© The Institution of Engineering and Technology
This study describes a corner selection strategy based on the Harris approach. Corners are usually defined as interest points for which intensity variation in the principal directions is locally maximised, as response from a filter given by the linear combination of the determinant and the trace of the autocorrelation matrix. The Harris corner detector, in its original definition, is only rotationally invariant, but scale-invariant and affine-covariant extensions have been developed. As one of the main drawbacks, corner detector performances are influenced by two user-given parameters: the linear combination coefficient and the response filter threshold. The main idea of the authors' approach is to search only the corners near enhanced edges and, by a z-score normalisation, to avoid the introduction of the linear combination coefficient. Combining these strategies allows a fine and stable corner selection without tuning the method. The new detector has been compared with other state-of-the-art detectors on the standard Oxford data set, achieving good results showing the validity of the approach. Analogous results have been obtained using the local detector evaluation framework on non-planar scenes by Fraundorfer and Bischof.
References
-
-
1)
-
Parida, L., Geiger, D., Hummel, R.A.: `Kona – a multi-junction detector using minimum description length principle', First Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, 1997, p. 51–65.
-
2)
-
Lowe, D.G.: `Object recognition from local scale-invariant features', Int. Conf. on Computer Vision, 1999, p. 1150–1157.
-
3)
-
T. Lindeberg
.
(1994)
Scale-space theory in computer vision.
-
4)
-
Shi, J., Tomasi, C.: `Good features to track', IEEE Conf. on Computer Vision and Pattern Recognition, 1994, p. 593–600.
-
5)
-
Schaffalitzky, F., Zisserman, A.: `Multi-view matching for unordered image sets, or “How do I organize my holiday snaps?” ', European Conf. on Computer Vision, 2002, p. 414–431.
-
6)
-
Beaudet, P.R.: `Rotationally invariant image operators', Int. Joint Conf. on Pattern Recognition, 1978, p. 578–583.
-
7)
-
Dickscheid, T., Förstner, W.: `Evaluating the suitability of feature detectors for automatic image orientation systems', Int. Conf. on Computer Vision Systems, 2009, Liege, Belgium.
-
8)
-
Crowley, J.L., Riff, O., Piater, J.: `Fast computation of characteristic scale using a half octave pyramid', Int. Workshop on Cognitive Computing, 2002.
-
9)
-
K. Mikolajczyk ,
T. Tuytelaars ,
C. Schmid ,
A. Zisserman ,
J. Matas ,
F. Schaffslatzky ,
T. Kadir ,
V.L. Gool
.
A comparison of affine region detectors.
Int. J. Comput. Vis.
,
1 ,
43 -
72
-
10)
-
T. Tuytelaars ,
L.V. Gool
.
Matching widely separated views based on affine invariant regions.
Int. J. Comput. Vis.
,
1 ,
61 -
85
-
11)
-
J. Bigün
.
A structure feature for some image processing applications based on spiral functions.
Comput. Vis. Graph. Image Process.
,
2 ,
166 -
194
-
12)
-
W. Förstner
.
A feature-based correspondence algorithm for image matching.
Int. Arch. Photogram. Remote Sens.
,
150 -
166
-
13)
-
Baumberg, A.: `Reliable feature matching across widely separated views', IEEE Conf. on Computer Vision and Pattern Recognition, 2000, p. 774–781.
-
14)
-
Kenney, C.S., Zuliani, M., Manjunath, B.S.: `An axiomatic approach to corner detection', IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2005, p. 191–197.
-
15)
-
M. Trajkovic ,
M. Hedley
.
Fast corner detection.
Image Vis. Comput.
,
2 ,
75 -
87
-
16)
-
H. Moravec
.
(1980)
Obstacle avoidance and navigation in the real world by a seeing robot rover.
-
17)
-
T. Lindeberg
.
Feature detection with automatic scale selection.
Int. J. Comput. Vis.
