S. Arivazhagan

Texture classification using wavelet transform

Today, texture analysis plays an important role in many tasks, ranging from remote sensing to medical imaging and query by content in large image data bases. The main difficulty of texture analysis in the past was the lack of adequate tools to characterize different scales of textures effectively. The development in multi-resolution analysis such as Gabor and wavelet transform help to overcome this difficulty. This paper describes the texture classification using (i) wavelet statistical features, (ii) wavelet co-occurrence features and (iii) a combination of wavelet statistical features and co-occurrence features of one level wavelet transformed images with different feature databases. It is found that, the results of later method are promising.

Texture classification using Gabor wavelets based rotation invariant features

Texture based image analysis techniques have been widely employed in the interpretation of earth cover images obtained using remote sensing techniques, seismic trace images, medical images and in query by content in large image data bases. The development in multi-resolution analysis such as wavelet transform leads to the development of adequate tools to characterize different scales of textures effectively. But, the wavelet transform lacks in its ability to decompose input image into multiple orientations and this limits their application to rotation invariant image analysis. This paper presents a new approach for rotation invariant texture classification using Gabor wavelets. Gabor wavelets are the mathematical model of visual cortical cells of mammalian brain and using this, an image can be decomposed into multiple scales and multiple orientations. The Gabor function has been recognized as a very useful tool in texture analysis, due to its optimal localization properties in both spatial and frequency domain and found widespread use in computer vision. Texture features are found by calculating the mean and variance of the Gabor filtered image. Rotation normalization is achieved by the circular shift of the feature elements, so that all images have the same dominant direction. The texture similarity measurement of the query image and the target image in the database is computed by minimum distance criterion.

Texture segmentation using wavelet transform

Texture analysis such as segmentation and classification plays a vital role in computer vision and pattern recognition and is widely applied to many areas such as industrial automation, bio-medical image processing and remote sensing. This paper describes a novel technique of feature extraction for characterization and segmentation of texture at multiple scales based on block by block comparison of wavelet co-occurrence features. The performance of this segmentation algorithm is superior to traditional single resolution techniques such as texture spectrum, co-occurrences, local linear transforms, etc. The results of the proposed algorithm are found to be satisfactory.

Fault segmentation in fabric images using Gabor wavelet transform

Gabor wavelets have been successfully applied for a variety of machine vision applications such as Texture segmentation, Edge detection, Boundary detection etc. As the Fourier transform is not suitable for detecting local defects, and the Wavelet transforms posses only limited number of orientations, Gabor wavelet transform is chosen and applied to detect the defects in fabrics. Gabor filters scheme that imitates the early human vision process is applied to the sample under inspection. Defects can be automatically segmented from the regular texture by applying the proposed method. Proper thresholding ensures segmentation of the defect from the texture background. The results obtained using this method confirms its efficiency. This can also be applied to detect defects on surfaces and materials that have regular periodic texture.

Texture classification using Curvelet Statistical and Co-occurrence Features

Texture classification has long been an important research topic in image processing. Now a days classification based on wavelet transform is being very popular. Wavelets are very effective in representing objects with isolated point singularities, but failed to represent line singularities. Recently, ridgelet transform which deal effectively with line singularities in 2D is introduced. But images often contain curves rather than straight lines, so curvelet transform is designed to handle it. It allows representing edges and other singularities along lines in a more efficient way when compared with other transforms. In this paper, the issue of texture classification based on curvelet transform has been analyzed. Curvelet statistical features (CSFs) and curvelet co-occurrence features (CCFs) are derived from the sub-bands of the curvelet decomposition and are used for classification. Experimental results show that this approach allows obtaining high degree of success rate in classification

Texture classification using ridgelet transform

Texture classification has long been an important research topic in image processing. Classification based on the wavelet transform has become very popular. Wavelets are very effective in representing objects with isolated point singularities, but failed to represent line singularities. Recently, a ridgelet transform which deals effectively with line singularities in 2-D is introduced. It allows representing edges and other singularities along lines in a more efficient way. In this paper, the issue of texture classification based on a ridgelet transform has been analyzed. Features are derived from sub-bands of the ridgelet decomposition and are used for classification for a data set containing 20 texture images. Experimental results show that this approach allows to obtain a high degree of success in classification.

Performance Comparison of Discrete Wavelet Transform and Dual Tree Discrete Wavelet Transform for Automatic Airborne Target Detection

Automatic airborne target detection is a challenging task in video surveillance applications. In our paper, an automatic target detection (ATD) algorithm using cooccurrence features, derived from sub-bands of discrete wavelet transform / dual tree discrete wavelet transformed sub blocks to identify the seed sub-block, and then to detect the target using region growing algorithm is presented. Also, the performance of discrete wavelet transform and dual tree discrete wavelet transform for automatic airborne target detection has been compared and presented.

Face Recognition Using Multi-Resolution Transform

Face recognition has wide range potential applications in commercial and law enforcements, such as, security surveillance, telecommunication, human computer interaction. This paper deals with a novel technique of face recognition using multi-resolution transform such as, Gabor wavelet transform. Multi-scale or resolution methods are based on image transformations that analyze the image at multiple resolutions. Gabor wavelet is used to extract the spatial frequency, spatial locality and orientation selectivity from faces irrespective of the variations in the expressions, illumination and pose. Normalization is done to reduce dimensionality which will reduce memory problem and computation time. Principal component analysis (PCA) deals with the decomposition of the training set into the eigenvectors called eigen faces. Then by considering each eigen faces as each co-ordinate, a co-ordinate system is formed called face space. In this face space, each face is considered as a point. All samples in each class forms the cluster of points in the face space. By projecting each faces, its co-ordinate values can be determined, which are later used for distance measures in discrimination analysis. Various discrimination analyzes such as, Euclidean, L1, L2 and cosine similarity are used for the recognition of face images.

Multi-resolution system for artifact removal and edge enhancement in computerized tomography images

The aim of image enhancement means adopting some technical method, which includes algorithm, to stand out the interested characteristics of the image and to restrain some useless characteristics of the image. The image, which has been improved, can satisfy some special analysis better than the original one. In this paper, we propose a new method for ring artifact removal and edge enhancement for industrial CT images based on the multi-resolution techniques such as, discrete wavelet transform (DWT), stationary wavelet transform (SWT) and dual tree complex wavelet transform (DT-CWT). The performance of the proposed method is compared and analyzed in detail and the promising results and findings are presented.

A novel image denoising scheme based on fusing multiresolution and spatial filters

The denoising of natural images corrupted by noise is a long established problem in signal or image processing. This paper proposes an effective denoising scheme to remove Gaussian noise by combining spatial filtering and multiresolution techniques. The spatial filter employed here is Joint Bilateral Filter. The Bilateral Filter is a nonlinear filter that does spatial averaging without smoothing edges; it has shown to be an effective image denoising technique. The Joint Bilateral Filter is similar to Bilateral Filter, but it needs a reference image for the parameter estimation. In the proposed scheme, noise-free image is taken as the reference image. The multiresolution techniques applied in this paper are Wavelet Transform, Contourlet Transform and Non-Subsampled Contourlet Transform. In the transformed domain, Bayes thresholding is performed on the detail subbands, while Joint Bilateral Filter is applied as the pre-filter and post-filter. The performance is evaluated in terms of Peak Signal to Noise Ratio, Image Quality Index and Edge Keeping Index. The experimental results proved that this algorithm is competitive with other denoising schemes.