Abstract
In this paper we present a novel scale invariant interest point detector of blobs which incorporates the idea of blob movement along the scales. This trajectory of the blobs through the scale space is shown to be valuable information in order to estimate the most stable locations and scales of the interest points. Our detector evaluates interest points in terms of their self trajectory along the scales and its evolution obtaining non-redundant and discriminant features. Moreover, in this paper we present a differential geometry view to understand how interest points can be detected. We propose to analyze the gaussian curvature to classify image regions as blobs, edges or corners.
Our interest point detector has been compared with some of the most important scale invariant detectors on infrared (IR) images, outperforming their results in terms of: number of interest points detected and discrimination of the interest points.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lindeberg, T.: Feature detection with automatic scale selection. International Journal on Computer Vision (IJCV) 30, 77–116 (1998)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal on Computer Vision (IJCV) 60, 91–110 (2004)
Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. International Journal on Computer Vision (IJCV) 60, 63–86 (2004)
Kadir, T., Brady, M.: Saliency, scale and image description. International Journal on Computer Vision (IJCV) 45(2), 83–105 (2001)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: British Machine Vision Conference, pp. 384–393 (2002)
Bay, H., Ess, A., Tuytelaars, T., Gool, L.J.V.: Speeded-up robust features (surf). Computer Vision and Image Understanding 110(3), 346–359 (2008)
Crowley, J.L.: A representation for visual information with application to machine vision. PhD thesis (1982)
DoCarmo, M.P.: Differential Geometry of Curves and Surfaces. Prentice-Hall, Englewood Cliffs (1976)
Lenz, R.: Group theoretical feature extraction: Weighted invariance and textulre analysis. In: Theory & Applications of Image Analysis: Selected Papers from the 7th Scandinavian Conference on Image Analysis, pp. 63–70 (1992)
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A comparison of affine region detectors. International Journal on Computer Vision (IJCV) 65, 43–72 (2005)
Kadir, T., Zisserman, A., Brady, M.: An affine invariant salient region detector. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 228–241. Springer, Heidelberg (2004)
Lindeberg, T.: Scale-Space Theory in Computer Vision. The International Series in Engineering and Computer Science. Springer, Heidelberg (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ferraz, L., Binefa, X. (2009). A Scale Invariant Interest Point Detector for Discriminative Blob Detection. In: Araujo, H., Mendonça, A.M., Pinho, A.J., Torres, M.I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2009. Lecture Notes in Computer Science, vol 5524. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02172-5_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-02172-5_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02171-8
Online ISBN: 978-3-642-02172-5
eBook Packages: Computer ScienceComputer Science (R0)