A high payload frequency-based reversible image hiding method

To provide both a high payload capacity and a good stego-image quality, a high payload frequency-based reversible image hiding (HPFRIH) method was presented in this paper. These performances can be achieved by transforming a cover image from the spatial domain into the frequency domain through Haar digital wavelet transform (HDWT) method followed by an adaptive arithmetic coding method to encode the HDWT coefficients in a high-frequency band. The HPFRIH method was used to conceal both the compressed data and the secret data in the high-frequency band. This simple method can completely reconstruct the cover image only from the stego-image. Moreover, experimental results showed that the HPFRIH method offers both impressive hiding capacity and satisfied stego-image quality.     

智能车竞赛中道路的视频识别方案

视频信号的的采集和处理是一个复杂的过程,这需要程序设计者了解视频信号的构成,充分利用复合视频信号中的各种同步信息作为视频信号采集的控制信号,从而完成信号的采集。     

混沌权值变异的Huffman树图像加密算法

提出一种采用混沌权值变异的Huff man树的图像加密算法.此算法在熵编码过程中,以基本的Huffman树为标准,利用双耦合混沌序列1对DC系数进行树的结构未变异、路径值变异的加密;再利用双耦合混沌序列2对AC系数进行码字序号的加密.论文对算法进行了仿真,并对安全性、计算复杂度、压缩比性能进行了分析.实验结果表明,该算法基本上不影响压缩效率,且计算复杂度低、安全性高和实时性好,可用于网络上的图像服务.     

基于改进傅里叶描绘子的CBIR系统

在现有的基于傅里叶描绘子的CBIR系统中,为了提高检索速度,一般需要舍去物体轮廓经傅里叶变换后的大部分高频分量.当物体轮廓在细节部分具有较高能量时,此方法不具备有效性.为尽可能保证检索准确率并兼顾检索速度,在原有傅里叶描绘子上进行扩展,避免直接舍去高频分量,引入Fisher判别分析法将描绘子映射到子空间进行降维,并保证...     

端到端冗余流量消除技术的指纹选择算法研究

为了兼顾服务器端的内存消耗和有效性,在分析了现有端到端冗余流量消除技术中的指纹选择算法后,提出了贪婪指纹选择算法。该算法认为两个数据包中相同片段的邻接片段很有可能也是相同的,基于这一原理,邻接片段的指纹也将被选为代表指纹,有效地避免了在服务器端缓存整个数据包,大大减少了服务器端的内存消耗。仿真实验结果表明,该算法与现有的指纹选择算法相比,能够大大减少服务器端和用户终端的内存消耗,同时冗余消除率在10%-50%之间,与现有算法相近,证明了该算法的有效性。     

SIFT在行星表面探测器视觉导航系统中的应用

介绍了行星探测器自主导航系统的相关背景,讨论了行星探测器自主视觉导航系统算法的主要目的和传感器信息融合的必要性,并就此引入了尺度不变特征变换(SIFT)的相关理论分析.对SIFT方法提出了改进,从而改善了算法的效果.最后给出了三维重建和目标追踪的实验结果.     

A numerical modelling for the extraction of decay regions from color images of monuments

An innovative application focused on the segmentation of decay zones from images of stone materials is presented. The adopted numerical approach to extract decay regions from the color images of monuments provides a tool that helps experts analyze degraded regions by contouring them. In this way even if the results of the proposed procedure depend on the evaluation of experts, the approach can be a contribution to improving the efficiency of the boundary detection process. The segmentation is a process that allows an image to be divided into disjoint zones so that partitioned zones contain homogeneous characteristics. The numerical method, used to segment color images, is based on the theory of interface evolution, which is described by the eikonal equation. We adopted the fast marching technique to solve the upwind finite difference approximation of the eikonal equation. The fast marching starts from a seed point in the region of interest and generates a front which evolves according to a specific speed function until the boundary of the region is identified. We describe the segmentation results obtained with two speed functions, attained by the image gradient computation and color information about the object of interest. Moreover, we present the extension of the working modality of the method by introducing the possibility to extract the regions not only in a local way but also in a global way on the entire image. In this case, in order to improve the segmentation efficiency the application of the fast marching technique starts with more seed points defined as seed regions. The study case concerns the impressive remains of the Roman Theatre in the city of Aosta (Italy). In the image segmentation process the color space L^∗a^∗b^∗$L^{\ast }{a}^{\ast }{b}^{\ast }$ is utilized.     

A genetic algorithm with gene rearrangement for K-means clustering

In this paper, a new clustering algorithm based on genetic algorithm (GA) with gene rearrangement (GAGR) is proposed, which in application may effectively remove the degeneracy for the purpose of a more efficient search. A new crossover operator that exploits a measure of similarity between chromosomes in a population is also presented. Adaptive probabilities of crossover and mutation are employed to prevent the convergence of the GAGR to a local optimum. Using the real-world data sets, we compare the performance of our GAGR clustering algorithm with K-means algorithm and other GA methods. An application of the GAGR clustering algorithm in unsupervised classification of multispectral remote sensing images is also provided. Experiment results demonstrate that the GAGR clustering algorithm has high performance, effectiveness and flexibility.     

Learning a color distance metric for region-based image segmentation

In this paper we describe an experiment where we studied empirically the application of a learned distance metric to be used as discrimination function for an established color image segmentation algorithm. For this purpose we chose the Mumford–Shah energy functional and the Mahalanobis distance metric. The objective was to test our approach in an objective and quantifiable way on this specific algorithm employing this particular distance model, without making generalization claims. The empirical validation of the results was performed in two experiments: one applying the resulting segmentation method on a subset of the Berkeley Image Database, an exemplar image set possessing ground-truths and validating the results against the ground-truths using two well-known inter-cluster validation methods, namely, the Rand and BGM indexes, and another experiment using images of the same context divided into training and testing set, where the distance metric is learned from the training set and then applied to segment all the images. The obtained results suggest that the use of the specified learned distance metric provides better and more robust segmentations, even if no other modification of the segmentation algorithm is performed.