ISPRS Commission III, Vol.34, Part 3A ,,Photogrammetric Computer Vision", Graz, 2002
Score
e
P
Lt
+
= c
opos)
e
Weis
(à
0
02 03 04 05 06 07 08 09 1 val i i : i i
Visibility 04 02 0 02 04 056 08
(a) Score vs. visibility
(b) Position errors
0.6 T T T T T 0.6
04 + i j 0.4 +
02 } > UNE 1 02r ;
4 d E
0 * ti 0 3 +
> = €
-0.2 + + + + nf. -02 + >
-04 L T ri 1 -04 }
-0.6 i L : L i -0.6 L 4 L i
-0.4 -0.2 0 0.2 0.4 0.6 0.8 -0.4 -0.2 0 0.2 0.4 0.6 0.8
(c) Pos. err. adjustment (1) (d) Pos. err. adjustment (3)
= 200 |
di
To 150
es | E |
100 |
1
Score
i
*»
++
| ^
i
i
0
02 03 04.05 06 07.08 09 1 -1
Visibility
(e) Score vs. visibility
|
00 Lia a
-100-80 -60 -40 -20 0 20 40 60 80 100 120
(f) Position errors
Figure 3: Extracted scores plotted against the visibility of the ob-
ject (a) and position errors of the extracted matches for the pro-
posed approach using extrapolation (b) and least-squares adjust-
ment with one (c) and three (d) iterations; scores vs. visibility (e)
and position errors (f) using the Hausdorff distance.
A more thorough evaluation of the proposed method, including
a comparison to a larger number of algorithms can be found in
(Ulrich and Steger, 2001).
7 CONCLUSIONS
A novel object recognition approach for industrial inspection us-
ing a new class of similarity measures that are inherently robust
against occlusion, clutter, nonlinear illumination changes, and
global as well as local contrast reversals, has been proposed. The
system is able to recognize objects under similarity transforma-
tions in video frame rate. A performance evaluation shows that
extremely high object recognition rates (more than 98% in the
test data set) are achievable. The evaluation also shows that ac-
curacies of 1/22 pixel and 1/100 degree can be achieved on real
images.
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"Pos. err. extrapolation T
Pos. err. adjustment (1)
Pos. err. ádjustment, (3)
0 5 10:-.15 20 25 30/35. 40. 45 50
(a) Position errors
0.08
004} | [4]
i A
if Vl
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