Shape representation provides fundamental features for many applications in computer vision and it is known to be important cues for human vision. This paper presents an experimental study on recognition of mice behavior. We investigate the performance of the four shape recognition methods, namely Chain-Code, Curvature, Fourier descriptors and Zernike moments. These methods are applied to a rea...

In this paper, a neural network (NN) based approach for translation, scale, and rotation invariant recognition of objects is presented. The utilized network is a Multi-Layer Perceptron (MLP) classifier with one hidden layer. The back-propagation learning is used for its training. The image is represented by rotation invariant features which are the magnitudes of the Zernike moments of the image...

Today, the number of registered trademarks is huge and is increasing rapidly. Thus, the job of identifying infringement of trademarks by solely using manual inspection is tiring, laborious and time consuming. To cope with the tremendous amount of available registered trademarks and to protect the infringement of trademarks, a new automatic and efficient trademark retrieval system is necessary a...

We test various features for recognition of leaves of wooden species. We compare Fourier descriptors, Zernike moments, Legendre moments and Chebyshev moments. All the features are computed from the leaf boundary only. Experimental evaluation on real data indicates that Fourier descriptors slightly outperform the other tested features.

Digital watermarking has become an important technique for copyright protection but its robustness against attacks remains a major problem. In this paper, we propose a normalizationbased robust image watermarking scheme. In the proposed scheme, original host image is first normalized to a standard form. Zernike transform is then applied to the normalized image to calculate Zernike moments. Dith...

The selection of features for classifying a pattern by means a fuzzy reasoning, is fundamental in order to obtain a reliable and significative response. The scope of this work is to compare three methods specialized for the extraction of features from images and, consequently, to study the ability of classification performed by applying a fuzzy inference system. The methods to be compared were:...

This paper presents a novel and robust algorithm that retrieves the rotation angle between two different patterns, together with their similarity degree. The result is optimal in the sense that it minimizes the euclidean distance between the two images. This approach is based on Zernike moments and constitutes a new way to compare two Zernike descriptors that improves standard Euclidean approac...

The aim of this paper is to study the multi-algorithm based palmprint indexing at feature extraction level. The proposed approach is based on the fusion of Haar wavelets and Zernike moments. Experiments are conducted on PolyU palmprint database to assess the actual advantage of the fusion of the multiple representations, in comparison to the single representation. Experimental results reveal th...

Handwritten signature is the most accepted and economical means of personnel authentication. It can be verified using online or offline schemes. This paper proposes a signature verification model by combining Zernike moments feature with circularity and aspect ratio. Unlike characters, signatures vary each time because of its behavioural biometric property. Signatures can be identified based on...

In this paper, we propose a method that uses Local Zernike Moments (LZM) for face recognition in low-resolution face images. Global Zernike Moments produce one moment value for whole image, whereas LZM are based on the evaluation of the moments for each pixel. LZM are shown to achieve significant success and robustness in face recognition. In order to further increase the robustness of LZM agai...