面向对象的可视化编程方法

用一种简单有趣的方式,叙述了利用面向对象的可视化编程工具进行软件设计的一般过程。通过这种论述,使初次接触这种编程方式的人员,可以很快地掌握编程基本方法的精髓,从而能顺利地进行更深入的学习。     

面向安全校核的电网调度仿真和可视化平台设计

由于可视化数据复杂多样,传统的电网调度安全校核仿真可视化平台无法对其进行正常显示,仿真准确性较低。因此,设计面向安全校核的电网调度仿真和可视化平台,该平台由安全校核仿真模块、仿真监控模块和可视化输出模块组成。其中,可视化输出模块的操作全部在计算机上完成;安全校核仿真模块负责电网中安全仿真数据的获取,并将其生成安全校核指令,该指令会传输到仿真监控模块实现电网设施的安全校核,仿真监控模块对电网进行实时监控;可视化输出模块接收安全校核仿真模块中的安全校核仿真数据和仿真监控模块中的监控日志,并将其转换成三维视频和二维图像,以供电网调度管理人员使用。软件设计部分给出了电网调度安全校核仿真可视化平台的更新流程图,以及平台对电网的安全校核语言。实验结果表明所设计平台拥有较高的仿真准确性和安全校核效率。     

Bimodal fusion of low-level visual features and high-level semantic features for near-duplicate video clip detection

The detection of near-duplicate video clips (NDVCs) is an area of current research interest and intense development. Most NDVC detection methods represent video clips with a unique set of low-level visual features, typically describing color or texture information. However, low-level visual features are sensitive to transformations of the video content. Given the observation that transformations tend to preserve the semantic information conveyed by the video content, we propose a novel approach for identifying NDVCs, making use of both low-level visual features (this is, MPEG-7 visual features) and high-level semantic features (this is, 32 semantic concepts detected using trained classifiers). Experimental results obtained for the publicly available MUSCLE-VCD-2007 and TRECVID 2008 video sets show that bimodal fusion of visual and semantic features facilitates robust NDVC detection. In particular, the proposed method is able to identify NDVCs with a low missed detection rate (3% on average) and a low false alarm rate (2% on average). In addition, the combined use of visual and semantic features outperforms the separate use of either of them in terms of NDVC detection effectiveness. Further, we demonstrate that the effectiveness of the proposed method is on par with or better than the effectiveness of three state-of-the-art NDVC detection methods either making use of temporal ordinal measurement, features computed using the Scale-Invariant Feature Transform (SIFT), or bag-of-visual-words (BoVW). We also show that the influence of the effectiveness of semantic concept detection on the effectiveness of NDVC detection is limited, as long as the mean average precision (MAP) of the semantic concept detectors used is higher than 0.3. Finally, we illustrate that the computational complexity of our NDVC detection method is competitive with the computational complexity of the three aforementioned NDVC detection methods.     

面向用户的音像资源数字媒体管理系统设计与实现

针对音像信息的特点及应用前景,提出了音像资源数字媒体管理系统结构设计的思想和技术实现方法,阐述了系统管理和应用的主要功能,旨在进一步加强音像信息资源的开发和利用。     

Clustering results of image searches by annotations and visual features

Image on web has become one of the most important information for browsers; however, the large number of results retrieved from images search engine increases the difficulty in finding the intended images. Image search result clustering (ISRC) is a solution to this problem. Currently, the ISRC-based methods separately utilized textual and visual features to present clustering result. In this paper, we proposed a new ISRC method as called Incremental-Annotations-based image search with clustering (IAISC), which adopted annotation as textual features and category model as visual features. IAISC can provide clustering result based on the semantic meaning and visual trail; further, presented by the iteratively structure, a user can obtain the intended image easily. The experimental result shows our method has high precision that the average precision rate is 73.4%; particularly, the precision rate is 96.5% when the user drills down the intended images till the last round. Regarding efficiency, our system is one and a half times as efficient as the previous studies.     

Discrete visual features modeling via leave-one-out likelihood estimation and applications

Discrete data are an important component in many image processing and computer vision applications. In this work we propose an unsupervised statistical approach to learn structures of this kind of data. The central ingredient in our model is the introduction of the generalized Dirichlet distribution as a prior to the multinomial. An estimation algorithm, based on leave-one-out likelihood and empirical Bayesian inference, for the parameters is developed. This estimation algorithm can be viewed as a hybrid expectation–maximization (EM) which alternates EM iterations with Newton–Raphson iterations using the Hessian matrix. We propose then the use of our model as a parametric basis for support vector machines within a hybrid generative/discriminative framework. In a series of experiments involving scene modeling and classification using visual words, and color texture modeling we show the efficiency of the proposed approach.     

