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CSE576 (Winter 2008) Project #1=
o:p>
Woon Jong Yoon (4/17/2008)
Detecting interest points /Feature
Descriptor
I used the Harris corner measure discussed in lecture to detect interest
points. The Harris corner measure is computed as determinant divided by tra=
ce of
the Harris matrix. All points for which this measure is greater than some
threshold are identified. My first and only feature descriptor is a simple =
5 X
5 window descriptor. For each of the points of interest, I stored the value=
s of
the pixels in the 5 x 5 window with this point as the center.
Based on careful observation, a threshold of 0.5 is observed as a good
number compared to the state of the art technique, SIFT. See Figure 1. Also=
the
bike example shows how feature detection is working on different viewpoint
pictures (Figure 3)
<=
span
style=3D'mso-spacerun:yes'> <=
span
style=3D'font-size:10.0pt'> Figure 1. The results of feature detection (Left) wind=
ow
descriptor with threshold 0.5 (Rigtht) SIFT.
<=
span
style=3D'mso-spacerun:yes'> <=
span
style=3D'font-size:10.0pt'>
Figure 2. After Harris and HarrisMax=
operation of yosemite1.jpg
<=
!--[if gte vml 1]>
<=
/p>
Figure 3. Some more results of feature detect=
ion with
translational change (Threshold=3D0.5)
Matching features
There are two feature match algorithms are used. First=
, This just calculate the Euclidean distance betwe=
en two
feature descriptors, and matches a feature in the first image with the clos=
est
feature in the second image.Second one is to Use Euclidean distance =
to
calculate the distance between two feature descriptor and then apply a
threshold on the ratio of the best distance matched to the second best dist=
ance
matched. This matching can match multiple
features in the first image to the same feature in the second image.
Currently the threshold is set at 0.5 and this means if the second best
distance method is at least twice as much as the first distance then that's
good enough for a match.
Benchmark tests
Average pixel error/av=
erage
AUC (both used the simple window descriptor):
Benchmark Set
|
SSD matching
|
Ratio matching
|
bikes
|
527/0.26
|
528/0.007
|
leuven
|
371/0.400
|
371/0.0002
|
Based on the benchmark results, both matching methods show similar resul=
ts
with the simple window descriptor. Strangely, wall and giraffe pictures
didn’t’ work with the benchmark test. It is recommenced that I
would rather try a better descriptor to compare the average pixel error and=
AUC
to conclude that which descriptor performs considerably better.
ROC curves
Gnuplot tool didn’t work for m=
e. I
will leave this as a future work.
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