Zernike moments are complex moments with the orthogonal Zernike polynomials as kernel function, compared with other moments; Zernike moments have greater advantages in image rotation and low noise sensitivity. Because of the Zernike moments have image rotation invariance, and can construct arbitrary high order moments, it can be used for target recognition. In this paper, the Zernike moment alg...

In this Paper we have used moment based Statistical methods for Feature extraction like Zernike, Pseudo-Zernike methods. Handwritten Gurmukhi isolated characters are used for feature extraction. Our database consists of 75-115 samples of each of 40 characters of Gurmukhi script collected from different writers. These samples are preprocessed, normalized and scaled to 48*48 sizes. Feature extrac...

Local representations became popular for facial affect recognition as they efficiently capture the image discontinuities, which play an important role for interpreting facial actions. We propose to use Local Zernike Moments (ZMs) [4] due to their useful and compact description of the image discontinuities and texture. Their main advantage in comparison to well-established alternatives such as L...

In this paper, we propose a novel approach to robust skeletonization, that is developed based on a statistical method using the Zernike moment theory controlled by Maximum Entropy Principal (MEP). This new concept of skeletonization is articuled into three steps. In the first one, estimation of the underlying probability density function (pdf) using Zernike moment is carried out. In the second,...

In this paper, we present a fingerprint matching approach based on localizing the matching regions in fingerprint images. The determination of the location of such Region Of Interest (ROI) using only the information related to core points based on a novel feature vectors extracted for each fingerprint image by Zernike Moment Invariant (ZMI) as the shape descriptor. The Zernike Moments is select...

Most of the sub-pixel edge detection methods proposed in literature are based on Ghosal and Mehrotra’s method which uses Zernike moments. Some research has been reported using Fourier-Mellin moments for sub-pixel edge detection. Pseudo Zernike moments have been proved to be superior to Zernike moments in terms of their feature representation capabilities and sensitivity to image noise. This pap...

The purpose of multi-frame super resolution (SR) is to combine multiple low resolution (LR) images to produce one high resolution (HR) image. The major challenge of classic SR approaches is accurate motion estimation between the frames. To address this problem, fuzzy motion estimation method has been proposed that replaces value of each pixel using the weighted average of all its neighboring pi...

Compound character recognition of Devanagari script is one of the challenging tasks since the characters are complex in structure and can be modified by writing combination of two or more characters. These compound characters occurs 12 to 15% in the Devanagari Script. The moment based techniques are being successfully applied to several image processing problems and represents a fundamental too...

Robust image hashing methods require the robustness to content preserving processing and geometric transform. Zernike moment is a local image feature descriptor whose magnitude components are rotationally invariant and most suitable for image hashing application. In this paper, we proposed Geometric invariant robust image hashing via zernike momment. Normalized zernike moments of an image are u...

A new set, to our knowledge, of orthogonal moment functions for describing images is proposed. It is based on the generalized pseudo-Zernike polynomials that are orthogonal on the unit circle. The generalized pseudo-Zernike polynomials are scaled to ensure numerical stability, and some properties are discussed. The performance of the proposed moments is analyzed in terms of image reconstruction...