Abstract
We describe an accurate keypoint detector that is stable under viewpoint change. In this paper, keypoints correspond to actual junctions in the image. The principle of ASN differs fundamentally from other keypoint detectors. At each position in the image and before any detection, it systematically estimates the position of a potential junction from the local gradient field. Keypoints then appear where multiple position estimates are attracted. This approach allows the detector to adapt in shape and size to the image content. One further obtains the area where the keypoint has attracted solutions. Comparative results with other detectors show the improved accuracy and stability with viewpoint change.
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Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. International Journal of Computer Vision 37(2), 151–172 (2000)
Lowe, D.G.: Object recognition from local scale-invariant features. In: International Conference on Computer Vision, pp. 1150–1157 (1999)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. International Journal of Computer Vision 60(1), 63–86 (2004)
Bay, H., Tuytelaars, T., Gool, L.V.: Surf: Speeded up robust features. In: European Conference on Computer Vision, pp. 404–417 (2006)
Rohr, K.: Localization properties of direct corner detectors. Journal of Mathematical Imaging and Vision 4( 2), 139–150 (1994)
Deriche, R., Giraudon, G.: Accurate corner detection: an analytical study. In: International Conference on Computer Vision, pp. 66–70 (1990)
Tuytelaars, T., Gool, L.V.: Wide baseline stereo matching based on local, affinely invariant regions. In: British Machine Vision Conference, pp. 412–422 (2000)
Lindeberg, T.: Feature detection with automatic scale selection. International Journal of Computer Vision 30(2), 79–116 (1998)
Triggs, B.: Detecting keypoints with stable position, orientation, and scale under illumination changes. In: European Conference on Computer Vision, pp. 100–113 (2004)
Matas, J., Chum, O., Martin, U., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: British Machine Vision Conference, vol. 1, pp. 384–393 (2002)
Kadir, T., Zisserman, A., Brady, M.: An affine invariant salient region detector. In: European Conference on Computer Vision, pp. 228–241 (2004)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. Pattern Analysis and Machine Intelligence 27(10), 1615–1630 (2005)
Fraundorfer, F., Bischof, H.: A novel performance evaluation method of local detectors on non-planar scenes. In: Computer Vision and Pattern Recognition, vol. 3, pp. 33–33 (2005)
Moreels, P., Perona, P.: Evaluation of features detectors and descriptors based on 3d objects. International Journal of Computer Vision 73(3), 263–284 (2007)
Forstner, W.: A framework for low level feature extraction. In: European Conference on Computer Vision, pp. 383–394 (1994)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, pp. 147–151 (1988)
Park, S.J., Ahmad, M.B., Rhee, S.H., Han, S.J., Park, J.A.: Image corner detection using radon transform. In: International Conference on Computational Science and Applications, pp. 948–955 (2004)
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© 2008 Springer-Verlag Berlin Heidelberg
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Ouellet, JN., Hébert, P. (2008). ASN: Image Keypoint Detection from Adaptive Shape Neighborhood. In: Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88682-2_35
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DOI: https://doi.org/10.1007/978-3-540-88682-2_35
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