Feature detection in numeric data

A method (10) for detecting features in digital numeric data comprises obtaining (11) digital numeric data comprising values corresponding to a plurality of sampling points over a domain space having at least one dimension, computing (13) a plurality of scale-space data comprising filtering said digital numeric data using a filter bank, determining (15) a plurality of feature regions each corresponding to a local extremum in scale and location of the scale-space data; and determining (17) a feature region descriptor for each of said plurality of feature regions. The filter bank is a Cosine Modulated Gaussian filter bank in which the standard deviation parameter of the Gaussian equals 1 À �¢ ln 2 2 2 b + 1 2 b - 1 multiplied by the cosine wavelength, in which b is in the range of 0.75 to 1.25, or said filter bank is an N th -order Gaussian Derivative filter bank with N being in the range of 5 to 20.

Octrooi-publicatienummer: EP2677464

Jaar aanvraag: 2018

Jaar toekenning: 2018

Jaar van publicatie: 2018

Status: Toegewezen

Technologiedomeinen: Computertechnologie

Gevalideerd voor IOF-sleutel: Ja

Toegewezen aan: Associatie KULeuven