基于混沌序列的视频图像数字水印隐藏与多分辨检测

基于混沌序列的优良性能和视频信号多分辨小波表示的特点 ,提出了一种视频图像混沌水印隐藏及其多分辨检测方法 .首先利用小波变换对图像进行多级分解 ;其次将混沌数字水印叠加在从低分辨率到高分辨率的高频小波系数里 ;最后进行逆小波变换生成水印图像 .其特点是混沌水印安全性能高 ,不易破译 ,可以进行多分辨检测 ,节约计算量 .实验表明 ,对于常见的图像处理和噪声干扰 ,多分辨检测方法仍能识别隐藏的水印 ,具有很好的鲁棒性 ,而且该方法可以通过三维小波变换很容易地推广到视频序列图像的水印隐藏应用中去 .

小波变换在原子钟相频跳变检测中的应用

原子钟相频跳变是制约标准时间信号准确度和稳定度的关键因素之一,它的分析和检测在守时系统自动化监控中非常重要。本文基于小波去噪和多尺度分辨方法,较好地解决了原子钟相频跳变的检测问题,研究成果具有较高的参考价值。

MIXED SCHEME FOR IMAGE EDGE DETECTION BASED ON WAVELET TRANSFORM

A mixed scheme based on Wavelet Transformation (WT) is proposed for image edge detection. The scheme combines the wavelet transform and traditional Sobel and LoG (Laplacian of Gaussian) operator edge-detection algorithms. The precise theory analysis is given to show that the wavelet transformation has an advantage for signal processing. Simulation results show that the new scheme is better than only using the Sobel or LoG methods. Complexity analysis is also given and the conclusion is acceptable, therefore the proposed scheme is effective for edge detection.

Moving object detection method based on complementary multi resolution background models

A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion,thermal and dynamic background sequences.Two groups of complementary Gaussian mixture models were used.The ghost and real static object could be classified by comparing the similarity of the edge images further.In each group,the multi resolution Gaussian mixture models were used and dual thresholds were applied in every resolution in order to get a complete object mask without much noise.The computational color model was also used to depress illustration variations and light shadows.The proposed method was verified by the public test sequences provided by the IEEE Change Detection Workshop and compared with three state-of-the-art methods.Experimental results demonstrate that the proposed method is better than others for all of the evaluation parameters in intermittent object motion sequences.Four and two in the seven evaluation parameters are better than the others in thermal and dynamic background sequences,respectively.The proposed method shows a relatively good performance,especially for the intermittent object motion sequences.

Field Experiments of Multi-Channel Oceanographic Fluorescence Lidar for Oil Spill and Chlorophyll-a Detection

A Multi-channel Oceanographic Fluorescence Lidar(MOFL), with a UV excitation at 355 nm and multiple receiving channels at typical wavelengths of fluorescence from oil spills and chlorophyll-a(Chl-a), has been developed using the Laser- induced Fluorescence(LIF) technique. The sketch of the MOFL system equipped with a compact multi-channel photomultiplier tube(MPMT) is introduced in the paper. The methods of differentiating the oil fluorescence from the background water fluorescence and evaluating the Chl-a concentration are described. Two field experiments were carried out to investigate the field performance of the system, i.e., an experiment in coastal areas for oil pollution detection and an experiment over the Yellow Sea for Chl-a monitoring. In the coastal experiment, several oil samples and other fluorescence substances were used to analyze the fluorescence spectral characteristics for oil identification, and to estimate the thickness of oil films at the water surface. The experiment shows that both the spectral shape of fluorescence induced from surface water and the intensity ratio of two channels(I495/I405) are essential to determine oil-spill occurrence. In the airborne experiment, MOFL was applied to measure relative Chl-a concentrations in the upper layer of the ocean. A comparison of relative Chl-a concentration measurements by MOFL and the Moderate Resolution Imaging Spectroradiometer(MODIS) indicates that the two datasets are in good agreement. The results show that the MOFL system is capable of monitoring oil spills and Chl-a in the upper layer of ocean water.

Multiresolution edge detection based on modified bubble function and SWT

A new way based on a modified bubble function and stationary wavelet transform(SWT) is proposed to solve the problem that the conventional edge detection algorithms are sensitive to the noises.Firstly,the traditional bubble function is modified in order to get different time-frequency domain responses and to get filtering effects through adjusting the parameters. Secondly, the modified bubble function is combined with SWT to construct a multiresolution network. By using the modified bubble function to enhance the edges and by using SWT to reduce the noises, the edges can be extracted accurately,effectively and quickly with lower noise.Finally, the experimental results of the proposed edge detection algorithm are verified to be better than that with the traditional bubble function.

A Wavelet-Based Technique for Distribution Networks

This paper presents a wavelet-based technique for detection and classification of normal and abnormal conditions that occur on power distribution lines. The proposed technique depends on a sensitive fault detection parameter （denoted DET） calculated from the wavelet multi-resolution decomposition of the three phase currents only. This parameter is fast and sensitive to any small changes in the current signal since it uses the square of the first and second details of the decomposed signals. The simulation results of this study clearly show that the proposed technique can be successfully used to detect and classify not only low-current faults that could not be detected by conventional overcurrent relays but also normal transients like load switching and inrush currents.

Research on the detecting methods of singularity in deformation signal based on two kinds of wavelet entropy

There are various influencing factors that affect the deformation observation, and deformation signals show differ- ent characteristics under different scales. Wavelet analysis possesses multi-scale property, and the information entropy has great representational capability to the complexity of information. By hamming window to the wavelet coefficients and windowed wavelet energy obtained by multi-resolution analysis （MRA）, it can be achieved to measure the wavelet time entropy （WTE） and wavelet energy entropy （WEE）. The paper established deformation signals, selected the parameters, and compared the sin- gularity detection ability and anti-noise ability of two kinds of wavelet entropy and applied them to the singularity detection at the GPS continuously operating reference stations. It is shown that the WTE performs well in weak singularity information de- tection in finite frequency components signals and the WEE is more suitable for detecting the singularity in the signals with complex, strong background noise.