Seite - 168 - in Joint Austrian Computer Vision and Robotics Workshop 2020
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Figure 1: IQM Comparison among GS vs LS vs
GS+LS
cessed imagesdecreases.
Table 1displays IQM values for theCA.
Patch Sizes PSNR VSNR UQI SSIM
3x3 Patch 64.80 32.78 0.99 0.99
5x5 Patch 51.95 32.23 0.99 0.99
7x7 Patch 47.10 47.58 0.99 0.99
9x9 Patch 42.86 40.89 0.98 0.99
Table1: IQM forCA.
For UQI as well as SSIM we notice almost no
qualitydegradationbytheCA,nomatterwhichpatch
size is being used. For PSNR, CA is superior to all
GS+LS variants and for almost all other settings ex-
cept for extremely low smoothing strength. Finally,
for VSNR, CA is again superior to all GS+LS vari-
ants and for all other techniques but LS with patch-
size 3 for low smoothing strength. Overall, the qual-
ityobtainedwith theCAisverygood,andonlycom-
parable to LS with patchsize 3, however, with all
patchsizesconsidered.
But how effective are the smoothing-based meth-
ods in actually removing keypoints ? Contrasting to
CA, in which all present keypoints are replaced by
keypoint-freepatches, smoothingdoesnotguarantee
that keypoints are actually removed. Fig. 2 illus-
trates the percentage of original keypoints which are still present after smoothing for increasing smooth-
ingstrength.
100
80
60
40
20
0
0 1 2 3 4 5
Strength of Smoothing GS
LS(3x3)
GS+LS(3x3)
(a)PatchSize3×3 100
80
60
40
20
0
0 1 2 3 4 5
Strength of Smoothing GS
LS(5x5)
GS+LS(5x5)
(b) PatchSize5×5
100
80
60
40
20
0
0 1 2 3 4 5
Strength of Smoothing GS
LS(7x7)
GS+LS(7x7)
(c)PatchSize7×7 100
80
60
40
20
0
0 1 2 3 4 5
Strength of Smoothing GS
LS(9x9)
GS+LS(9x9)
(d) PatchSize9×9
Figure 2: Share of retained keypoints: GS vs LS vs
GS+LS
For larger patch sizes, GS and LS perform al-
most identically (which is clear considering the def-
inition), while GS+LS is most effective in removing
keypoints. For smaller patch sizes, GS is most ef-
fective for high smoothing strength, while GS+LS is
best for low smoothing strength. LS is not very ef-
fectiveunder theseconditions.
When applying techniques for keypoint removal,
new keypoints are being created, e.g. at the edge of
the patches in CA, LS, and GS+LS. This is not de-
sired, as these new keypoints might match to exist-
ing ones and thus aid the forensic analyst. Fig. 3
illustrates the creation of new, additional keypoints
by showing the percentage of newly created ones.
LS clearly introduces the lowest number of addi-
tional keypoints, and if the size of the smoothing
patch is increased then also the number of new key-
points isalso increased. Thesmoothingstrengthalso
plays a certain role: For weak smoothing, increasing
the strength leads to more new keypoints, while af-
ter reaching a peak, a further increase of smoothing
strength decreases the number of newly created key-
points. This effect is expected and most obvious for
GS.
In Table 2, the percentage of newly created key-
points for CA is shown. Only LS with patchsize 3
givesbetter results, forallother techniqueswenotice
higher percentages of newly created keypoints when
comparingFig. 3 to thevalues inTable2.
168
Joint Austrian Computer Vision and Robotics Workshop 2020
- Titel
- Joint Austrian Computer Vision and Robotics Workshop 2020
- Herausgeber
- Graz University of Technology
- Ort
- Graz
- Datum
- 2020
- Sprache
- englisch
- Lizenz
- CC BY 4.0
- ISBN
- 978-3-85125-752-6
- Abmessungen
- 21.0 x 29.7 cm
- Seiten
- 188
- Kategorien
- Informatik
- Technik