Discrete Wavelet Frame Research Articles

Texture image classification using modular radial basis function neural networks

Image classification has become an important topic in multimedia processing. Recently, neural network-based methods have been proposed to solve the classification problem. Among them, the radial basis function neural network (RBFNN) is the most popular architecture, because it has good learning and approximation capabilities. However, traditional RBFNNs are sensitive to center initialization. To obtain appropriate centers, it needs to find significant features for further RBF clustering. In addition, the training procedure of a traditional RBFNN is time consuming. Therefore, in this work, a combination of a self-organizing map (SOM) and learning vector quantization (LVQ) neural networks is proposed to select more appropriate centers for an RBFNN, and a modular RBF neural network (MRBFNN) is proposed to improve the classification rate and to speed up the training time. Experimental results show that the proposed MRBFNN has better performance than those of the traditional RBFNN, the discrete wavelength transform (DWT)-based method, the tree structured wavelet (TWS), the discrete wavelet frame (DWF), the rotated wavelet filter (RWF), and the wavelet neural network based on adaptive norm entropy (WNN-ANE) methods.

Multifocus image fusion using the nonsubsampled contourlet transform

A novel image fusion algorithm based on the nonsubsampled contourlet transform (NSCT) is proposed in this paper, aiming at solving the fusion problem of multifocus images. The selection principles of different subband coefficients obtained by the NSCT decomposition are discussed in detail. Based on the directional vector normal, a ‘selecting’ scheme combined with the ‘averaging’ scheme is presented for the lowpass subband coefficients. Based on the directional bandlimited contrast and the directional vector standard deviation, a selection principle is put forward for the bandpass directional subband coefficients. Experimental results demonstrate that the proposed algorithm cannot only extract more important visual information from source images, but also effectively avoid the introduction of artificial information. It significantly outperforms the traditional discrete wavelet transform-based and the discrete wavelet frame transform-based image fusion methods in terms of both visual quality and objective evaluation, especially when the source images are not perfectly registered.

EXTRACTION OF NOISE TOLERANT, GRAY-SCALE TRANSFORM AND ROTATION INVARIANT FEATURES FOR TEXTURE SEGMENTATION USING WAVELET FRAMES

In this paper, we propose a texture feature extraction scheme at multiple scales and discuss the issues of rotation and gray-scale transform invariance as well as noise tolerance of a texture analysis system. The nonseparable discrete wavelet frame analysis is employed which gives an overcomplete wavelet decomposition of the image. The texture is decomposed into a set of frequency channels by a circularly symmetric wavelet filter, which in essence gives a measure of edge magnitudes of the texture at different scales. The texture is characterized by local energies over small overlapping windows around each pixel at different scales. The features so extracted are used for the purpose of multi-texture segmentation. A simple clustering algorithm is applied to this signature to achieve the desired segmentation. The performance of the segmentation algorithm is evaluated through extensive testing over various types of test images.

Image Analysis Techniques for Automated IVUS Contour Detection

Intravascular ultrasound (IVUS) constitutes a valuable technique for the diagnosis of coronary atherosclerosis. The detection of lumen and media-adventitia borders in IVUS images represents a necessary step towards the reliable quantitative assessment of atherosclerosis. In this work, a fully automated technique for the detection of lumen and media-adventitia borders in IVUS images is presented. This comprises two different steps for contour initialization: one for each corresponding contour of interest and a procedure for the refinement of the detected contours. Intensity information, as well as the result of texture analysis, generated by means of a multilevel discrete wavelet frames decomposition, are used in two different techniques for contour initialization. For subsequently producing smooth contours, three techniques based on low-pass filtering and radial basis functions are introduced. The different combinations of the proposed methods are experimentally evaluated in large datasets of IVUS images derived from human coronary arteries. It is demonstrated that our proposed segmentation approaches can quickly and reliably perform automated segmentation of IVUS images. (E-mail: mpapad@iti.gr)

