdorff Distance
Daniel P. Huttenlocher and William J. RucklidgeNov 7 2000
Presented by Chang Ha Lee
Introduction Registration methods
Correlation and sequential methods Fourier methods Point mapping Elastic model-based matching
The Hausdorff Distance Given two finite point sets A = {a1,
…,ap} and B = {b1,…,bq}
H(A, B) = max(h(A, B), h(B, A))where
baBAhBbAa
minmax),(
With transformations A: a set of image points B: a set of model points transformations: translations, scales
t = (tx, ty, sx, sy) w = (wx, wy) => t(w) = (sxwx+tx, sywy+ty) f(t) = H(A, t(B))
With transformations Forward distance
fB(t) = h(t(B), A) a hypothesize
reverse distance fA(t) = h(A, t(B)) a test method
Computing Hausdorff distance Voronoi surface d(x) = {(x, d(x))|xR2}
xaxdbxxdAaBb
min)(',min)(
))('max),(maxmax(),( bdadBAHBbAa
Computing Hausdorff distance The forward distance for a
transformation t = (tx, ty, sx, sy)
),('max
),(minmax
)(minmax)),(()(
yyyxxxBb
yyyxxxAaBb
AaBbB
tbstbsd
tbstbsa
btaABthtf
Comparing Portion of Shapes Partial distance
The partial bidirectional Hausdorff distance HLK(A,t(B))=max(hL(A,t(B)), hK(t(B),A))
baKABthAaBtb
thK
min)),(()(
A Multi-Resolution Approach Claim 1.
0bxxmax, 0byymax for all b=(bx,by)B,t = (tx, ty, sx, sy),
)'',''()',( maxmax yyyyxxxx ttyssttxsstt
)',(' tt
A Multi-Resolution Approach If HLK(A, t(B)) = >, then any transformation t’
with (t,t’) < - cannot have HLK(A, t’(B)) A rectilinear region(cell)
The center of this cell
If then no transformation t’R have HLK(A, t’(B))
],[],[],[],[ highy
lowy
highx
lowx
highy
lowy
highx
lowx ssssttttR
))(2/)(),(2/)((
))(,(
maxmaxlowy
highy
lowy
highy
lowx
highx
lowx
highx
cLK
ttssyttssx
BtAH
)2/)(,2/)(,2/)(,2/)(( highy
lowy
highx
lowx
highy
lowy
highx
lowxc ssssttttt
A Multi-Resolution Approach1. Start with a rectilinear region(cell) which
contains all transformations2. For each cell,
If
Mark this cell as interesting3. Make a new list of cells that cover all
interesting cells.4. Repeat 2,3 until the cell size becomes small
enough
))(2/)(),(2/)((
))(,(
maxmaxlowy
highy
lowy
highy
lowx
highx
lowx
highx
cLK
ttssyttssx
BtAH
The Hausdorff distance for grid points A = {a1,…,ap} and B = {b1,…,bq} where
each point aA, bB has integer coordinates The set A is represented by a binary array
A[k,l] where the k,l-th entry is nonzero when the point (k,l) A
The distance transform of the image set A D’[x,y] is zero when A[k,l] is nonzero, Other locations of D’[x,y] specify the distance to
the nearest nonzero point
Rasterizing transformation space Translations
An accuracy of one pixel Scales
b=(bx,by) B, 0bxxmax, 0byymax x-scale: an accuracy of 1/xmax y-scale: an accuracy of 1/ymax Lower limits: sxmin , symin Ratio limits: sx/sy > amax or sy/sx > amax
Transformations can be represented by (ix, iy, jx, jy) represents the transformation (ix, iy, jx /xmax, jy /ymax)
Rasterizing transformation space Restrictions where A[k,l], 0kma, 0lna
yay
xax
y
x
yy
xx
jnijmi
xay
jj
axy
yjys
xjxs
00
max
maxmax
maxmax
max
maxmaxmin
maxmaxmin
Rasterizing transformation space Forward distance hK(t(B),A)
Bidirectional distance
]/,/['],,,[ maxmax yyyxxxBb
thyxyxB iybjixbjDKjjiiF
]),,,[],,,,[max(],,,[ yxyxByxyxAyxyx jjiiFjjiiFjjiiF
Reverse distances in cluttered images
],['],[],,,[),(
lkDlkALjjiiF
yyyxxxjilijiki
lk
thyxyxA
When the model is considerably smaller than the image Compute a partial reverse distance only for
image points near the current position of the model
Increasing the efficiency of the computation Early rejection
K = f1q where q = |B| If the number of probe values greater than the
threshold exceeds q-K, then stop for the current cell Early Acceptance
Accept “false positive” and no “false negative” A fraction s, 0<s<1 of the points of B When s=.2
10% of cells labeled as interesting actually are shown to be uninteresting
Increasing the efficiency of the computation Skipping forward
Relies on the order of cell scanning Assume that cells are scanned in the x-scale order. D’+x[x,y]: the distance in the increasing x direction to
the nearest location where D’[x,y]’ Computing D’+x[x,y]: Scan right-to-left along each
row of D’[x,y]
FB[ix,iy,jx,jy],…,FB[ix,iy,jx+-1,jy] must be greater than ’
)1]/,/[',0max( maxmaxmax yyyxxxxx
x iybjixbjDbx
Examples Camera images
Edge detection
Examples Engineering drawing
Find circles
Conclusion A multi-resolution method for searching
possible transformations of a model with respect to an image
Problem domain Two dimensional images which are taken of
an object in the 3-dimensional world Engineering drawings