Jump to content

Scale-invariant feature operator

From Wikipedia, the free encyclopedia
This is an old revision of this page, as edited by Lithopsian (talk | contribs) at 18:11, 22 January 2019 (rewrite lead to give context). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

In the fields of computer vision and image analysis, the scale-invariant feature operator (or SFOP) is an algorithm to detect local features in images. The algorithm was published by Förstner et al. in 2009.[1]

Algorithm

The scale-invariant feature operator (SFOP) is based on two theoretical concepts:

  • spiral model[2]
  • feature operator[3]

Desired properties of keypoint detectors:

  • Invariance and repeatability for object recognition
  • Accuracy to support camera calibration
  • Interpretability: Especially corners and circles, should be part of the detected keypoints (see figure).
  • As few control parameters as possible with clear semantics
  • Complementarity to known detectors

scale-invariant corner/circle detector.

Theory

Maximize the weight

Maximize the weight = 1/variance of a point

  

comprising:

1. the image model[2]

2. the smaller eigenvalue of the structure tensor

Reduce the search space

Reduce the 5-dimensional search space by

  • linking the differentiation scale to the integration scale
  • solving for the optimal using the model
  • and determining the parameters from three angles, e. g.
  • pre-selection possible:

Filter potential keypoints

  • non-maxima suppression over scale, space and angle
  • thresholding the isotropy :
    eigenvalues characterize the shape of the keypoint, smallest eigenvalue has to be larger than threshold
    derived from noise variance and significance level :

Algorithm

Algorithm
Algorithm

Results

Interpretability of SFOP keypoints

See also

  • [1], the authors project website at University of Bonn

References

  1. ^ Förstner, Wolfgang; Dickscheid, Timo and Schindler, Falko (2009). "Detecting Interpretable and Accurate Scale-Invariant Keypoints" (PDF). International Conference on Computer Vision. Kyoto, Japan. pp. 2256–2263. {{cite conference}}: Unknown parameter |booktitle= ignored (|book-title= suggested) (help)CS1 maint: multiple names: authors list (link)
  2. ^ a b Bigün, J. (1990). "A Structure Feature for Some Image Processing Applications Based on Spiral Functions". Computer vision, graphics, and image processing. 51 (2). Academic Press: 166–194.
  3. ^ Förstner, Wolfgang (1994). "A Framework for Low Level Feature Extraktion". European Conference on Computer Vision. Vol. 3. Stockholm, Sweden. pp. 383–394. {{cite conference}}: Unknown parameter |booktitle= ignored (|book-title= suggested) (help)