Related Concepts
Definition
The notion of scale selection refers to methods for estimating characteristic scales in image data and for automatically determining locally appropriate scales in a scale-space representation, so as to adapt subsequent processing to the local image structure and compute scale-invariant image features and image descriptors.
An essential aspect of the approach is that it allows for a bottom-up determination of inherent scales of features and objects without first recognizing them or delimiting, alternatively segmenting, them from their surroundings.
Scale selection methods have also been developed from other viewpoints of performing noise suppression and exploring top-down information.
Background
The concept of scaleis essential when computing features and descriptors from image data. Real-world objects may contain different types of...
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
References
Bay H, Ess A, Tuytelaars T, van Gool (2008) Speeded up robust features (SURF). Comput Vis Image Underst 110(3):346–359
Bretzner L, Lindeberg T (1998) Feature tracking with automatic selection of spatial scales. Comput Vis Image Underst 71(3):385–392
Crowley J, Riff O (2003) Fast computation of scale normalised receptive fields. In: Proceedings of scale-space'03. Lecture Notes in Computer Science, vol 2695. Springer, Berlin/New York, pp 584–598
Elder JH, Zucker SW (1998) Local scale control for edge detection and blur estimation. IEEE Trans Pattern Anal Mach Intell 20(7):699–716
Field DJ (1987) Relations between the statistics of natural images and the response properties of cortical cells. J Opt Soc Am 4:2379–2394
Frangi AF, Niessen WJ, Hoogeveen RM, vanWalsum T, Viergever MA (2000) Model-based quantitation of 3D magnetic resonance angiographic images. IEEE Trans Med Imaging 18(10):946–956
Kadir T, Brady M (2001) Saliency, scale and image description. Int J Comput Vis 45(2):83–105
Koenderink JJ (1984) The structure of images. Biol Cybern 50:363–370
Krissian K, Malandain G, Ayache N, Vaillant R, Trousset Y (2000) Model-based detection of tubular structures in 3D images. Comput Vis Image Underst 80(2):130–171
Lindeberg T (1994) Scale-space theory in computer vision. The Springer international series in engineering and computer science. Springer, Berlin/New York
Lindeberg T (1998) Feature detection with automatic scale selection. Int J Comput Vis 30(2):77–116
Lindeberg T (1998) Edge detection and ridge detection with automatic scale selection. Int J Comput Vis 30(2):117–154
Lindeberg T (1998) A scale selection principle for estimating image deformations. Image Vis Comput 16(14):961–977
. Lindeberg T (2008) Scale-space. In: Wah B (ed.) Encyclopedia of computer science and engineering. Wiley, pp 2495–2504. dx.doi.org/10.1002/9780470050118.ecse609. Also available from http://www.nada.kth.se/ tony/abstracts/Lin08-EncCompSci.html
. Lindeberg T (2010) Interest point from scale-space features: scale-space primal sketch for differential descriptors International Journal of Computer Vision
Lindeberg T (2013) Scale selection properties of generalized scale-space interest point detectors. J Mathematical Imaging and Vision, 46(2):177–210
Lindeberg T, Bretzner L (2003) Real-time scale selection in hybrid multi-scale representations. In: Proceedings of Scale-Space Methods in Computer Vision. Lecture Notes in Computer Science, vol 2695. Springer, Berlin/New York, pp 148–163
Loog M, Li Y, Tax D (2009) Maximum membership scale selection. In: Multiple Classifier Systems. Lecture Notes in Computer Science, vol 5519. Springer, Berlin, pp 468–477.
Li Y, Tax DMJ, Loog M (2012) Supervised scale-invariant segmentation (and detection). In: Proc. Scale Space and Variational Methods in Computer Vision (Scale-Space'11), Ein Gedi, Israel. Lecture Notes in Computer Science, vol 6667. Springer, Berlin, pp 350–361
Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Mikolajczyk K, Schmid C (2004) Scale and affine invariant interest point detectors. Int J Comput Vis 60(1):63–86
Mrázek P, Navara M (2003) Selection of optimal stopping time for nonlinear diffusion filtering. Int J Comput Vis 52(2–3):189–203
Negre A, Braillon C, Crowley JL, Laugier C (2008) Real-time time-to-collision from variation of intrinsic scale. Exp Robot 39:75–84
Sato Y, Nakajima S, Shiraga N, Atsumi H, Yoshida S, Koller T, Gerig G, Kikinis R (1998) 3D multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Med Image Anal 2(2):143–168
Sporring J, Colios, CJ, Trahanias, PE (2000) Generalized scale-selection. In: Proc. International Conference on Image Processing (ICIP'00), Vancouver, Canada, pp 920–923
Witkin AP (1983) Scale-space filtering. In: Proceedings of 8th International Joint Conference on Artificial Intelligence, Karlsruhe, Germany, pp 1019–1022
Lindeberg T (2013) Invariance of visual operations at the level of receptive fields. PLoS ONE 8(7): e66990. doi:10.1371/journal.pone.0066990
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this entry
Cite this entry
Lindeberg, T. (2014). Scale Selection. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_242
Download citation
DOI: https://doi.org/10.1007/978-0-387-31439-6_242
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30771-8
Online ISBN: 978-0-387-31439-6
eBook Packages: Computer ScienceReference Module Computer Science and Engineering