다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Fractional Charlier moments for image reconstruction and image watermarking

Quaternion polar complex exponential transform for invariant color image description

3D image analysis by separable discrete orthogonal moments based on Krawtchouk and Tchebichef polynomials

Stable computation of higher order Charlier moments for signal and image reconstruction

Fast computation of separable two-dimensional discrete invariant moments for image classification

Recently, the discrete orthogonal moments have been introduced in image analysis and pattern recognition. They have better capacities than the continuous orthogonal moments but the use of these moments as feature descriptors is limited by their high computational cost. As a solution to this problem, we present in this paper an approach for fast computation of Meixner's discrete orthogonal moments. By using the recurrence relation with respect to variable x instead of order n in computation of Meixner's discrete orthogonal polynomials and the image block representation for binary images and intensity slice representation for gray-scale images. The acceleration of the computation time of Meixner moments is due to an innovative image representation where the image is described by a number of homogenous rectangular blocks instead of individual pixels. A novel set of invariant moments based on the Meixner discrete orthogonal moments is also proposed. These invariant moments are derived algebraically from the geometric invariant momenst and their computation is accelerated using image representation scheme. The presented algorithms are tested in several well-known computer vision datasets, regarding computational time, image reconstruction, invariability, and classification. The performances of Meixner invariant moments used as pattern features for a pattern classification application are compared with Hu, Tchebichef, dual-Hahn, and Krawtchouk invariant moments.

A Fast Computation of Novel Set of Meixner Invariant Moments for Image Analysis

The discrete orthogonal moments are powerful descriptors for image analysis and pattern recognition. However, the computation of these moments is a time consuming procedure. To solve this problem, a new approach that permits the fast computation of Hahn’s discrete orthogonal moments is presented in this paper. The proposed method is based, on the one hand, on the computation of Hahn’s discrete orthogonal polynomials using the recurrence relation with respect to the variable x instead of the order n and the symmetry property of Hahn’s polynomials and, on the other hand, on the application of an innovative image representation where the image is described by a number of homogenous rectangular blocks instead of individual pixels. The paper also proposes a new set of Hahn’s invariant moments under the translation, the scaling, and the rotation of the image. This set of invariant moments is computed as a linear combination of invariant geometric moments from a finite number of image intensity slices. Several experiments are performed to validate the effectiveness of our descriptors in terms of the acceleration of time computation, the reconstruction of the image, the invariability, and the classification. The performance of Hahn’s moment invariants used as pattern features for a pattern classification application is compared with Hu [IRE Trans. Inform. Theory8, 179 (1962)] and Krawchouk [IEEE Trans. Image Process.12, 1367 (2003)] moment invariants.

Mhamed Sayyouri

Papers

Fast computation of separable two-dimensional discrete invariant moments for image classification

Fractional Charlier moments for image reconstruction and image watermarking

Stable computation of higher order Charlier moments for signal and image reconstruction

A Fast Computation of Novel Set of Meixner Invariant Moments for Image Analysis

Improving the performance of image classification by Hahn moment invariants