Redundant Wavelet Watermarking using Spread Spectrum Modulation

Spread Spectrum modulation has become a preferred paradigm in many watermarking applications. This paper analyzes the performance of such a blind watermarking scheme under discrete wavelet frame rather than a traditional orthonormal wavelet expansion. The over complete representation offered by the redundant frame facilitates the identification of significant image features via a simple correlation operation across scales. The performance and resiliency of the proposed technique are analyzed against several volumetric distortion sources. The experimental results of this oblivious algorithm illustrate better visual and statistical imperceptibility and robustness compared to the usually critically sampled discrete wavelet transform. This algorithmic architecture utilizes the existing allocated bandwidth in the data transmission channel in a more efficient manner.

Texture Analysis and Radial Basis Function Approximation for IVUS Image Segmentation

>Intravascular ultrasound (IVUS) has become in the last years an important tool in both clinical and research applications. The detection of lumen and media-adventitia borders in IVUS images represents a first necessary step in the utilization of the IVUS data for the 3D reconstruction of human coronary arteries and the reliable quantitative assessment of the atherosclerotic lesions. To serve this goal, a fully automated technique for the detection of lumen and media-adventitia boundaries has been developed. This comprises two different steps for contour initialization, one for each corresponding contour of interest, based on the results of texture analysis, and a procedure for approximating the initialization results with smooth continuous curves. A multilevel Discrete Wavelet Frames decomposition is used for texture analysis, whereas Radial Basis Function approximation is employed for producing smooth contours. The proposed method shows promising results compared to a previous approach for texture-based IVUS image analysis.

Image fusion using a low-redundancy and nearly shift-invariant discrete wavelet frame

This paper describes an efficient approach to the fusion of spatially registered images and image sequences. The fusion method uses an improved wavelet representation, which has low redundancy as well as near-shift-invariance. Specifically, this representation is derived from an extended discrete wavelet frame with variable resampling strategies, and it corresponds to an optimal strategy. The proposed method lends itself well to rapid fusion for image sequences. Experimental results tested on different types of imagery show that the proposed method, as a shift-invariant scheme, provides better results than conventional wavelet methods and it is much more efficient than existing shift-invariant methods (the undecimated wavelet and the dual-tree complex wavelet methods).

Texture Analysis and Radial Basis Function Approximation for IVUS Image Segmentation

>Intravascular ultrasound (IVUS) has become in the last years an important tool in both clinical and research applications. The detection of lumen and media-adventitia borders in IVUS images represents a first necessary step in the utilization of the IVUS data for the 3D reconstruction of human coronary arteries and the reliable quantitative assessment of the atherosclerotic lesions. To serve this goal, a fully automated technique for the detection of lumen and media-adventitia boundaries has been developed. This comprises two different steps for contour initialization, one for each corresponding contour of interest, based on the results of texture analysis, and a procedure for approximating the initialization results with smooth continuous curves. A multilevel Discrete Wavelet Frames decomposition is used for texture analysis, whereas Radial Basis Function approximation is employed for producing smooth contours. The proposed method shows promising results compared to a previous approach for texture-based IVUS image analysis.

A multiscale approach to texture-based image retrieval

This paper presents research on a robust technique for texture-based image retrieval in multimedia museum collections. The aim is to be able to use a query image patch containing a single texture to retrieve images containing an area with similar texture to that in the query. The feature extractor used to build the feature vectors is based on an improved version of the discrete wavelet frames (DWF), proposed elsewhere. In order to utilise the feature extractor on real scene image datasets, a block-oriented decomposition technique, termed the multiscale sub-image matching method, is presented. The multiscale method, together with the DWF, provide an efficient content-based retrieval technique without the need for segmentation. The algorithms are tested on a range of databases of texture images as well as on real museum image collections. Promising results are reported.

