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This work presents a method to increase the face recognition accuracy using a combination of wavelet, PCA, KPCA, and RBF neural networks. Preprocessing, feature extraction and classification rules are three crucial issues for face recognition. This paper presents a hybrid approach to employ these issues. For preprocessing and feature extraction steps, we apply a combination of wavelet transform, PCA...
This paper proposes a set of efficient algorithms for rotation- and scale-invariant texture classification. This set is based on the well established Gabor feature. A circular sum of the Gabor feature elements belonging to the same scale is proposed to reduce the effect of rotation, while a slide matching of augmented scales is proposed to address the effect of scaling. The resulting feature vector...
Support vector machine (SVM) is a machine learning algorithm, which has been used recently for classification of hyperspectral images. SVM uses various kernel functions like RBF and polynomial to map the data into higher dimensional space to improve data separability. New kernel functions are used in this paper to classify hyperspectral images which are based on wavelet functions as named wavelet-kernels...
This paper introduces a new method of feature extraction from wavelet coefficients for classification of digital mammograms. A matrix is constructed by putting wavelet coefficients of each image of a building set as a row vector. The method consists then on selecting by threshold, the columns which will maximize the Euclidian distances between the different class representatives. The selected columns...
Feature extraction is one of the important tasks in face recognition. Structural and statistical based approaches are two broad categories of feature extraction. This paper proposes a statistical approach for feature extraction based on Generalized Pseudo-Zernike Moment (GPZM) invariants which is powerful to characterize the image using region based shape features and also invariant to size, tilt,...
A new rotation invariant texture descriptor based on the difference of offset Gaussian (DooG) and a sub-micro pattern encoding are proposed. We first apply the Gabor wavelet to texture images. We then utilize the DooG to measure the difference between the center positive Gaussian and the neighbor rotated negative one. We encode the local micro texture using our proposed method, a sub-micro pattern...
This paper address the problem of change detection in very high resolution remote sensing images. To that end, we define a measure of the observed change based on the distribution of the coefficients issued from a wavelet transform, taking care to be rotation invariant. The dissimilarities are obtained through the Kullback-Liebler distance and a change features vector is defined from all the distances...
The generalized Gaussian density model for wavelet subbands has been applied widely in texture image retrieval. In this paper, we employ wavelet-based texture extraction that is based on accurate modeling of the distribution of wavelet coefficients using generalized Gaussian density to classify four diffuse lung disease patterns: normal, emphysema, ground glass opacity and honey-combing. The evaluated...
This paper presents a new efficient technique for supervised pixel-based texture classification. The proposed scheme first performs a selection process that automatically determines a subset of prototypes that characterize each texture class based on the outcome of a multichannel Gabor wavelet filter bank. Then, every image pixel is classified into one of the given texture classes by using a K-NN...
The dual-tree complex wavelet transform (DT CWT) was introduced to overcome the disadvantages of the traditional fully decimated discrete wavelet transform (DWT), namely the shift-variance and the poor directional selectivity properties. Because of its improvements in these aspects, the dual-tree has been widely used in many image processing applications such as denoising, motion estimation, image...
Image classification often relies on texture characterization. Yet texture characterization has so far rarely been based on a true 2D multifractal analysis. Recently, a 2D wavelet Leader based multifractal formalism has been proposed. It allows to perform an accurate, complete and low computational and memory costs multifractal characterization of textures in images. This contribution describes the...
This paper examines whether machine learning and image analysis tools can be used to assist art experts in the authentication of unknown or disputed paintings. Recent work on this topic has presented some promising initial results. Our reexamination of some of these recently successful experiments shows that variations in image clarity in the experimental datasets were correlated with authenticity,...
This paper introduces an efficient detection of brain tumor from cerebral MRI images. The methodology consists of three steps: enhancement, segmentation and classification. To improve the quality of images and limit the risk of distinct regions fusion in the segmentation phase an enhancement process is applied. We adopt mathematical morphology to increase the contrast in MRI images. Then we apply...
This paper proposes to combine standard SVM classification with a hierarchical approach to increase SVM classification accuracy as well as reduce computational load of SVM testing. Support vectors are obtained by applying SVM training to the entire original training data. For classification, multi-level two-dimensional wavelet decomposition is applied to each hyperspectral image band and low spatial...
Image classification problem is one of the most challenges of computer vision. In this paper, a robust image classification approach using multilevel neural networks is proposed. In this approach, each image is fixedly divided into five regions each equal to half of the original image. Then these regions are classified by the multilevel neural classifier into five categories, i.e., ??sky??, ??water??,...
Identifying relevant, representative and more important, discriminant image features for analysis and proper image classification purpose is one of the main tasks in image processing and pattern recognition field. In this paper, Gabor wavelets based features are extracted from medical mammogram images representing normal tissues, or benign and malign tumors. Once features are detected, Principal Component...
We proposed a new normalization method for iris recognition, which is different from the conventional one in which the annular iris region is unwrapped to a rectangular block under polar coordinate. In this method, we investigate the effect of interpolation and decimation in conventional normalization method to recognition rate for the first time. We used the original texture to fill the pupil area,...
The identification of image acquisition source is an important problem in digital image forensics. In this work, we focus on building a classifier to effectively distinguish between digital images taken from digital single lens reflex (DSLR) and compact cameras. Based on the architecture and the imaging features of DSLR and compact cameras, the images taken from different sources may have different...
The paper proposed a novel algorithm for texture classification system. This texture classification system is based on the extracted features on the performance of texture images' nonsubsampled contourlet transform (NSCT). To decrease the dimension of feature vector, we achieve the mean and standard deviation of NSCT coefficients matrix in different subbands and various directions. To compare the...
This paper proposes a new image thresholding function which exploits spatial correlation among image wavelet coefficients and classification technique. This approach is valid because a large wavelet coefficient will probably have large wavelet coefficients as its neighbours, so that estimation accuracy is increased and the new function overcomes the shortcomings of the hard thresholding function and...
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