E. G. Karakasis

TL;DR: An image retrieval framework that uses affine image moment invariants as descriptors of local image areas and results are promising compared with other widely used local descriptors, allowing the proposed framework to serve as a reference point for future image moment local descriptor applied to the general task of content based image retrieval.

TL;DR: A general framework for computing accurate quaternion color moments and their corresponding invariants is proposed by studying the characteristics of different orthogonal polynomials, which permits the usage of any polynomial-like kernel in a unified and consistent way.

TL;DR: A novel set of moment invariants based on the Krawtchouk moments are introduced, which are exhaustively tested in several well known computer vision datasets, regarding their rotation, scaling and translation (RST) invariant recognition performance, by resulting to remarkable outcomes.

TL;DR: A novel descriptor able to improve the classification capabilities of a typical pattern recognition system is proposed in this paper and has shown the superiority of the introduced descriptor over the moment invariants, the LBP operator and other well-known from the literature descriptors such as HOG, HOG-LBP and LBP-HF.

TL;DR: A novel algorithm that permits the fast and accurate computation of geometric moments on gray-scale images is presented in this paper, which constitutes an extension of the IBR algorithm, introduced in the past, which was applicable only for binary images.