基于金字塔梯度方向图像特征的检索模型设计

随着互联网的普及和发展,电子商务网站数量急剧增长,迫切需要一个平台对在线销售商品进行标注以方便用户进行搜索。通过提取类别图像和测试图像的金字塔梯度方向直方图(PHOG)全部特征,然后计算两者之间的距离,测试图像与类别图像距离比较近的就属于同一类图像。利用Matlab语言开发出了能够实现这一检索分类模型。实验证明这一模型灵活性好,准确性高。     

Web image concept annotation with better understanding of tags and visual features

This paper focuses on improving the semi-manual method for web image concept annotation. By sufficiently studying the characteristics of tag and visual feature, we propose the Grouping-Based-Precision & Recall-Aided (GBPRA) feature selection strategy for concept annotation. Specifically, for visual features, we construct a more robust middle level feature by concatenating the k-NN results for each type of visual feature. For tag, we construct a concept-tag co-occurrence matrix, based on which the probability of an image belonging to certain concept can be calculated. By understanding the tags’ quality and groupings’ semantic depth, we propose a grouping based feature selection method; by studying the tags’ distribution, we adopt Precision and Recall as a complementary indicator for feature selection. In this way, the advantages of both tags and visual features are boosted. Experimental results show our method can achieve very high Average Precision, which greatly facilitates the annotation of large-scale web image dataset.     

A unified framework for image retrieval using keyword and visual features.

In this paper, a unified image retrieval framework based on both keyword annotations and visual features is proposed. In this framework, a set of statistical models are built based on visual features of a small set of manually labeled images to represent semantic concepts and used to propagate keywords to other unlabeled images. These models are updated periodically when more images implicitly labeled by users become available through relevance feedback. In this sense, the keyword models serve the function of accumulation and memorization of knowledge learned from user-provided relevance feedback. Furthermore, two sets of effective and efficient similarity measures and relevance feedback schemes are proposed for query by keyword scenario and query by image example scenario, respectively. Keyword models are combined with visual features in these schemes. In particular, a new, entropy-based active learning strategy is introduced to improve the efficiency of relevance feedback for query by keyword. Furthermore, a new algorithm is proposed to estimate the keyword features of the search concept for query by image example. It is shown to be more appropriate than two existing relevance feedback algorithms. Experimental results demonstrate the effectiveness of the proposed framework.     

3D visual experience oriented cross-layer optimized scalable texture plus depth based 3D video streaming over wireless networks

3D video streaming over the mobile Internet generally incurs the inferior 3D visual experience due to the time-varying characteristics of wireless channel. The conventional video streaming optimization methods generally neglect the harmony among different networking protocol layers. This paper proposes a cross-layer optimized texture plus depth based scalable 3D video streaming method to improve the expected 3D visual experience of the user by systematically considering the application layer texture-video/depth/FEC bit-rate allocation, MAC layer multi-channel allocation, and physical layer modulation and channel coding scheme (MCS) selection. In the cross-layer optimization, a networking-related 3D visual experience model which fuses the overlapped retinal view visual quality and depth sensation with mimicking human vision system is established to predict the 3D visual experience under the specific parameter configurations of different protocol layers. The efficiency and effectiveness of the proposed cross-layer optimized 3D video streaming method has been validated by subjective and objective experimental results.     

Characteristic features of the accumulation of vacancy-and interstitial-type radiation defects in dislocation-free silicon with different oxygen contents

The formation of the main radiation defects in silicon (A and E centers, C _i -C _s and C _i -O _i complexes) in dislocation-free crystals and crystals with a low dislocation density (N _D ≃1×10⁴ cm⁻²) have been investigated as a function of the oxygen density N _O. The characteristic features of the accumulation and annealing of radiation defects observed in dislocation-free silicon are interpreted taking into account the presence of interstitial inclusions in the volume of such crystals. It has been determined that the gettering properties of the inclusions depend in a complicated manner on the oxygen concentration and are most obvious when N _O⋍3×10¹⁶ cm⁻³. Fiz. Tekh. Poluprovodn. 31, 405–409 (April 1997)     

Generalized Gaussian mixture models as a nonparametric Bayesian approach for clustering using class-specific visual features

