Real-time homogenous translucent material editing

This paper presents a novel method for real-time homogenous translucent material editing under fixed illumination. We consider the complete analytic BSSRDF model proposed by Jensen et al. [ [JMLH01] ], including both multiple scattering and single scattering. Our method allows the user to adjust the analytic parameters of BSSRDF and provides high-quality, real-time rendering feedback. Inspired by recently developed Precomputed Radiance Transfer (PRT) techniques, we approximate both the multiple scattering diffuse reflectance function and the single scattering exponential attenuation function in the analytic model using basis functions, so that re-computing the outgoing radiance at each vertex as parameters change reduces to simple dot products. In addition, using a non-uniform piecewise polynomial basis, we are able to achieve smaller approximation error than using bases adopted in previous PRT-based works, such as spherical harmonics and wavelets. Using hardware acceleration, we demonstrate that our system generates images comparable to [ [JMLH01] ]at real-time frame-rates.     

An efficient algorithm for mining frequent inter-transaction patterns

In this paper, we propose an efficient method for mining all frequent inter-transaction patterns. The method consists of two phases. First, we devise two data structures: a dat-list, which stores the item information used to find frequent inter-transaction patterns; and an ITP-tree, which stores the discovered frequent inter-transaction patterns. In the second phase, we apply an algorithm, called ITP-Miner (Inter-Transaction Patterns Miner), to mine all frequent inter-transaction patterns. By using the ITP-tree, the algorithm requires only one database scan and can localize joining, pruning, and support counting to a small number of dat-lists. The experiment results show that the ITP-Miner algorithm outperforms the FITI (First Intra Then Inter) algorithm by one order of magnitude.     

云纹图象计算机处理技术的进展及其应用

本文对１９８７年至今国内外光测力学条纹图，主要是云纹图象的计算机采集，处理和力学量提取技术的进展进行了回顾，包括利用通用图象处理程序，直接利用灰度，用付利叶变换对条纹图进行分析，引入相移技术提高灵敏度，利用采色编码提取力学量。以及建立在云纹图特点基础上的新的图象处理技术等；分析了它们各自的优缺点，适用范围和存在的问题，讨论了它们在科究和工程测试中的应用前景。     

A visual news processing environment

CPP-TRS (Tonfoni 1989–94) is both a methodology and a language. Through the methodology (CPP — Communicative Positioning Program) those invisible aspects of communication are identified and represented by the meta-language (TRS — Text Representation Systems), which complements natural language. CPP-TRS is a new paradigm which applies to any communicative context.Icons in CPP-TRS are not intended to represent words or sentences in order to substitute them; they are rather intended to represent visually what natural language does not convey naturally. The improvement of communication is a challenge of the present and the future: CPP-TRS is designed explicitly for it.The CPP-TRS system thus is able to support the user with a set of visual tools, that are specifically suited to structuring text and communicating effectively.The same tools can support communication very effectively in situations where ambiguity can highly compromise the final result.The system may be used in a way that is either opaque or transparent to the reader of the text.Author of CPP-TRS¢ and CPP-TRS¢ Applications Designer.     

测井现场绘图软件的面向对象设计与实现

在面向对象的软件设计中,多态性是一种非常重要的概念,文章论述用封装、继承和多态性来实现测井绘图软件的设计和开发,实现不同测井图像连续绘制。测井绘图软件能够对不同的测井图像实现动态兼容,提高了软件系统整体的扩展性。     

软件重构及设计模式在摄像头阅卷系统中的应用研究

首先设计并实现一个基于摄像头的阅卷系统,该系统由试卷获取、灰度化、二值化、畸变变换、倾斜校正、图像定位和识别等图像处理流程组成。然后以此为依据,通过对重构技术及设计模式原则和方法的系统研究,对该系统框架进行优化和重构。本文充分参照现有的设计模式,通过重构技术,改善现有代码的质量,增加系统的可维护性和可复用性。     

Comparing ANN and GMM in a voice conversion framework

In this paper, we present a comparative analysis of artificial neural networks (ANNs) and Gaussian mixture models (GMMs) for design of voice conversion system using line spectral frequencies (LSFs) as feature vectors. Both the ANN and GMM based models are explored to capture nonlinear mapping functions for modifying the vocal tract characteristics of a source speaker according to a desired target speaker. The LSFs are used to represent the vocal tract transfer function of a particular speaker. Mapping of the intonation patterns (pitch contour) is carried out using a codebook based model at segmental level. The energy profile of the signal is modified using a fixed scaling factor defined between the source and target speakers at the segmental level. Two different methods for residual modification such as residual copying and residual selection methods are used to generate the target residual signal. The performance of ANN and GMM based voice conversion (VC) system are conducted using subjective and objective measures. The results indicate that the proposed ANN-based model using LSFs feature set may be used as an alternative to state-of-the-art GMM-based models used to design a voice conversion system.     

Mining the change of customer behavior in fuzzy time-interval sequential patterns

Comprehending changes of customer behavior is an essential problem that must be faced for survival in a fast-changing business environment. Particularly in the management of electronic commerce (EC), many companies have developed on-line shopping stores to serve customers and immediately collect buying logs in databases. This trend has led to the development of data-mining applications. Fuzzy time-interval sequential pattern mining is one type of serviceable data-mining technique that discovers customer behavioral patterns over time. To take a shopping example, (Bread, Short, Milk, Long, Jam), means that Bread is bought before Milk in a Short period, and Jam is bought after Milk in a Long period, where Short and Long are predetermined linguistic terms given by managers. This information shown in this example reveals more general and concise knowledge for managers, allowing them to make quick-response decisions, especially in business. However, no studies, to our knowledge, have yet to address the issue of changes in fuzzy time-interval sequential patterns. The fuzzy time-interval sequential pattern, (Bread, Short, Milk, Long, Jam), became available in last year; however, is not a trend this year, and has been substituted by (Bread, Short, Yogurt, Short, Jam). Without updating this knowledge, managers might map out inappropriate marketing plans for products or services and dated inventory strategies with respect to time-intervals. To deal with this problem, we propose a novel change mining model, MineFuzzChange, to detect the change in fuzzy time-interval sequential patterns. Using a brick-and-mortar transactional dataset collected from a retail chain in Taiwan and a B2C EC dataset, experiments are carried out to evaluate the proposed model. We empirically demonstrate how the model helps managers to understand the changing behaviors of their customers and to formulate timely marketing and inventory strategies.     

多级小波分解与LBP融合的纹理提取算法

为提高不同光照、不同角度条件下的纹理识别精度,提出了一种利用多级小波分解和多尺度旋转不变LBP融合的纹理提取算法。算法在传统的LBP特征提取基础上,采用多尺度的旋转不变LBP算子分别对多级小波逼近图像提取直方图序列特征向量,与各级小波能量进行加权融合,获取更多的纹理信息,对光照和角度的变化有更高的鲁棒性。仿真结果表明,相对传统的LBP特征提取算法,改进的算法具有更高的纹理识别率。