Automatic texture segmentation for content-based image retrieval application

In this article, a brief review on texture segmentation is presented, before a novel automatic texture segmentation algorithm is developed. The algorithm is based on a modified discrete wavelet frames and the mean shift algorithm. The proposed technique is tested on a range of textured images including composite texture images, synthetic texture images, real scene images as well as our main source of images, the museum images of various kinds. An extension to the automatic texture segmentation, a texture identifier is also introduced for integration into a retrieval system, providing an excellent approach to content-based image retrieval using texture features.

Small infrared target fusion detection based on support vector machines in the wavelet domain

A novel method for fusion detection of small infrared targets based on support vector machines (SVM) in the wavelet domain is presented. Target detection task plays an important role in automatic target recognition (ATR) systems because overall ATR performance depends closely on detection results. SVM is a powerful methodology for solving problems in nonlinear classification, function estimation and density estimation. Least-squares support vector machines (LS-SVMs) are reformulations to standard SVMs. The proposed algorithm can be divided into four steps. First, each frame of the image sequence is decomposed by the discrete wavelet frame (DWF). Second, the components with low frequency are performed by regression based on LS-SVM. The one-order partial derivatives in row and column directions are derived. Therefore, feature images of the gradient strength can be obtained. Third, feature images of five consecutive frames are fused to accumulate the energy of target of interest and greatly reduce false alarms. Finally, the segmentation method based on contrast between target and background is utilized to extract the target. In terms of connectivity of moving targets, the majority of residual clutter and false alarms that survive are removed based on 3-D morphological dilation across three consecutive frames along the motion direction of the moving targets. Actual infrared image sequences in backgrounds of sea and sky are applied to validate the proposed approach. Experimental results demonstrate the robustness of the proposed method with high performance.

Stereographic wavelet frames on the sphere

In this paper we exploit the continuous wavelet transform (CWT) on the two-dimensional sphere S 2 , introduced previously by two of us, to build associated discrete wavelet frames. We first explore half-continuous frames, i.e., frames where the position remains a continuous variable, and then move on to a fully discrete theory. We introduce the notion of controlled frames, which reflects the particular nature of the underlying theory, in particular the apparent conflict between dilation and the compactness of the S 2 manifold. We also highlight some implementation issues and provide numerical illustrations.

Image fusion based on an expectation maximization algorithm

A novel image fusion method based on an expectation maximization (EM) algorithm and the discrete wavelet frame (DWF) transform is proposed. The registered images are first decomposed using the DWF transform, which is both aliasing-free and translation-invariant. The DWF decomposes the image signal into a multiresolution representation with both low-frequency coarse information and high-frequency detail information. The EM algorithm is used to fuse the low-frequency coarse information of the registered images. The informative importance measure is applied to fuse the high-frequency detail information of the registered images. The final fused image is obtained by taking the inverse transform of the composite multiresolution representations. Simulation results show that the proposed method outperforms the conventional image fusion methods.

Fusing Images With Different Focuses Using Support Vector Machines

Many vision-related processing tasks, such as edge detection, image segmentation and stereo matching, can be performed more easily when all objects in the scene are in good focus. However, in practice, this may not be always feasible as optical lenses, especially those with long focal lengths, only have a limited depth of field. One common approach to recover an everywhere-in-focus image is to use wavelet-based image fusion. First, several source images with different focuses of the same scene are taken and processed with the discrete wavelet transform (DWT). Among these wavelet decompositions, the wavelet coefficient with the largest magnitude is selected at each pixel location. Finally, the fused image can be recovered by performing the inverse DWT. In this paper, we improve this fusion procedure by applying the discrete wavelet frame transform (DWFT) and the support vector machines (SVM). Unlike DWT, DWFT yields a translation-invariant signal representation. Using features extracted from the DWFT coefficients, a SVM is trained to select the source image that has the best focus at each pixel location, and the corresponding DWFT coefficients are then incorporated into the composite wavelet representation. Experimental results show that the proposed method outperforms the traditional approach both visually and quantitatively.