Recently, there has been a growing interest in the problem of learning mixture models from data. The reasons and motivations behind this interest are clear, since finite mixture models offer a formal approach to the important problems of clustering and data modeling. In this paper, we address the problem of modeling non-Gaussian data which are largely present, and occur naturally, in several computer vision and image processing applications via the learning of a generative infinite generalized Gaussian mixture model. The proposed model, which can be viewed as a Dirichlet process mixture of generalized Gaussian distributions, takes into account the feature selection problem, also, by determining a set of relevant features for each data cluster which provides better interpretability and generalization capabilities. We propose then an efficient algorithm to learn this infinite model parameters by estimating its posterior distributions using Markov Chain Monte Carlo (MCMC) simulations. We show how the model can be used, while comparing it with other models popular in the literature, in several challenging applications involving photographic and painting images categorization, image and video segmentation, and infrared facial expression recognition.     

面向业务的全面解决方案

大唐电信在“业务先导、服务共赢”的企业经营理念引领下,针对当前电信运营商面临的挑战,通过资源整合和充分发挥技术积累优势,为电信运营商提供了“面向业务的全面解决方案”。该解决方案针对交换、传输、接入、运营支撑管理和移动增值服务五个不同层面的业务应用,分别推出了五个解决方案。     

An effective use of adaptive combination of visual features to retrieve image semantics from a hierarchical image database

Correlating semantic and visual similarity of an image is a challenging task. Unlimited possibilities of objects classification in real world are challenges for learning based techniques. Semantics based categorization of images gives a semantically categorized hierarchical image database. This work utilizes the strength of such database and proposes a system for automatic semantics assignment to images using an adaptive combination of multiple visual features. ‘Branch Selection Algorithm’ selects only a few subtrees to search from this image database. Pruning Algorithms further reduce this search space. Correlation of semantic and visual similarities is also explored to understand overlapping of semantics in visual space. The efficacy of the proposed algorithms analyzed on hierarchical and non-hierarchical databases shows that the system is capable of assigning accurate general and specific semantics to images automatically.     

时态空间中时态序列模式的数据挖掘

时态数据挖掘是目前数据挖掘领域的研究热点。与其它相关研究不同，文章致力于利用时态序列模式挖掘进行预测与决策。首先介绍了时态类型的分类；然后定义了一个新的时态空间模型，用以描述基于不同时态类型、不同属性的各个不同对象的状态，并且为高效地进行预测与决策提供支持；最后，给出了时态空间模型中数据挖掘的四种时态序列模式，对时态数据挖掘的研究具有重要意义。     

Analysis of superimposed oriented patterns.

Estimation of local orientation in images may be posed as the problem of finding the minimum gray-level variance axis in a local neighborhood. In bivariate images, the solution is given by the eigenvector corresponding to the smaller eigenvalue of a 2 x 2 tensor. For an ideal single orientation, the tensor is rank-deficient, i.e., the smaller eigenvalue vanishes. A large minimal eigenvalue signals the presence of more than one local orientation, what may be caused by non-opaque additive or opaque occluding objects, crossings, bifurcations, or corners. We describe a framework for estimating such superimposed orientations. Our analysis is based on the eigensystem analysis of suitably extended tensors for both additive and occluding superpositions. Unlike in the single-orientation case, the eigensystem analysis does not directly yield the orientations, rather, it provides so-called mixed-orientation parameters (MOPs). We, therefore, show how to decompose the MOPs into the individual orientations. We also show how to use tensor invariants to increase efficiency, and derive a new feature for describing local neighborhoods which is invariant to rigid transformations. Applications are, e.g., in texture analysis, directional filtering and interpolation, feature extraction for corners and crossings, tracking, and signal separation.     

Designing of low-power data oriented adders

The paper presents an idea of designing of low-power adders addressed to specific data processing. Mainly, the idea consists in proper choosing of 1-bit full adder cells for given probability of summed data, to obtain reduction in consumed power. Additionally different structures of the cells can be used, in one design, if it leads to reduction of power dissipation. To proper choice of structures of 1-bit full adders theirs energy characteristic versus summed data is needed. So, at the beginning we present results of assessment of a few 1-bit adder cells selected from literature and designed in UMC180 CMOS technology. The extended model of power consumption, taking into consideration input vector changes, was used, giving more accurate values than traditional model based on switching activity only. Thanks to the use of this model, obtained results allow detailed analysis of 1-bit adders on account of the using them in designing of low-power multi-bit adders summing specific data. Based on the results of analyses and given characteristic of summed data, appropriate full adder cells can be chosen to the final design of low-power data oriented adder. In specific case, cells which are made in different techniques can be used in multi-bit adder. A few examples are shown at the end of the paper.