,
2 ,
79 -
116
-
18)
-
Moreels, P., Perona, P.: `Evaluation of features detectors and descriptors based on 3d objects', Int. Conf. on Computer Vision, 2005, p. 800–807.
-
19)
-
Kadir, T., Zisserman, A., Brady, M.: `An affine invariant salient region detector', European Conf. on Computer Vision, 2004, p. 345–457.
-
20)
-
W.T. Freeman ,
E.H. Adelson
.
The design and use of steerable filters.
IEEE Trans. Pattern Anal. Mach. Intell.
,
891 -
906
-
21)
-
K. Mikolajczyk ,
T. Tuytelaars ,
C. Schmid
.
Affine Covariant Features.
-
22)
-
Matas, J., Chum, O., Urban, M., Pajdla, T.: `Robust wide baseline stereo from maximally stable extremal regions', British Machine Vision Conf., 2002, p. 384–393.
-
23)
-
R. Sedgewick
.
(1990)
Algorithms in C.
-
24)
-
Förstner, W., Dickscheid, T., Schindler, F.: `Detecting interpretable and accurate scale-invariant keypoints', Int. Conf. on Computer Vision, 2009, Kyoto, Japan.
-
25)
-
Brown, M., Lowe, D.G.: `Recognizing panoramas', Int. Conf. on Computer Vision, 2003, p. 1218–1225.
-
26)
-
D.G. Lowe
.
Distinctive image features from scale-invariant keypoints.
Int. J. Comput. Vis
,
2 ,
91 -
110
-
27)
-
Klinger, A.: `Patterns and search statistics', Optimizing Methods in Statistics, 1971, p. 303–339.
-
28)
-
J. Koenderink
.
The structure of images.
Biol. Cybern.
,
363 -
370
-
29)
-
S.M. Smith ,
J.M. Brady
.
SUSAN: a new approach to low level image processing.
Int. J. Comput. Vis.
,
1 ,
45 -
78
-
30)
-
K. Mikolajczyk ,
C. Schmid
.
Scale & affine invariant interest point detectors.
Int. J. Comput. Vis.
,
1 ,
63 -
86
-
31)
-
R. Hartley
.
(2003)
Multiple view geometry in computer vision.
-
32)
-
Harris, C., Stephens, M.: `A combined corner and edge detector', Alvey Vision Conf., 1988, p. 147–151.
-
33)
-
Lindeberg, T.: `Junction detection with automatic selection of detection scales and localization scales', First Int. Conf. on Image Processing, 1994, p. 924–928.
-
34)
-
K. Mikolajczyk
.
(2002)
Detection of local features invariant to affines transformations.
-
35)
-
Witkin, A.P.: `Scale space filtering', Int. Joint Conf. on Artificial Intelligence, 1983, p. 1019–1023.
-
36)
-
M.H. DeGroot ,
M.J. Schervish
.
(2001)
Probability and statistics.
-
37)
-
K. Mikolajczyk ,
C. Schmid
.
A performance evaluation of local descriptors.
IEEE Trans. Pattern Anal. Mach. Intell.
,
10 ,
1615 -
1629
-
38)
-
Fraundorfer, F., Bischof, H.: `A novel performance evaluation method of local detectors on non-planar scenes', Workshop on Empirical Evaluation Methods in Computer Vision, 2005.
-
39)
-
F. Fraundorfer ,
H. Bischof
.
Local detector evaluation.
-
40)
-
H. Bay ,
A. Ess ,
T. Tuytelaars ,
L.V. Gool
.
SURF: speeded up robust features.
Comput. Vis. Image Underst.
,
3 ,
346 -
359
-
41)
-
Mikolajczyk, K., Leibe, B.: `Local features for object class recognition', Int. Conf. on Computer Vision, 2005, p. 1792–1799.
-
42)
-
J. Crowley
.
(1981)
A representation for visual information.
-
43)
-
L. Kitchen ,
A. Rosenfeld
.
Gray-level corner detection.
Pattern Recognit. Lett.
,
95 -
102
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