Keypoints Selection in the Gauss Laguerre Transformed Domain

The present paper is devoted to the introduction of a novel technique to select keypoints from digital images and build representative and distinctive descriptors. The algorithm performs a multiresolution image analysis in the Laguerre Gauss transformed domain and collects in a local descriptor the transformed coefficients at multiple scales, of the keypoint’s representative pattern. The rotation invariance of the Circular Harmonics and the multiscale approach make the system more robust than other descriptors to match patterns related by affine transformations.