Topic
Contourlet
About: Contourlet is a research topic. Over the lifetime, 3533 publications have been published within this topic receiving 38980 citations.
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Papers
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TL;DR: A new algorithm is proposed for separation of machine-printed and handwritten texts using correlation coefficients and probabilities-based moments features and it provides a better text separation performance compared to that of other state-of-the-art approaches.
Abstract: To make paperless environment in office, document image analysis, where optical character recognition is mostly used, plays a major role. The documents such as bank cheques, admission forms, application forms, memorandums and letters generally consist of text material in mixed form, i.e., handwritten and machine-printed texts along with some noises. Because of this mixture, significant issues raise in the recognition process. By separating out handwritten and machine-printed texts, a solution is offered to overcome this problem. In this paper, a new algorithm is proposed for separation of machine-printed and handwritten texts using correlation coefficients and probabilities-based moments features. Contourlet transform is used to extract these significant features because it has excellent directional and isotropic properties. Finally, set of support vector machines classifiers is used to identify machine-printed text, handwritten text and noise. Comprehensive experiments on different databases show that the proposed algorithm is robust and it provides a better text separation performance compared to that of other state-of-the-art approaches. In benchmarking analysis, maximum identification recall rate of 98.9% is obtained from the proposed technique, which demonstrates its effectiveness.
11 citations
Patent•
14 Jan 2015
TL;DR: In this paper, a contourlet transformation-adaptive medical image fusion method based on non-sampling is proposed, which is very effective and correct, the edge and space texture information of the fused image is clear, color distortion is low, the false contour phenomenon does not exist, and feature information is well reserved.
Abstract: The invention relates to a contourlet transformation-adaptive medical image fusion method based on non-sampling and belongs to the field of image processing. The method comprises the steps that firstly, a source image is subjected to arithmetic average filtering and then is decomposed through an orthogonal 9-7 wavelet filter and a pkva filter during non-sampling to obtain low-frequency sub-band coefficients and all band-pass direction sub-band coefficients; secondly, the low-frequency sub-band coefficients are selected and fused according to the edge information maximum criterion, all the band-pass sub-band coefficients are selected and fused through an adaptive PCNN model based on a visual neuron model; lastly, a final fused image is obtained by means of inverse transformation of NSCT. According to the contourlet transformation-adaptive medical image fusion method based on non-sampling, the algorithm is very effective and correct, the edge and space texture information of the fused image is clear, color distortion is low, the false contour phenomenon does not exist, and feature information of the source image is well reserved.
11 citations
15 Nov 2013
TL;DR: A novel idea to hide secret data in contour let domain in order to have secure communication in the presence of steganalyzer is presented and the cover selection criteria based on contrast measurement is adopted.
Abstract: Steganography, the science of invisible communication is one of the branches of information hiding. It allows secret data transmission and hides the existence of message itself so as to protect the transmitted information from unintended recipient. In past decade, a lot of research has been done on various steganography schemes in spatial and transform domain. In this article, we present a novel idea to hide secret data in contour let domain. In order to have secure communication in the presence of steganalyzer, we have adopted the cover selection criteria based on contrast measurement. Using contrast measurement, suitable cover is chosen from standard test image database and then embedding is carried out in contour let sub bands of cover image. Embedding data in suitable cover image will enhance system performance and results in more secured steganography. The efficiency of the employed method is illustrated using image quality metrics.
11 citations
TL;DR: A novel directional lifting image coder locally adapting the filtering directions to image content is presented and has a better performance in terms of peak signal to noise ratio (PSNR) and subjective quality.
Abstract: Directional wavelet can effectively capture the directional dependence in images. However, the computational complexity is high. Based on the image statistics estimated by the structure tensor, a novel directional lifting image coder locally adapting the filtering directions to image content is presented. Before performing wavelet transform (WT), the proposed algorithm detects all the image blocks in a given image to decide whether the block is homogenous or not. For homogeneous block, the conventional 2-D discrete WT is used. This will considerably reduce the computational complexity and the number of bits needed to code the directional information. On the other hand, heterogeneous block is decomposed using directional lifting wavelet transform, which can effectively capture the directional dependence in the selected image and improve the coding gain of the image coder. Experimental results have shown that, compared to some existing methods, the proposed scheme has a better performance in terms of peak signal to noise ratio (PSNR) and subjective quality.
11 citations
24 Jun 2014
TL;DR: The proposed denoising methods demonstrate contourlets and curvelets as a viable alternative to the DWT and FFT during signal processing and initial results indicate that the contourlet and curvelet methods yield a higher PSNR and lower error than the DWt and F FT for 1D data.
Abstract: Fast Fourier Transforms (FFTs) and Discrete Wavelet Transformations (DWTs) have been routinely used as methods of denoising signals. DWT limitations include the inability to detect contours, curves and directional information of multi-dimensional signals. In the past decade, two new approaches have surfaced: curvelets, developed by Candes; and contourlets, developed by Do et al. The typical applications of contourlets and curvelets include two-dimensional image data denoising. We explore the use of curvelets and contourlets to the one-dimensional (1D) denoising problem. Working with seismic data, we introduce various types of data noise and the wavelet, curvelet, and contourlet transforms are applied to each signal. We tested multiple decomposition levels and different thresholding values. The benchmark for determining the effectiveness of each transform is the peak signal-to-noise ratio (PSNR) between the original signal and the denoised signal. The proposed denoising methods demonstrate contourlets and curvelets as a viable alternative to the DWT and FFT during signal processing. The initial results indicate that the contourlet and curvelet methods yield a higher PSNR and lower error than the DWT and FFT for 1D data.
11 citations