Medical image classification as an important research topic both in image processing and biomedical engineering. The ridgelet transform has good directional selective ability to locally and sparsely in representing the image compared with the traditional wavelet transform. This paper proposes a novel classification model for medical image, which is using ridgelet transform and dynamic fuzzy the...

A spatial classification technique incorporating a novel feature derivation method is proposed for classifying the heterogeneous classes present in the hyper spectral images. The classification accuracy can be improved if and only if both the feature extraction and classifier selection are proper. As the classes present in the hyper spectral image are having different textures, textural classif...

This work presents a new technique that classifies various land cover classes present in the hyperspectral images by making use of the features from transformed version of the input image. A hyperspectral image is characterized by a large dimensionality data, recorded at very fine spatial resolution in hundreds of narrow frequency bands. These bands provide a wealth of spatial and spectral info...

Medical image classification as an important research topic both in image processing and biomedical engineering. The ridgelet transform has good directional selective ability to locally and sparsely in representing the image compared with the traditional wavelet transform. This paper proposes a novel classification model for medical image, which is using ridgelet transform and dynamic fuzzy the...

A spatial classification technique incorporating a novel feature derivation method is proposed for classifying the heterogeneous classes present in the hyper spectral images. The classification accuracy can be improved if and only if both the feature extraction and classifier selection are proper. As the classes present in the hyper spectral image are having different textures, textural classif...

Texture analysis is an important characteristic for automatic visual inspection for surface and object identification from medical images and other type of images. This paper presents an application of wavelet extension and Gray level cooccurrence matrix (GLCM) for diagnosis of myocardial infarction tissue from echocardiography images. Many of applications approach have provided good result in ...

We propose a new framework for manifold denoising based on processing in the graph Fourier frequency domain, derived from the spectral decomposition of the discrete graph Laplacian. Our approach uses the Spectral Graph Wavelet transform in order to perform non-iterative denoising directly in the graph frequency domain, an approach inspired by conventional wavelet-based signal denoising methods....

Vulnerabilities in biometric systems including spoofing has emerged as an important issue. The focus of this work is on characterization of ‘perspiration pattern’ in a time-series of fingerprint images for liveness detection. By using information in the high pass bands of the images the similarity score for the two images is calculated to determine the uniqueness of the perspiration pattern. In...

An image compression method combining discrete wavelet transform (DWT) and vector quantization (VQ) is presented. First, a three-level DWT is performed on the original image resulting in ten separate sub bands. These sub bands are then vector quantized. VQ indices are Huffman coded to increase the compression ratio. Lloyd extended scalar quantization technique is used to design memory less vect...

The paper develops an approach based on wavelet technique for the evaluation and estimation of harmonic contents of power system waveform. The proposed algorithm decomposes the signal waveforms into the uniform frequency sub-bands corresponding to the odd harmonic components of the signal. The proposed implementation of algorithm determines the frequency bands of harmonics which retain both the...