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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.

Algorithm

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

• spiral model
• feature operator

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 ${\displaystyle w}$= 1/variance of a point ${\displaystyle p}$

${\displaystyle w(\mathbf {p} ,\alpha ,\tau ,\sigma )=\left(N(\sigma )-2\right){\frac {\lambda _{min}(M(\mathbf {p} ,\alpha ,\tau ,\sigma ))}{\Omega (\mathbf {p} ,\alpha ,\tau ,\sigma )}}}$  

comprising:

1. the image model

• Distance d of an edge from a reference point p in a spiral feature
• 
• 
• 

${\displaystyle {\begin{aligned}\Omega (\mathbf {p} ,\alpha ,\tau ,\sigma )&=\sum _{n=1}^{N(\sigma )}^{2}G_{\sigma }(\mathbf {q} _{n}-\mathbf {p} )\\&=N(\sigma )\mathbf {tr} \left\{R_{\alpha }\mathbf {\nabla } _{\tau }\mathbf {\nabla } _{\tau }^{T}R_{\alpha }^{T}*\mathbf {p} \mathbf {p} ^{T}G_{\sigma }(\mathbf {p} )\right\}\end{aligned}}}$

2. the smaller eigenvalue of the structure tensor ${\displaystyle \underbrace {M(\mathbf {p} ,\alpha ,\tau ,\sigma )} _{\text{structure tensor}}=\underbrace {G_{\sigma }(\mathbf {p} )} _{\text{weighted summation}}*\underbrace {(R_{\sigma }\nabla _{\tau }\nabla _{\tau }^{T}R_{\sigma }^{T})} _{\text{squared rotated gradients}}}$

Reduce the search space

Reduce the 5-dimensional search space by

• linking the differentiation scale ${\displaystyle \tau }$ to the integration scale

${\displaystyle \tau =\sigma /3}$

• solving for the optimal ${\displaystyle {\hat {\alpha }}}$ using the model

${\displaystyle \Omega (\alpha )=a-b\cos(2\alpha -2\alpha _{0})}$

• and determining the parameters from three angles, e. g.

${\displaystyle \Omega (0^{\circ }),\Omega (60^{\circ }),\Omega (120^{\circ })\quad \rightarrow \quad a,b,\alpha _{0}\quad \rightarrow \quad {\hat {\alpha }}}$

• pre-selection possible:

${\displaystyle \alpha =0^{\circ }\,\rightarrow \,{\mbox{junctions}},\quad \alpha =90^{\circ }\,\rightarrow \,{\mbox{circular features}}}$

Filter potential keypoints

• non-maxima suppression over scale, space and angle

• thresholding the isotropy ${\displaystyle \lambda _{2(M)}}$:
  eigenvalues characterize the shape of the keypoint, smallest eigenvalue has to be larger than threshold ${\displaystyle T_{\lambda }}$
  derived from noise variance ${\displaystyle V(n)}$ and significance level ${\displaystyle S}$:

${\displaystyle T_{\lambda }(V(n),\tau ,\sigma ,S)={\frac {N(\sigma )}{16\pi \tau ^{4}}}V(n)\chi _{2,S}^{2}}$

Algorithm

Results

Interpretability of SFOP keypoints

• Results of different detectors on a Siemens star
• Sfop: junctions red, circular features cyan
• Edge-based Regions
• Intensity-based Regions
• MSER
• Harris affine
• Hessian affine
• Lowe

See also

• Corner detection
• Feature detection (computer vision)

References

1. Forstner, Wolfgang; Dickscheid, Timo; Schindler, Falko (2009). "Detecting interpretable and accurate scale-invariant keypoints". 2009 IEEE 12th International Conference on Computer Vision. pp. 2256–2263. CiteSeerX 10.1.1.667.2530. doi:10.1109/ICCV.2009.5459458. ISBN 978-1-4244-4420-5.
2. ^ Bigün, J. (1990). "A Structure Feature for Some Image Processing Applications Based on Spiral Functions". Computer Vision, Graphics, and Image Processing. 51 (2): 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.

External links

• Project website at University of Bonn

• Applications of computer vision
• Learning in computer vision