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Contourlet

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


Papers
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Journal ArticleDOI
TL;DR: It’s time to dust off that paintbrush and dust off those brushes!
Abstract: 激光与光电子学进展 2009.09 1 引言 随着科学技术的发展,超大规模集成(VLSI)和超 高速集成电路(VHSIC)、高精度数控机床、计算机辅 助设计和制造及其他设计和生产工艺的改进,传感器 在分辨率、反应时间、探测性能等方面有了很大提高。 各种面向复杂应用的多传感器信息系统随之大量出 现,系统的信息来自多个传感器或称为信息具有多源 特性。这时,对信息容量及信息处理速度的要求大大 超出以前系统的工作能力,原有的处理方法已不再适 应这种要求,多传感器数据融合(信息融合)技术就是 为解决这一问题而产生的新的处理方法[1]。 通过将不同传感器获得的同一场景的图像或同 一传感器在不同时间获得的同一场景的图像,经过去 噪、配准重采样后,再运用某种融合技术以得到一幅 合成图像的过程称为多传感器图像融合。它从多信息 的视角进行处理及综合,得到各种信息的内在联系和 规律,从而剔除无用的和错误的信息,保留正确的和 有用的成分,最终实现信息的优化。相对于单个传感 器信号的信息来讲,由于来自多个传感器的信号所提 供的信息具有冗余性、互补性、实时性和信息获取的 基于小波 -Contourlet 变换的多传 感器图像融合 Multisensor Image Fusion Using Wavelet Based on Contourlet Transform

8 citations

Journal ArticleDOI
TL;DR: A shift-invariant anisotropic Contourlet transform (CT) is used to decompose the retinal image into subbands and a new nonlinear method is applied over the subbands to modify the CT coefficients, followed by inverse CT.
Abstract: Low-contrast retinal images have to be enhanced for good visual perception to aid in retinal vessel analysis. Classical sharpening enhancement techniques such as unsharp masking (USM) improve the contrast and bring out the information along with noise. This article uses a shift-invariant anisotropic Contourlet transform (CT) to decompose the retinal image into subbands. A new nonlinear method is applied over the subbands to modify the CT coefficients, followed by inverse CT. The proposed method is compared with a nonlinear USM (NLUSM) technique and wavelet transform-based method. The objective performance is measured in terms of enhancement measure. We observed that the proposed methodology provides better result. We demonstrate that this sharpening algorithm can be used as a preprocessing step to (i) adaptive histogram equalization and (ii) retinal vessel extraction. Pratt's figure of merit was used to analyze the vessel extracted from the retinal images with their ground truth that were obtained from STARE and DRIVE databases.

8 citations

Proceedings ArticleDOI
03 Dec 2010
TL;DR: Experiments on the CASIA iris image database show that the proposed iris recognition method based on the CT and BPR can achieve promising results compared with other methods.
Abstract: Iris recognition has been a hot research topic in these years. In this paper, an iris recognition method based on the the Contourlet Transform (CT) and Biomimetic Pattern Recognition (BPR) has been proposed. In proposed method, the Contourlet Transform was used to extract the significant features of the preprocessed iris image, and the Biomimetic Pattern Recognition algorithm was used to construct the high-dimensional covering hypersurface in order to recognize the iris images. Experiments on the CASIA iris image database show that the proposed iris recognition method based on the CT and BPR can achieve promising results compared with other methods.

8 citations

Journal ArticleDOI
TL;DR: Among the four classes of driving postures, the class of grasping the steering wheel is the most difficult to recognize and the proposed CF-RSE approach is effective and hence has great promises in developing a successful HDAS.
Abstract: In order to develop Human-centered Driver Assistance Systems (HDAS), an efficient Combined Feature (CF) extraction approach from Contourlet Transform (CT) and Edge Orientation Histogram (EOH) is proposed for vehicle driving posture descriptions. A Random Subspace Ensemble (RSE) of Intersection Kernel Support Vector Machines (IKSVMs) is then exploited as the base classifier. Four testing driving postures are grasping the steering wheel, operating the shift lever, eating a cake, and talking on a cellar phone. On a dedicated Southeast University Driving Posture (SEU-DP) Database, the holdout and cross-validation experiments were conducted. The experimental results show that the proposed CF-RSE approach outperforms single Contourlet-IKSVM, EOH-IKSVM recognition strategies. With CF-RSE, the average classification accuracies of four driving posture classes are over 90%. Among the four classes of driving postures, the class of grasping the steering wheel is the most difficult to recognize and the proposed approach achieved over 85% accuracy in both experiments. These encouraging results show that the proposed CF-RSE approach is effective and hence has great promises in developing a successful HDAS.

8 citations

Journal Article
Wang Zulin1
TL;DR: A novel nonsubsampled contourlet (NSCT) image denosing algorithm based on inter-scale correlations is presented that is superior both in signal to noise ratio (SNR) and edge preservation.
Abstract: Combining the threshold denoising,we present a novel nonsubsampled contourlet(NSCT) image denosing algorithm based on inter-scale correlations.The simulation results have shown that the performance of the above method is superior both in signal to noise ratio(SNR) and edge preservation.

8 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202336
202299
202175
2020109
2019155
2018164