Contourlet

About: Contourlet is a research topic. Over the lifetime, 3533 publications have been published within this topic receiving 38980 citations.

Video shot boundary detection using multiscale geometric analysis of nsct and least squares support vector machine

Abstract: The fundamental step in video content analysis is the temporal segmentation of video stream into shots, which is known as Shot Boundary Detection (SBD). The sudden transition from one shot to another is known as Abrupt Transition (AT), whereas if the transition occurs over several frames, it is called Gradual Transition (GT). A unified framework for the simultaneous detection of both AT and GT have been proposed in this article. The proposed method uses the multiscale geometric analysis of Non-Subsampled Contourlet Transform (NSCT) for feature extraction from the video frames. The dimension of the feature vectors generated using NSCT is reduced through principal component analysis to simultaneously achieve computational efficiency and performance improvement. Finally, cost efficient Least Squares Support Vector Machine (LS-SVM) classifier is used to classify the frames of a given video sequence based on the feature vectors into No-Transition (NT), AT and GT classes. A novel efficient method of training set generation is also proposed which not only reduces the training time but also improves the performance. The performance of the proposed technique is compared with several state-of-the-art SBD methods on TRECVID 2007 and TRECVID 2001 test data. The empirical results show the effectiveness of the proposed algorithm.

Electroencephalogram signal classification based on shearlet and contourlet transforms

Abstract: Detection of epilepsy patterns in EEG signals with high accuracy.Development of a novel methodology based on curvelet and shearlet transforms.Extraction of a set of discriminative characteristics from the signals.Evaluation on a public data set.Results superior/comparable to the literature. Epilepsy is a disorder that affects approximately 50 million people of all ages, according to World?Health Organization?(2016), which makes it one of the most common neurological diseases worldwide. Electroencephalogram (EEG) signals have been widely used to detect epilepsy and other brain abnormalities. In this work, we propose and evaluate a novel methodology based on shearlet and contourlet transforms to decompose the EEG signals into frequency bands. A set of features are extracted from these time-frequency coefficients and used as input to different classifiers. Experiments are conducted on a public data set to demonstrate the effectiveness of the proposed classification method. The developed system can help neurophysiologists identify EEG patterns in epilepsy diagnostic tasks.

Contourlet-based mammography mass classification

Abstract: The research presented in this paper is aimed at the development of an automatic mass classification of mammograms. This paper focuses on using contourlet-based multi-resolution texture analysis. The contourlet transform is a new two-dimensional extension of the wavelet transform using multi-scale framework as well as directional filter banks. The proposed method consists of three steps: removing pectoral muscle and segmenting regions of interest, extracting the most discriminative texture features based on the contourlet coefficients, and finally creating a classifier, which identifies various tissues. In this research classification is performed based on the idea of Successive Enhancement Learning (SEL) weighted Support Vector Machine (SVM). The main contribution of this work is exploiting the superiority of the contourlets to the-state-of-the-art multi-scale techniques. Experimental results show that contourlet-based feature extraction in conjunction with the SEL weighted SVM classifier significantly improves breast mass detection.

Image denoising algorithm based on contourlet transform for optical coherence tomography heart tube image.

Abstract: Optical coherence tomography (OCT) is becoming an increasingly important imaging technology in the Biomedical field. However, the application of OCT is limited by the ubiquitous noise. In this study, the noise of OCT heart tube image is first verified as being multiplicative based on the local statistics (i.e. the linear relationship between the mean and the standard deviation of certain flat area). The variance of the noise is evaluated in log-domain. Based on these, a joint probability density function is constructed to take the inter-direction dependency in the contourlet domain from the logarithmic transformed image into account. Then, a bivariate shrinkage function is derived to denoise the image by the maximum a posteriori estimation. Systemic comparative experiments are made to synthesis images, OCT heart tube images and other OCT tissue images by subjective assessment and objective metrics. The experiment results are analysed based on the denoising results and the predominance degree of the proposed algorithm with respect to the wavelet-based algorithm. The results show that the proposed algorithm improves the signal-to-noise ratio, whereas preserving the edges and has more advantages on the images containing multi-direction information like OCT heart tube image.