In this paper, a novel method of image based palmprint matching based on features extracted from wavelet-based pseudo Zernike moments feature descriptor is proposed. Pseudo Zernike moments have additional properties of being more robust to image noise, possessing geometrical invariants property, having a nearly zero value of redundancy measure in a moment set due to its orthogonality property a...

Moments of images provide efficient local descriptors and have been used extensively in image analysis applications. Their main advantage is their ability to provide invariant measures of shape. In this work, we present an overview of the most commonly used image moments, as well as a hardware architecture capable of fast calculation of geometric, Zernike and Tchebichef moments. Index Terms – I...

The aim of this work is to present a system for recognizing isolated Arabic printed characters. This system goes through several stages: preprocessing, feature extraction and classification. Zernike moments, invariant moments and Walsh transformation are used to calculate the features. The classification is based on multilayer neural networks. A recognition rate of 98% is achieved by using Zern...

So far, plant identification has challenges for several researchers. Various methods and features have been proposed. However, there are still many approaches could be investigated to develop robust plant identification systems. This paper reports several experiments in using Zernike moments to build foliage plant identification systems. In this case, Zernike moments were combined with other fe...

In this paper, a face recognition approach using Zernike moments is presented for the main purpose of detecting faces in surveillance cameras. Zernike moments are invariant to rotation and scale and these properties make them an appropriate feature for automatic face recognition. A Viola-Jones detector based on the Adaboost algorithm is employed for detecting the face within an image sequence. ...

Zernike Moments are useful tools in pattern recognition and image analysis due to their orthogonality and rotation invariance property. However, direct computation of these moments is very expensive, limiting their use especially at high orders. There has been some efforts to reduce the computational cost by employing quantized polar coordinate systems, which also reduces the accuracy of the mo...

This paper introduces a robust image watermark based on an invariant image feature vector. Normalized Zernike moments of an image are used as the vector. The watermark is generated by modifying the vector. Watermark signal is designed with Zernike moments. The signal is added to the cover image in the spatial domain after the reconstruction process. We extract the feature vector from the modifi...

This paper presents a new class of Tchebichef moments in polar coordinate form, using which rotational invariants can be easily constructed. The structure of the invariants is very similar to that of Zernike and Pseudo-Zernike moments, and their computation does not involve discrete approximation of continuous integral terms. The invariants are thus very robust in the presence of image noise, a...

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 method using Zernike Moments (ZM) and Histogram of Oriented Gradients (HOG) for classification and recognition of plant leaf images is proposed in this paper. After preprocessing, we compute the shape features of a leaf using Zernike Moments (ZM) and texture features using Histogram of Oriented Gradients (HOG) and then Support Vector Machine (SVM) classifier is used for plant leaf image class...