Color and Texture Image Retrieval Using Chromaticity Histograms and Wavelet Frames

In this paper, we explore image retrieval mechanisms based on a combination of texture and color features. Texture features are extracted using Discrete Wavelet Frames (DWF) analysis, an over-complete decomposition in scale and orientation. Two-dimensional (2-D) or one-dimensional (1-D) histograms of the CIE Lab chromaticity coordinates are used as color features. The 1-D histograms of the a, b coordinates were modeled according to the generalized Gaussian distribution. The similarity measure defined on the feature distribution is based on the Bhattacharya distance. Retrieval benchmarking is performed over the Brodatz album and on images from natural scenes, obtained from the VisTex database of MIT Media Laboratory and from the Corel Photo Gallery. As a performance indicator recall (relative number of correct images retrieved) is measured on both texture and color separately and in combination. Experiments show this approach to be as effective as other methods while computationally more tractable.

Texture classification using the support vector machines

In recent years, support vector machines (SVMs) have demonstrated excellent performance in a variety of pattern recognition problems. In this paper, we apply SVMs for texture classification, using translation-invariant features generated from the discrete wavelet frame transform. To alleviate the problem of selecting the right kernel parameter in the SVM, we use a fusion scheme based on multiple SVMs, each with a different setting of the kernel parameter. Compared to the traditional Bayes classifier and the learning vector quantization algorithm, SVMs, and, in particular, the fused output from multiple SVMs, produce more accurate classification results on the Brodatz texture album.

Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images

In this paper, we propose a pixel level image fusion algorithm for merging Landsat thematic mapper (TM) images and SPOT panchromatic images. The two source images are first decomposed using the discrete wavelet frame transform (DWFT), which is both aliasing free and translation invariant. Wavelet coefficients from TM's approximation subband and SPOT's detail subbands are then combined together, and the fused image is reconstructed by performing the inverse DWFT. Experimental results show that the proposed approach outperforms methods based on the intensity-hue-saturation transform, principal component analysis and discrete wavelet transform in preserving spectral and spatial information, especially in situations where the source images are not perfectly registered.

Doppler ultrasound signal denoising based on wavelet frames.

A novel approach was proposed to denoise the Doppler ultrasound signal. Using this method, wavelet coefficients of the Doppler signal at multiple scales were first obtained using the discrete wavelet frame analysis. Then, a soft thresholding-based denoising algorithm was employed to deal with these coefficients to get the denoised signal. In the simulation experiments, the SNR improvements and the maximum frequency estimation precision were studied for the denoised signal. From the simulation and clinical studies, it was concluded that the performance of this discrete wavelet frame (DWF) approach is higher than that of the standard (critically sampled) wavelet transform (DWT) for the Doppler ultrasound signal denoising.

Classification of Rotated Textures using Overcomplete Wavelet Frames

In this paper we present an approach to characterize textures at multiple scales using wavelet transforms and propose a texture classification algorithm that is invariant to rotation and translation. The nonseparable discrete wavelet frame is used as the wavelet transform that decompose the texture images into a set of frequency channels. In each channel we take the variance as feature. Classification experiments using twenty Brodazt textures indicate that texture signatures based on wavelet frame analysis are beneficial for accomplishing subtle discrimination of textures and robust classification against rotation and translation.

Texture classification using rotated wavelet filters

We propose an approach to the texture classification problem using a set of two-dimensional (2-D) wavelet filters that are nonseparable and oriented for improved characterization of diagonally oriented textures. Channel energies are estimated at the output of both the new filter bank and a standard discrete wavelet frames (DWF) filter bank. Classification results obtained using each individual method and in combination are presented. The results show that the oriented filter set results in finer discrimination providing complementary texture information to the DWF by making use of its orientation selectivity. As a result, a combination of the features from the output of two filter banks improved the classification accuracy significantly with a smaller number of features.