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。     

Blind omnidirectional image quality assessment with representative features and viewport oriented statistical features

With the development of information technologies, various types of streaming images are generated, such as videos, graphics, Virtual Reality (VR)/omnidirectional images (OIs), etc. Among them, the OIs usually have a broader view and a higher resolution, which provides human an immersive visual experience in a head-mounted display. However, the current image quality assessment works cannot achieve good performance without considering representative human visual features and visual viewing characteristics of OIs, which limited OIs’ further development. Motivated by the above problem, this work proposes a blind omnidirectional image quality assessment (BOIQA) model based on representative features and viewport oriented statistical features. Specifically, we apply the local binary pattern operator to encoder the cross-channel color information, and apply the weighted LBP to extract the structural features. Then the local natural scene statistics (NSS) features are extracted by using the viewport sampling to boost the performance. Finally, we apply support vector regression to predict the OIs’ quality score, and experimental results on CVIQD2018 and OIQA2018 Databases prove that the proposed model achieves better performance than state-of-the-art OIQA models.     

No-reference stereoscopic images quality assessment method based on monocular superpixel visual features and binocular visual features

No-reference quality assessment of images has received considerable attention. However, the accuracy of such assessment remains questionable because of its weak biological basis. In this paper, we propose a novel quality assessment model based on the superpixel index and biological binocular mechanisms. The technical contributions of our model are the introduction of local monocular superpixel features and three global binocular visual features. We utilize monocular superpixel segmentation to extract two types of entropies as the local visual features for accurate quality-aware feature extraction. In addition, natural scene statistics features are extracted from the binocular visual information to complement the local monocular features and quantify the naturalness of the stereoscopic images. Finally, a regression model is learned to evaluate the quality of the stereoscopic images. Experimental results from three popular databases demonstrate that the proposed model has a more reliable performance than earlier models in terms of prediction accuracy and generalizability.     

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。     

基于视觉特性的半脆弱水印算法

提出了DWT(离散小波变换)变换域中基于人眼视觉特性模型的特征参数及其表示方法以及适用于一般变换域参数调整的量化中心极限定理,使得在动态量化过程中嵌入的半脆弱水印能够达到最大的顽健性。实验表明,算法生成的载体图像透明性好,对常见的JPEG压缩、噪声迭加和平滑滤波等图像处理操作具有较好的顽健性,而且可嵌入的水印信息量大,并能准确确定恶意攻击的位置。     

Human personality privacy analysis based on visual features

Personalized recommendation in social media attempts to analyze the habits and personality based on the loca-tion information and pictures from the users' comments on the public platform. This is very useful for personalized in-formation recommendation. But from perspective of privacy protection, this will affect the privacy security. A novel hu-man personality privacy analysis method based on portrait was proposed. By analyzing the personality model of the psy-chology, five visual features were proposed, including color features, texture features, shape features, Itten contrast fea-tures and expression features. Comprehensive experiments demonstrate the effectiveness of the proposed method. Further, the features analysis experiment show that the proposed features are very relevant to human personality privacy analyz-ing.     

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。     

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.     

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。     

基于方向梯度直方图和局部对比度特征的海面背景红外图像分类

在复杂多变的海面环境下,应用红外成像技术对海面中小目标进行搜救时,为有利于后续针对不同场景的目标处理,有必要对采集的原始图像进行分类处理。根据不同的环境条件,将海面红外图像分为五类场景。从两个方面对训练集图像进行特征提取,一个是通过高斯滤波将图像分为基础层和细节层,然后使用改进的方向梯度直方图（HOG）方法提取特征;另一个是提取图像的局部对比度得到局部特征。将提取的特征向量融合并输入到分类器中,使用支持向量机（SVM）对测试集图像进行分类。文章使用了HOG和局部对比度方法（LCM）结合的新特征描述符对海面红外图像的场景进行分类,与其它方法相比,结果表明改进方法的准确率达到96.4%,体现了可行性和有效性。     

基于视皮层全局感知特征的顽健图像散列方法

提出了一种基于视皮层全局感知特征的感知散列方法,图像首先被低通滤波后缩放成预定尺寸,然后图像分割成依赖于密钥的重叠子图像块,每块根据人类视觉系统主视皮层的层次结构,抽取图像经视觉通道逐层处理后的方向轮廓响应,并与表面颜色信息联合形成视皮层的全局感知特征,这些特征包含了每个图像块的方向、颜色信息,使得相邻系数的关系在通常的图像处理下保持不变,利用这种不变性将所有图像块的特征信息量化、置乱后形成二值图像散列.实验表明提出的方法对JPEG压缩、图像滤波等内容保持操作具有较好的顽健性,同时具有检测恶意篡改的能力,不同图像之间具有很低的共谋概率.     

Correlation filter via random-projection based CNNs features combination for visual tracking

Object tracking based on the Convolutional Neural Networks (CNNs) with multiple feature correlation filter (CF) has become one of the best object tracking frameworks. In this paper, we propose a novel approach of CNNs based CF, which combines deep features from CNNs into low-dimensional features. To achieve the dimensionality reduction, random-projection is used due to its data-independence and superior computational efficiency over other widely used. In our proposed approach, the spectral graph theory is applied to generate a random projection matrix. This method bypasses the time-consuming Gram–Schmidt orthogonalization, where the dimension of the feature is high. The combined features have very low dimensions, less than one tenth of the dimensions of the original deep features from CNNs, offering an improvement of tracking speed and without loss of performance simultaneously. Extensive experiments are conducted on large-scale benchmark datasets. The results demonstrate that the proposed algorithm outperforms the state-of-the-art methods.     

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.     

视觉测量中基于单应性矩阵的平面靶标图像特征提取

针对平面圆特征靶标和平面方格特征靶标图像,研究一种基于映射矩阵实现特征提取的新方法。根据靶标图像上至少4个角点位置的特征图像坐标及其对应的三维空间坐标建立靶标平面与像平面的单应性关系矩阵,结合已知的特征点空间坐标得到与已知空间坐标特征点相对应像面上特征点的粗略图像坐标,进而通过最小二乘椭圆拟合（圆特征靶标）或角点提取算...     

基于提取双选紧密特征的RGB-D显著性检测网络

针对现有算法对不同来源特征之间的交互选择关注度欠缺以及对跨模态特征提取不充分的问题,提出了一种基于提取双选紧密特征的RGB-D显著性检测网络。首先,为了筛选出能够同时增强RGB图像显著区域和深度图像显著区域的特征,引入双向选择模块(bi-directional selection module, BSM);为了解决跨模态特征提取不充分,导致算法计算冗余且精度低的问题,引入紧密提取模块(dense extraction module, DEM);最后,通过特征聚合模块(feature aggregation module, FAM)对密集特征进行级联融合,并将循环残差优化模块(recurrent residual refinement aggregation module, RAM)配合深度监督实现粗显著图的持续优化,最终得到精确的显著图。在4个广泛使用的数据集上进行的综合实验表明,本文提出的算法在4个关键指标方面优于7种现有方法。