基于非下采样Contourlet和MRF的纹理图像分割

提出了一种基于非下采样Contourlet变换(NSCT)和马尔科夫随机场(MRF)相结合的纹理图像分割算法.算法包括两个步骤,首先通过NSCT实现对图像纹理特征的提取,并使用模糊C-均值完成对图像的初始分割;然后将初始分割结果用 MRF模型表示,通过贝叶斯置信传播得到图像的最终分割结果.实验结果表明,对于纹理图像,该方法在分割错误率、区域一致性以及边缘的准确性方面都比传统小波变换的方法有了明显的改善.     

Towards Japanese corpus-based semantic accessibility scale

A detailed analysis of semantic accessibility scale （SAS） based on Japanese corpus has been performed in this paper. Firstly, the method of systematic random sampling is devised for quicker comparison among six groups in きくに . Secondly, according to the direct result of comparison, different sampling ratios （SR） in groups show regular deviation. The more pages are extracted, the higher SR is involved. Thirdly, the values of SAS in groups are calculated and then contrasted with SR. The regular deviation of SR fails to bring fundamental variation of SAS even though minor difference of SAS in groups may be reflected. The irrelevance to SAS establishes the fact that SR can be used for literary critics to extract and analyze SAS in corpus-involved texts from semantic perspective.     

一种履带式本体构建模型

为了有效地指导本体构建,创建了履带式本体构造模型来描述本体的构造方法。定义和描述了构成此模型的基本要素,包括本体（上层本体、领域本体和应用本体）和本体构造步骤（分析、设计、开发、部署和评估）,并在此基础上做出了定性分析,说明了顶级本体驱动开发方法、领域本体驱动开发方法和应用领域开发方法各自的特点。通过对此模型的分析和应用,以指导在本体开发中使用正确的开发方法。     

基于ARM和DSP的嵌入式ICE芯片扫描分析系统

集成毛细管电泳（ICE）芯片信号采集与处理系统的微型化、智能化是ICE芯片能否得到广泛应用的关键。针对传统的ICE芯片扫描分析系统结构复杂、处理速度慢、体积大等弱点,设计了一种基于DSP＋双口RAM＋ARM的嵌入式ICE芯片扫描分析系统。该系统将高速DSP与在通讯、网络和实时控制方面具有独特优势的ARM处理器,以及实时性好、接口电路简单、数据传输量大的双口RAM结合起来,为开发高性能、集成化、便携式的新型微小生化分离分析系统奠定了一定的技术基础。     

Rigor, rigor mortis, and conspiratorial views of depression research.

The authors reassert the need for methodological changes in depression research appearing in the Journal of Personality and Social Psychology and other personality and social psychology journals. In this rejoinder the authors update their earlier literature review (H. Tennen, J. Hall, & G. Affleck; see record 1995-31710-001) and respond to the commentaries by P C. Kendall and E. C. Flannery-Schroeder (see record 1995-31700-001) and G. Weary, J. A. Edwards, and J. A. Jacobson (see record 1995-31713-001). The authors notice that G. Weary et al.'s own findings demonstrate the need to change how depression is measured and participants are assigned to experimental groups. The authors also challenge G. Weary et al.'s contention that structured interviews are limited because they require interviewer judgments, and they urge personality and social psychologists to learn more about these interviews. Finally, G. Weary et al.'s suspicion that depression research guidelines reflect professional parochialism is disputed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Img2Logo: Generating Golden Ratio Logos from Images

Logos are one of the most important graphic design forms that use an abstracted shape to clearly represent the spirit of a community. Among various styles of abstraction, a particular golden-ratio design is frequently employed by designers to create a concise and regular logo. In this context, designers utilize a set of circular arcs with golden ratios (i.e., all arcs are taken from circles whose radii form a geometric series based on the golden ratio) as the design elements to manually approximate a target shape. This error-prone process requires a large amount of time and effort, posing a significant challenge for design space exploration. In this work, we present a novel computational framework that can automatically generate golden ratio logo abstractions from an input image. Our framework is based on a set of carefully identified design principles and a constrained optimization formulation respecting these principles. We also propose a progressive approach that can efficiently solve the optimization problem, resulting in a sequence of abstractions that approximate the input at decreasing levels of detail. We evaluate our work by testing on images with different formats including real photos, clip arts, and line drawings. We also extensively validate the key components and compare our results with manual results by designers to demonstrate the effectiveness of our framework. Moreover, our framework can largely benefit design space exploration via easy specification of design parameters such as abstraction levels, golden circle sizes, etc.     

