Traditional trademark image retrieval algorithm only using global feature easily makes mistaken retrieval, and scale invariant feature transform (SIFT) features have limited descriptive ability for image contour and high algorithm complexity. This paper proposes a trademark retrieval algorithm combining the image global features and local features. In this paper, we will firstly, extract Zernik...

This paper introduces a novel discriminant moment-based method as a feature extraction technique for face recognition. In this method, pseudo Zernike moments are performed before the application of Fisher’s Linear Discriminant to achieve a stable numerical computation and good generalization in small-sample-size problems. Fisher’s Linear Discriminant uses pseudo Zernike moments to derive an enh...

An architecture for fast video object recognition is proposed. This architecture is based on an approximation of featureextraction function: Zernike moments and an approximation of a classification framework: Support Vector Machines (SVM). We review the principles of the moment-based method and the principles of the approximation method: dithering. We evaluate the performances of two moment-bas...

Offline Tamil Handwritten character recognition system using a combination of Zernike moments and diagonal features of Zones are proposed in this paper. The proposed recognition system yields higher rate of recognition accuracy compared to the systems developed using the conventional feature extractions using horizontal and vertical methods. Since, Zernike moments are known as better descriptor...

Handwritten automatic character recognition has attracted many researchers all over the world to contribute automatic character recognition domain. Shape identification and feature extraction is very important part of any character recognition system and success of method is highly dependent on selection of features. However feature extraction is the most important step in defining the shape of...

Super-resolution (SR) with probabilistic motion estimation is a successful algorithm to circumvent the limitation of motion estimation upon conventional superresolution methods. However, the algorithm can’t match similar patches with rotation or scale. This paper presents an efficient improved algorithm by introducing Zernike moments as representation of image invariant features into similarity...

In this paper, we present a unified framework for the analysis of video databases by using Markov spatio-temporal random walks on graph. The framework provides an efficient approach for both clustering, data organization, dimension reduction and recognition. The aim is to develop a visionbased approach for human behaviour recognition. Our contribution lies in three aspects. First, we employ 3D ...

Digital Watermarking allows an individual to add some hidden copyright notices or other verification messages to digital media, where message is a group of bits describing the information pertaining to the signal or its author. For this purpose, two techniques viz. global watermarking and local watermarking can be used. In this paper, the performance of two feature based global and local waterm...

In the paper, an effective method for reducing Geometrical Error (G.E) and Numerical Error (N.E) of Zernike moments is proposed. By running MATLAB for the proposed Zernike’s algorithm, the results of our proposed methods have shown a remarkable improvement to the total error of the analysis. The new proposed technique in reducing significantly Geometrical Error is performed better than that in ...

In this paper, we present a data hiding detection (steganalysis) method based on Zernike moments of discrete wavelet coefficients of an image and SVM. We use Zernike moments of DWT subbands coefficients of an images as features for steganalysis and use support vector machines (SVM) for classification of stego and non-stego images. Experimental results show that there are different between these...