跨媒体彩色复杂影像再现品质效益评估模式-「空间滤波器」及「CIEDE2000色差公式」的结合应用

在现行跨媒体复制系统中,若应用CIELAB于复杂彩色影像的评估,则因其未考量影像中各色点与周遭色彩之间相互影响的因素,将导致转换后的影像色彩外貌,无法与人眼的感觉相吻合.所以,此研究的目的乃一希望植基于人眼视觉机制,推导出得以精确应用于跨媒体复杂影像与其复制品之间的色差评估模式.以近年来新推荐之CIEDE2000色差公式,结合"具人眼机制的空间滤波器技术",进行"SCIELAB型的色差模式"推导.另外,亦将进行观测者心理物理配对实验.再将配对实验的结果与前述之色差评估模式者相比较,以期建立出最佳化的"跨媒体彩色复杂影像的色差评估模式".     

Recognition of Group Activities Using Complex Wavelet Domain Based Cayley-Klein Metric Learning

A group activity recognition algorithm is proposed to improve the recognition accuracy in video surveillance by using complex wavelet domain based Cayley-Klein metric learning. Non-sampled dual-tree complex wavelet packet transform (NS-DTCWPT) is used to decompose the human images in videos into multi-scale and multi-resolution. An improved local binary pattern (ILBP) and an inner-distance shape context (IDSC) combined with bag-of-words model is adopted to extract the decomposed high and low frequency coefficient features. The extracted coefficient features of the training samples are used to optimize Cayley-Klein metric matrix by solving a nonlinear optimization problem. The group activities in videos are recognized by using the method of feature extraction and Cayley-Klein metric learning. Experimental results on behave video set, group activity video set, and self-built video set show that the proposed algorithm has higher recognition accuracy than the existing algorithms.     

CVSS环境指标变量对系统安全的影响研究

通用漏洞评分体系(CVSS)分三个层次对漏洞的威胁进行评估,特定系统的安全性反映在最终的环境分层面上。在CVSS的三组指标变量中,仅环境指标变量取决于特定组织机构、特定系统,难以自动获取,是用户实施安全风险管理和控制策略中关键的和最困难的环节。在分析CVSS计算方法基础上,研究环境指标变量对最终CVSS总分的影响,给出了环境指标向量对CVSS环境分影响的总体估计式,同时给出了环境向量各分量单独影响的估计式。实验表明,本文在CVSS环境指标变量的总体影响和分项指标影响两方面,实现了精度提升,进入了实际标准完全可接受的范围。     

Comments on the article ‘Comparison of calculations for interplanar distances in a crystal lattice’, by Ying Liu,Yue Liu & Michael G. B. Drew

Some statements in the review by Liu et al. [Liu Y, Liu Y, Drew GB. Comparison of calculations for interplanar distances in a crystal lattice. Cryst Rev. 2017] are corrected. The role of metric tensor to simplify calculations of interplanar spacings in crystal lattices is emphasized.     

Medical image fusion based on hybrid intelligence

Medical image fusion combines complementary images from different modalities for proper diagnosis and surgical planning. A new approach for medical image fusion based on the hybrid intelligence system is proposed. This paper has integrated the swarm intelligence and neural network to achieve a better fused output. The edges are an important feature of an image and they are detected and optimized by using ant colony optimization. The detected edges are enhanced and it is given as the feeding input to the simplified pulse coupled neural network. The firing maps are generated and the maximum fusion rule is applied to get the fused image. The performance of the proposed method is compared both subjectively and objectively, with the genetic algorithm method, neuro-fuzzy method and also with the modified pulse coupled neural network. The results show that the proposed hybrid intelligent method performs better when compared to the existing computational and hybrid intelligent methods.     

An optimization-driven approach for computing geodesic paths on triangle meshes

There are many application scenarios where we need to refine an initial path lying on a surface to be as short as possible. A typical way to solve this problem is to iteratively shorten one segment of the path at a time. As local approaches, they are conceptually simple and easy to implement, but they converge slowly and have poor performance on large scale models. In this paper, we develop an optimization driven approach to improve the performance of computing geodesic paths. We formulate the objective function as the total length and adopt the L-BFGS solver to minimize it. Computational results show that our method converges with super-linear rate, which significantly outperforms the existing methods. Moreover, our method is flexible to handle anisotropic metric, non-uniform density function, as well as additional user-specified constraints, such as coplanar geodesics and equally-spaced geodesic helical curves, which are challenging to the existing local methods.     

结合时间戳的指纹密钥数据加解密传输方案

目的 对于生物密钥而言，生物特征数据的安全与生物密钥的管理存储都很关键。为了构造能够应用在通信数据传输场景的生物密钥，同时保证生物特征本身的模糊性与密码学的精确性处于一种相对平衡状态，提出一种基于时间戳与指纹密钥的数据加解密传输方案。方法 利用发送方指纹特征点之间的相对信息，与保密随机矩阵生成发送方指纹密钥；借助通信双方的预先设定数与时间戳，生成接收方恢复指纹密钥时所需的辅助信息；利用发送方指纹密钥加密数据，实现密文数据的传输。结果 本文方法在仿真通信双方数据加解密的实现中，测试再生指纹密钥的识别率（GAR）与误识率（FAR）。通过实验数据分析，表明了本文提出的指纹密钥生成方法的可用性，以及指纹密钥作为数字身份所具备的可认证性，其中真实发送方的再生指纹密钥识别率可高达99.8%，并且本方案还可用于即时通信、对称加密等多种场景当中。结论 本文方法利用时间戳确定了通信事件的唯一性与不可否认性，同时实现了指纹密钥恢复时的"一次一密"。此外，方案通过保密随机矩阵实现了发送方指纹密钥的可撤销，极大程度保障了指纹数据的安全性。     

聚类混合型数据的密度峰值改进算法

聚类混合型数据，通常是依据样本属性类别的不同分别进行评价。但这种将样本属性划分到不同子空间中分别度量的方式，割裂了样本属性原有的统一性；导致对样本个体的相似性评价产生了非一致的度量偏差。针对这一问题，提出以二进制编码样本属性，再由海明差异对属性编码施行统一度量的新的聚类算法。新算法通过在统一的框架内对混合型数据实施相似性度量，避免了对样本属性的切割，在此基础上又根据不同属性的性质赋予其不同的权重，并以此评价样本个体之间的相似程度。实验结果表明，新算法能够有效地聚类混合型数据；与已有的其他聚类算法相比较，表现出更好的聚类准确率及稳定性。