VA + Embeddings STAR: A State-of-the-Art Report on the Use of Embeddings in Visual Analytics

Over the past years, an increasing number of publications in information visualization, especially within the field of visual analytics, have mentioned the term “embedding” when describing the computational approach. Within this context, embeddings are usually (relatively) low-dimensional, distributed representations of various data types (such as texts or graphs), and since they have proven to be extremely useful for a variety of data analysis tasks across various disciplines and fields, they have become widely used. Existing visualization approaches aim to either support exploration and interpretation of the embedding space through visual representation and interaction, or aim to use embeddings as part of the computational pipeline for addressing downstream analytical tasks. To the best of our knowledge, this is the first survey that takes a detailed look at embedding methods through the lens of visual analytics, and the purpose of our survey article is to provide a systematic overview of the state of the art within the emerging field of embedding visualization. We design a categorization scheme for our approach, analyze the current research frontier based on peer-reviewed publications, and discuss existing trends, challenges, and potential research directions for using embeddings in the context of visual analytics. Furthermore, we provide an interactive survey browser for the collected and categorized survey data, which currently includes 122 entries that appeared between 2007 and 2023.     

高阶条件随机场引导的多分支极化SAR图像分类

目的 针对极化合成孔径雷达（polarimetric synthetic aperture radar,PolSAR）小样本分类问题,基于充分挖掘有限样本的极化、空间特征考虑,提出一种由高阶条件随机场（conditional random field,CRF）引导的多分支分类网络模型。方法 利用Yamaguchi非相干目标分解方法,构建每个像素的极化特征向量。设计了由高阶CRF能量函数引导的多卷积分支特征提取网络,将像素点极化特征向量作为输入,分别提取像素点的像素特征、邻域特征和位置特征信息。将以上特征进行加和融合,并输入到softmax分类器中得到预分类结果。利用超像素方法对预分类结果图进行进一步修正和调优,平滑相邻像素之间的特异性和相似性。结果 采用1%的采样率对两组真实的极化SAR数据进行测试。同时,为了更好地模拟实际应用中训练样本位置分布不均匀的情况,考虑了空间不相交采样方法作为对比实验。综合两种采样策略的实验结果表明,相较于只利用像素级特征或简单利用空间特征的方法,本文方法总分类精度平均提升7%～10%,不同地物类别的分类精准度均在90%以上,运行速度相比于支持向量机（support vector machine,SVM）提高了2.5倍以上。结论 通过构建高阶CRF引导的卷积神经网络,将像素特征信息、同质区域特征和地理位置信息进行融合,有效建立了像素级和对象级数据之间的尺度关联,进一步扩充了像素点之间的空间依赖性,提取到了更强大更准确的表征特征,显著提高了标记样本数量较少情况下的卷积网络模型的分类性能,进一步保证了地物目标散射机制表征的全面性和可靠性。     

特征挖掘与区域增强的弱监督时序动作定位

弱监督时序动作定位旨在定位视频中行为实例的起止边界及识别相应的行为。现有方法尽管取得很大进展,但依然存在动作定位不完整及短动作的漏检问题。为此,提出了特征挖掘与区域增强（FMRE）的定位方法。首先,通过基础分支计算视频片段之间的相似分数,并以此分数聚合上下文信息,得到更具有区别性的段分类分数,实现动作的完整定位;然后,添加增强分支,对基础分支定位中持续时间较短的动作提案沿时间维度进行动态上采样,进而采用多头自注意机制对动作提案间的时间结构显式建模,促进具有时间依赖关系的动作定位且防止短动作的漏检;最后,在两个分支之间构建伪标签互监督,逐步改进在训练过程中生成动作提案的质量。该算法在THUMOS14和ActivityNet1.3数据集上分别取得了70.3%和40.7%的检测性能,证明了所提算法的有效性。     

电力系统密集型频率信号的测量与分析

针对非同步采样条件下密集频率信号无法准确测量的问题,提出一种密集型频率信号检测方法。基于离散傅里叶变换建立密集型频率信号模型,然后对单通道观测信号做周期延拓,形成多路信号。使用可变主成分分析法进行去相关性处理。再基于极大似然盲源分离法求解密集型频率信号模型。最后根据比例系数校正各频率成分参数。仿真和实验结果表明,所提方法具有较高的精度和一定的抗噪声能力。