Diving deeper into underwater image enhancement: A survey

The powerful representation capacity of deep learning has made it inevitable for the underwater image enhancement community to employ its potential. The exploration of deep underwater image enhancement networks is increasing over time; hence, a comprehensive survey is the need of the hour. In this paper, our main aim is two-fold, (1): to provide a comprehensive and in-depth survey of the deep learning-based underwater image enhancement, which covers various perspectives ranging from algorithms to open issues, and (2): to conduct a qualitative and quantitative comparison of the deep algorithms on diverse datasets to serve as a benchmark, which has been barely explored before.We first introduce the underwater image formation models, which are the base of training data synthesis and design of deep networks, and also helpful for understanding the process of underwater image degradation. Then, we review deep underwater image enhancement algorithms, and a glimpse of some of the aspects of the current networks is presented, including architecture, parameters, training data, loss function, and training configurations. We also summarize the evaluation metrics and underwater image datasets. Following that, a systematically experimental comparison is carried out to analyze the robustness and effectiveness of deep algorithms. Meanwhile, we point out the shortcomings of current benchmark datasets and evaluation metrics. Finally, we discuss several unsolved open issues and suggest possible research directions. We hope that all efforts done in this paper might serve as a comprehensive reference for future research and call for the development of deep learning-based underwater image enhancement.     

Mean Shift和粒子滤波实现红外人体跟踪算法综述

红外摄像仪能够全天候工作且不会受限于像光线不足、漆黑夜晚等外界环境的干扰,但是红外图像成像质量差、分辨率低、信息单一等特点导致研究人体目标跟踪出现许多难点问题。主要贡献表现在以下三个方面：（1）对少有的公开的红外数据集进行详细归纳;（2）重点阐述了国内外在红外人体跟踪方面对Mean Shift算法和粒子滤波算法的改进方案;（3）重点介绍了融合红外成像与可见光成像实现红外人体跟踪的研究进展。     

基于用户相似性传递的跨平台交叉推荐算法

个性化推荐系统在电子商务领域中的广泛应用带来了巨大的经济效益和良好的用户体验。由于用户数据往往分布在多个不同的网站,单个网站的推荐系统受制于数据稀疏性的限制,难以获得准确的推荐效果。该文提出了一种基于传递相似性的交叉推荐系统算法,可以利用多个网站平台数据计算不同网站中的用户的相似度,从而很大程度上克服了推荐系统中的数据稀疏性以及冷启动问题。结果显示,该交叉推荐算法与传统的针对单个数据集的推荐算法相比,推荐的精确性有一至两倍的提高。     

Data preprocessing issues for incomplete medical datasets

While there is an ample amount of medical information available for data mining, many of the datasets are unfortunately incomplete – missing relevant values needed by many machine learning algorithms. Several approaches have been proposed for the imputation of missing values, using various reasoning steps to provide estimations from the observed data. One of the important steps in data mining is data preprocessing, where unrepresentative data is filtered out of the data to be mined. However, none of the related studies about missing value imputation consider performing a data preprocessing step before imputation. Therefore, the aim of this study is to examine the effect of two preprocessing steps, feature and instance selection, on missing value imputation. Specifically, eight different medical‐related datasets are used, containing categorical, numerical and mixed types of data. Our experimental results show that imputation after instance selection can produce better classification performance than imputation alone. In addition, we will demonstrate that imputation after feature selection does not have a positive impact on the imputation result.     

基于亚分数混淆矩阵的中国典型区大尺度土地覆盖数据集评价

在中国东北、华北、华中、华南、西北、青藏、内蒙古7个自然地区分别选择典型区A、B、C、D、E、F、G,以Landsat TM/ETM+影像分类结果为参考数据,采用亚分数混淆矩阵对5种大尺度土地覆盖数据集的精度进行定量评价,为数据集的使用提供科学依据。亚分数混淆矩阵可避免参考数据与待评价数据尺度转换时引入的误差,能反映不同优势类比重情况下数据集的总体精度和分类方法误差。结果表明:GLC2000在全部典型区的总体精度最高,为65.64%;UMD总体精度最低,为43.06%。GLC2000在主要土地覆盖类型为林地和耕地以及草地区域具有较高的分类精度;UMD在各区域的分类精度均最低或较低。5种土地覆盖数据集对于城镇、其他的分类精度在各典型区均较低;对于草地和水体的分类精度则是在西北干旱区和青藏高原区的典型区较高。     

