MOMENT-BASED LOCAL DESCRIPTOR USING SCALE INVARIANT FEATURE

摘要

A method for generating a moment-based local descriptor by using a scale invariant feature is provided to match images strongly with a change in rotation, scale, time point and illumination and improve performance with respect to various rotation changes. An input image and a Gaussian kernel are successively matched to generate a Gaussian scale-space(101). A difference image between two adjacent Gaussian images is calculated by using two successive images of all the images in the generated Gaussian scale-space to generate a DoG(Difference of Gaussian) scale-space(102). Local polar values in the DoG scale-space are selected as a feature(103). Every feature and a peripheral image defined by the features are normalized to have the same scale(104). An absolute value of an ART(Angular Radial Transform) coefficient or an absolute value of a Zernike moment are calculated through convolution with an ART base function or a Zernike base function from the peripheral image of the normalized feature(105). The calculated absolute value is normalized to have a unit length to correct the absolute value(106). A component of the normalized absolute value having more than a predetermined boundary value is adjusted to have a lower certain value(107). The absolute value is re-normalized to have a unit length(108).