Harmonic wavelet transform

About: Harmonic wavelet transform is a research topic. Over the lifetime, 9602 publications have been published within this topic receiving 247336 citations.

The Monogenic Wavelet Transform

Abstract: This paper extends the 1-D analytic wavelet transform to the 2-D monogenic wavelet transform. The transformation requires care in its specification to ensure suitable transform coefficients are calculated, and it is constructed so that the wavelet transform may be considered as both local and monogenic. This is consistent with defining the transform as a real wavelet transform of a monogenic signal in analogy with the analytic wavelet transform. Classes of monogenic wavelets are proposed with suitable local properties. It is shown that the monogenic wavelet annihilates anti-monogenic signals, that the monogenic wavelet transform is phase-shift covariant and that the transform magnitude is phase-shift invariant. A simple form for the magnitude and orientation of the isotropic transform coefficients of a unidirectional signal when observed in a rotated frame of reference is derived. The monogenic wavelet ridges of local plane waves are given.

Feature extraction of brain mri by stationary wavelet transform and its applications

Abstract: Wavelet transform is widely used in feature extraction of magnetic resonance imaging. However, the traditional discrete wavelet transform (DWT) suffers from translation variant property, which may extract significantly different features from two images of the same subject with only slight movement. In order to solve this problem, this paper utilizes stationary wavelet transform (SWT) to extract features instead of DWT. Experiments on a normal brain MRI demonstrate that wavelet coefficients via SWT are superior to those via DWT, in terms of translation invariant property. In addition, we applied SWT to normal and abnormal brain classification. The results demonstrate that SWT-based classifier is more accurate than that of DWT.

Block-based and multi-resolution methods for ear recognition using wavelet transform and uniform local binary patterns

Abstract: This paper proposes a novel method based on Haar wavelet transform and uniform local binary patterns (ULBPs) to recognize ear images. Firstly, ear images are decomposed by Haar wavelet transform. Then ULBPs are combined simultaneously with block-based and multi-resolution methods to describe together the texture features of ear sub-images transformed by Haar wavelet. Finally, the texture features are classified by the nearest neighbor method. Experimental results show that Haar wavelet transform can boost effectively up intensity information of texture unit. It is not only fast but also robust to use ULBPs to extract texture features. The recognition rates of the method proposed by this paper outperform remarkably those of the classic PCA or KPCA especially when combining block-based and multi-resolution methods.

Lifting-Based Directional DCT-Like Transform for Image Coding

Abstract: Traditional 2-D discrete cosine transform (DCT) implemented by separable 1-D transform in horizontal and vertical directions does not take image orientation features in a local window into account. To improve it, we propose to introduce directional primary operations to the lifting-based DCT and thereby derive a new directional DCT-like transform, whose transform matrix is dependent on directional angle and interpolation used there. Furthermore, the proposed transform is compared with the straightforward one of first rotated and then transformed. A JPEG-wise image coding scheme is also proposed to evaluate the performance of the proposed directional DCT-like transform. The first 1-D transform is performed according to image orientation features, and the second 1-D transform still in the horizontal or vertical direction. At the same time, an approach is proposed to optimally select transform direction of each block because selected directions of neighboring blocks will influence each other. The experimental results show that the performance of the proposed directional DCT-like transform can dramatically outperform the conventional DCT up to 2 dB even without modifying entropy coding.