This paper describes a fast algorithm to compute local axial moments used for the detection of objects of interest in images. The basic idea is grounded on the elimination of redundant operations while computing axial moments for two neighboring angles of orientation. The main result is that the complexity of recursive computation of axial moments becomes independent of the total number of computed moments in a given point, i.e. it is of the order O(N) where N is the data size. This result is of great importance in computer vision since many feature extraction methods are based on the computation of axial moments. The experimental results confirm the time complexity and accuracy predicted by the theoretical analysis. © 2001 IEEE
Zernike moments are widely used in several pattern recognition applications, as invariant descriptor...
Discrete orthogonal moments are powerful tools for characterizing image shape features for applicati...
Discrete orthogonal moments have several computational advantages over continuous moments. However w...
This paper describes a fast algorithm to compute local axial moments used for the detection of objec...
In this work we describe a fast and stable algorithm for the computation of the orthogonal moments o...
Pseudo-Zernike moments have better feature representation capabilities and are more robust to image ...
Abstract. Geometric moments have been proven to be a very efficient tool for description and recogni...
This paper presents a novel approach to the fast computation of Zernike moments from a digital image...
This paper presents recursive algorithms for fast computation of Legendre and Zernike moments of a g...
Abstract. Zernike Moments are useful tools in pattern recognition and image analysis due to their or...
International audienceDiscrete orthogonal moments have been recently introduced in the field of imag...
In this paper, a new methodology for computing orientation of a gray-tone image by the method of mom...
Abstract: Problem statement: Orthogonal circular moments of gray level images such as Zernike, pseud...
Abstract—Discrete orthogonal moments have several computa-tional advantages over continuous moments....
Zernike moments are widely used in several pattern recognition applications, as invariant descriptor...
Zernike moments are widely used in several pattern recognition applications, as invariant descriptor...
Discrete orthogonal moments are powerful tools for characterizing image shape features for applicati...
Discrete orthogonal moments have several computational advantages over continuous moments. However w...
This paper describes a fast algorithm to compute local axial moments used for the detection of objec...
In this work we describe a fast and stable algorithm for the computation of the orthogonal moments o...
Pseudo-Zernike moments have better feature representation capabilities and are more robust to image ...
Abstract. Geometric moments have been proven to be a very efficient tool for description and recogni...
This paper presents a novel approach to the fast computation of Zernike moments from a digital image...
This paper presents recursive algorithms for fast computation of Legendre and Zernike moments of a g...
Abstract. Zernike Moments are useful tools in pattern recognition and image analysis due to their or...
International audienceDiscrete orthogonal moments have been recently introduced in the field of imag...
In this paper, a new methodology for computing orientation of a gray-tone image by the method of mom...
Abstract: Problem statement: Orthogonal circular moments of gray level images such as Zernike, pseud...
Abstract—Discrete orthogonal moments have several computa-tional advantages over continuous moments....
Zernike moments are widely used in several pattern recognition applications, as invariant descriptor...
Zernike moments are widely used in several pattern recognition applications, as invariant descriptor...
Discrete orthogonal moments are powerful tools for characterizing image shape features for applicati...
Discrete orthogonal moments have several computational advantages over continuous moments. However w...