An adaptive image steganographic scheme based on Noise Visibility Function and an optimal chaotic based encryption method

Steganography is the science of hiding secret message in an appropriate digital multimedia in such a way that the existence of the embedded message should be invisible to anyone apart from the sender or the intended recipient. This paper presents an irreversible scheme for hiding a secret image in the cover image that is able to improve both the visual quality and the security of the stego-image while still providing a large embedding capacity. This is achieved by a hybrid steganography scheme incorporates Noise Visibility Function (NVF) and an optimal chaotic based encryption scheme. In the embedding process, first to reduce the image distortion and to increase the embedding capacity, the payload of each region of the cover image is determined dynamically according to NVF. NVF analyzes the local image properties to identify the complex areas where more secret bits should be embedded. This ensures to maintain a high visual quality of the stego-image as well as a large embedding capacity. Second, the security of the secret image is brought about by an optimal chaotic based encryption scheme to transform the secret image into an encrypted image. Third, the optimal chaotic based encryption scheme is achieved by using a hybrid optimization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) which is allowing us to find an optimal secret key. The optimal secret key is able to encrypt the secret image so as the rate of changes after embedding process be decreased which results in increasing the quality of the stego-image. In the extracting process, the secret image can be extracted from the stego-image losslessly without referring to the original cover image. The experimental results confirm that the proposed scheme not only has the ability to achieve a good trade-off between the payload and the stego-image quality, but also can resist against the statistics and image processing attacks.     

Change detection in synthetic aperture radar images based on unsupervised artificial immune systems

In this paper, we propose a novel change detection method for synthetic aperture radar images based on unsupervised artificial immune systems. After generating the difference image from the multitemporal images, we take each pixel as an antigen and build an immune model to deal with the antigens. By continuously stimulating the immune model, the antigens are classified into two groups, changed and unchanged. Firstly, the proposed method incorporates the local information in order to restrain the impact of speckle noise. Secondly, the proposed method simulates the immune response process in a fuzzy way to get an accurate result by retaining more image details. We introduce a fuzzy membership of the antigen and then update the antibodies and memory cells according to the membership. Compared with the clustering algorithms we have proposed in our previous works, the new method inherits immunological properties from immune systems and is robust to speckle noise due to the use of local information as well as fuzzy strategy. Experiments on real synthetic aperture radar images show that the proposed method performs well on several kinds of difference images and engenders more robust result than the other compared methods.     

ECISER: Efficient Clip-art Image SEgmentation by Re-rasterization

Clip-art image segmentation is widely used as an essential step to solve many vision problems such as colorization and vectorization. Many of these applications not only demand accurate segmentation results, but also have little tolerance for time cost, which leads to the main challenge of this kind of segmentation. However, most existing segmentation techniques are found not sufficient for this purpose due to either their high computation cost or low accuracy. To address such issues, we propose a novel segmentation approach, ECISER, which is well-suited in this context. The basic idea of ECISER is to take advantage of the particular nature of cartoon images and connect image segmentation with aliased rasterization. Based on such relationship, a clip-art image can be quickly segmented into regions by re-rasterization of the original image and several other computationally efficient techniques developed in this paper. Experimental results show that our method achieves dramatic computational speedups over the current state-of-the-art approaches, while preserving almost the same quality of results.     

内插法在相关干涉仪测向体制中的应用

测向误差的减小一直以来都是无线电测向精度提高的一个难点,对于不同的测向体制所采用的减小测向误差的方法都不尽相同。本文主要介绍的是相关干涉仪测向体制的基本原理以及通过内插法的应用来减小测向误差的方法。     

Review of pulsed thermal NDT: Physical principles,theory and data processing

This paper summarizes the basics of pulsed thermal nondestructive testing (TNDT) including theoretical solutions, data processing algorithms and practical implementation. Typical defects are discussed along with 1D analytical and multi-dimensional numerical solutions. Special emphasis is focused on defect characterization by the use of inverse solutions. A list of TNDT terms is provided. Applications of active TNDT, mainly in the aerospace industry, are discussed briefly, and some trends in the further development of this technique are described.     

相关条件下基于N-P准则的分布式检测理论研究

本文针对并行网络、二元局部判决的情况,研究了在相关条件下基于N-P准则的分布式检测融合算法,给出了在联合概率密度已知和未知两种情况下的最优融合规则的理论推导及相应的解决方案,并在实验仿真的基础上,得到了部分有益的结论。     

以一种新的距离度量作为反馈的相关跟踪方法

基于相关的模板匹配方法在图像跟踪中得到了广泛的应用,但是这种方法的主要缺点是对图像噪声非常敏感,而且由这种方法得出的相关曲面比较平坦。为了有效抑制噪声和局部遮挡对图像的影响,提出用鲁棒统计中的一个值代替相关值作为距离度量。它不是计算两幅图像的对应像素差值,而是计算两幅图像中差别不大的对应像素对的个数。实验证明这样得到的曲面相对　　收稿日期:2003 08 31;　修订日期:2003 10 28作者简介:张桂林(1944 ),男,天津人,教授,博士生导师,主要研究方向为自动识别、人体生物统计特征识别、图像处理算法与系统性能评估等。　　　　　　　要尖锐得多。当上述的距离度量应用于跟踪过程中时,模板的合理更新对跟踪非常重要。基于上述的距离度量方法,提出了一种新的模板更新策略。利用两幅图像中近似像素在整修匹配模板像素中所占的比例大小决定加权系数。这种加权策略可以自适应地根据图像序列的变化选择更新相关模板,同时在一定程度上减小模板漂移的危险。     

I/OIMAGESYSTEM微波中继应用

微波中继的应用非常广泛 ,目前在二维、三维施工中跨越障碍物时 ,一般都使用微波中继站来传输命令和数据。文章就微波中继及I/OIMAGESYSTEM“蛇形排列”先进功能的实际应用进行了详细的阐述 ,同时提出了使用微波中继站应注意的问题。