Wavelet-based Image Splicing Forgery Detection

Digital image processing is a progressive field which has made development over period of time in a way that it becomes easy to play with artifacts of image by manipulating them using transformation such as copy-paste, copy-move, rotation, smoothing of boundaries, scaling, color enhancing, resizing, addition of noise, blurring, compressing etc. Forgery performed with a digital image, raising a doubt about the authenticity of it. Image splicing is one of the most used method for tampering an image by compositing two or many image fragments to create a spliced image. In this paper, a wavelet-based mechanism is proposed to detect image splicing forgery by taking edge information of an image as a distinguishing feature by performing edge analysis using wavelet transform. Haar-based Discrete Wavelet Transform (DWT) is used for edge analysis that decompose an image into four sub-images and it followed by Speed-Up Robust Feature (SURF) method which is a keypoint-based feature extractor technique. SURF extracts features from the decomposed images of DWT and used that features for performing classification using SVM linear classifier

Wavelet: a new tool for business cycle analysis

One basic problem in business-cycle studies is how to deal with nonstationary time series. The market economy is an evolutionary system. Economic time series therefore contain stochastic components that are necessarily time dependent. Traditional methods of business cycle analysis, such as the correlation analysis and the spectral analysis, cannot capture such historical information because they do not take the time-varying characteristics of the business cycles into consideration. In this paper, we introduce and apply a new technique to the studies of the business cycle: the wavelet-based time-frequency analysis that has recently been developed in the field of signal processing. This new method allows us to characterize and understand not only the timing of shocks that trigger the business cycle, but also situations where the frequency of the business cycle shifts in time. Our empirical analyses show that 1973 marks a new era for the evolution of the business cycle.Business cycles

Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation

Copyright @ 2011 Shadi AlZubi et al. This article has been made available through the Brunel Open Access Publishing Fund.The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from different medical scanners such as PET, CT, or MRI. Multiresolution analysis (MRA) using wavelet, ridgelet, and curvelet transforms has been used in the proposed segmentation system. It is particularly a challenging task to classify cancers in human organs in scanners output using shape or gray-level information; organs shape changes throw different slices in medical stack and the gray-level intensity overlap in soft tissues. Curvelet transform is a new extension of wavelet and ridgelet transforms which aims to deal with interesting phenomena occurring along curves. Curvelet transforms has been tested on medical data sets, and results are compared with those obtained from the other transforms. Tests indicate that using curvelet significantly improves the classification of abnormal tissues in the scans and reduce the surrounding noise