多卫星遥感降水产品在柬埔寨的应用比较分析

柬埔寨地区的水资源十分丰富,但由于气象、水文站点的稀缺以及水文气象数据的不连续等原因,导致流域基本水雨情及水资源特征数据不够完善、全面和准确,不利于该国水资源的科学管理和开发利用。一系列高时空分辨率的遥感降水产品的出现为降水数据的缺失和不连续提供了新的解决方法。选取了5种高精度的遥感降雨数据(CHIRPS、CMORPH_CRT、CMORRPH_BLD、MSWEP、TRMM)对柬埔寨地区2003~2015年降雨的时空分布差异、精度评价、极端降雨估计情况进行了对比分析。结果表明,5组遥感降雨产品总体均能较好地反映柬埔寨地区降雨的时间及空间变化规律。这对于柬埔寨及其它无资料地区的水资源研究及工程设计工作具有一定的指导作用。     

中国2000年1 km植物功能型分布图

植物功能型(PFT,Plant Functional Types)已被广泛用于生物地理模型、生物地球化学模型、陆面过程模型和全球动态植被模型,用以预测全球变化情景下生态系统结构与功能的变化。本数据基于近年发布并广泛应用的中国1 km土地覆盖图(MICLCover),根据土地覆盖类型与植物功能型的气候转换规则,结合中国科学院青藏高原研究所(ITPCAS)最新发展的中国区域近30 a 0.1°大气驱动数据,实现了中国2000年1 km植物功能型分布制图,间接验证表明该数据在1 km尺度上具有更高的精度,是中国区域的生态学相关研究和陆面过程模拟中植物功能型数据的重要选择。     

New fuzzy SVM model used in imbalanced datasets

The paper proposes a new fuzzy SVM, called CI-FSVM(Class Imbalance Fuzzy Support Vector Machine) short for which is based on imbalanced datasets classification. By improving penalty functions, we reduce the sensitivity of the model for imbalanced datasets with “overlap”. In addition, the parameters in SVM models are optimized by the grid-parameter-search algorithm. The results show that the CI-FSVM has a better effect in imbalanced datasets classification compared with other models. It not only has a higher overall accuracy, but also improves are judgment accuracy when dealing with the minority classifications.     

微表情自动分析方法研究综述

微表情自动分析是计算机视觉研究方向之一。微表情在刑侦、临床医学、商业谈判、公共安全等场景下的微表情分析技术具有重要研究和应用价值。为了梳理微表情自动分析领域研究现状及发展方向,对常用微表情数据集和数据预处理方法进行整理。基于微表情特征,全面整理和对比微表情检测和识别任务各类算法以及实验方法和验证指标。可以帮助研究人员更加快捷、全面了解该领域研究现状,存在的问题和未来发展方向。     

利用改进灰狼算法优化BP神经网络的入侵检测

神经网络技术被广泛应用于网络安全领域,在入侵检测中能够实现网络攻击的主动检测和攻击分类.然而随着恶意攻击的不断演化,神经网络技术存在的弊端日益显现.针对BP神经网络在入侵检测过程中存在的初始值随机性较大以及易陷入局部最优的问题,本文提出一种改进灰狼算法优化BP神经网络的入侵检测模型(IGWO-BP).首先,使用混沌映射初始化种群、设计非线性收敛因子以及动态权重策略对传统灰狼算法进行改进,并以此优化BP神经网络的初始权值和阈值,并运用改进BP神经网络对网络安全数据集进行实际检测.实验结果表明,IGWO-BP模型在NSL-KDD和UNSW-NB15数据集上取得了较优的检测结果,与其它现有模型相比性能也有较大提升.