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 = 1/variance of a point
comprising:
1. the image model
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
Results Interpretability of SFOP keypoints
See also
Corner detection Feature detection (computer vision)
References
External links Project website at University of Bonn