一种基于二维DCT变换的图像信息隐藏方法

提出了一种基于二维DCT变换的图像信息隐藏方法,经过二维DCT变换后的图像能量集中在低频部分,考虑到人眼的视觉特性,图像的高频部分可视为视觉冗余部分,可以把有用信息隐藏在变换载体图像的高频部分,即以牺牲图像高频信息来达到信息隐藏的效果,通过MATLAB实现这一思想,对隐藏后的图像质量做了特征分析,并对下一步实验做出了设想.     

Infrared-Visible Image Fusion Based on Semantic Guidance and Visual Perception

Infrared-visible fusion has great potential in night-vision enhancement for intelligent vehicles. The fusion performance depends on fusion rules that balance target saliency and visual perception. However, most existing methods do not have explicit and effective rules, which leads to the poor contrast and saliency of the target. In this paper, we propose the SGVPGAN, an adversarial framework for high-quality infrared-visible image fusion, which consists of an infrared-visible image fusion network based on Adversarial Semantic Guidance (ASG) and Adversarial Visual Perception (AVP) modules. Specifically, the ASG module transfers the semantics of the target and background to the fusion process for target highlighting. The AVP module analyzes the visual features from the global structure and local details of the visible and fusion images and then guides the fusion network to adaptively generate a weight map of signal completion so that the resulting fusion images possess a natural and visible appearance. We construct a joint distribution function between the fusion images and the corresponding semantics and use the discriminator to improve the fusion performance in terms of natural appearance and target saliency. Experimental results demonstrate that our proposed ASG and AVP modules can effectively guide the image-fusion process by selectively preserving the details in visible images and the salient information of targets in infrared images. The SGVPGAN exhibits significant improvements over other fusion methods.     

Highly Accurate Visual Method of Mars Terrain Classification for Rovers Based on Novel Image Features

It is important for Mars exploration rovers to achieve autonomous and safe mobility over rough terrain. Terrain classification can help rovers to select a safe terrain to traverse and avoid sinking and/or damaging the vehicle. Mars terrains are often classified using visual methods. However, the accuracy of terrain classification has been less than 90% in read operations. A high-accuracy vision-based method for Mars terrain classification is presented in this paper. By analyzing Mars terrain characteristics, novel image features, including multiscale gray gradient-grade features, multiscale edges strength-grade features, multiscale frequency-domain mean amplitude features, multiscale spectrum symmetry features, and multiscale spectrum amplitude-moment features, are proposed that are specifically targeted for terrain classification. Three classifiers, K-nearest neighbor (KNN), support vector machine (SVM), and random forests (RF), are adopted to classify the terrain using the proposed features. The Mars image dataset MSLNet that was collected by the Mars Science Laboratory (MSL, Curiosity) rover is used to conduct terrain classification experiments. The resolution of Mars images in the dataset is 256 × 256. Experimental results indicate that the RF classifies Mars terrain at the highest level of accuracy of 94.66%.     

基于局域预测的动载天线伺服控制策略研究

针对中小涌浪的扰动,研究了一种基于少量数据的局域预测方法,应用于海面小型漂浮式动载天线跟踪卫星的方位和俯仰伺服控制中.以天线系统的少量位置数据为基础,采用局域预测算法预测天线的位置,计算天线指向将偏离卫星的偏差值,进行天线指向偏离卫星的补偿控制;基于天线位置间相关性,研究了局域预测算法并进行仿真验证;根据天线系统的组成设计了天线方位伺服控制器,并通过matlab仿真和半实物仿真验证了局域预测伺服控制策略的可行性、可靠性和鲁棒性.     

一款Hi-Fi甲类恒流功放的设计

分析了左右声道正反相放大、纯直流放大及高低压电源隔离技术在音频功放中的应用,论述了甲类放大、开环放大及恒流放大技术对于Hi—Fi功放的重要性,介绍了电路构成、电路原理、元器件选型及安装调试要点。     

基于结构相似的小波域图像质量评价方法的研究

 小波域的图像处理已经成为图像处理领域的重要组成部分,研究能嵌入在小波域图像处理算法中的图像质量评判方法显得至关重要.空间域结构相似度SSIM具有计算简单、与主观评分相关性高等优点.本文通过对小波域图像结构特性的研究,提出了小波域结构相似度(DWTSSIM)的图像质量评价方法.实验结果表明DWTSSIM比SSIM更加符合人眼的视觉特性,可以更好地评判图像质量,并且能容易地嵌入在小波域图像处理算法中,在图像处理过程中指导或调节算法.     

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。     

动态环境视觉导航的有限状态粗集方法研究

为实现移动机器人在多目标动态环境下导航与控制,提出了有限状态机和粗糙集理论相结合的单PT(Pan-Tilt)视觉系统监控策略.首先定义了目标方位信息可信度估计模型及基于动态优先级的目标选定方法.然后确定了每个目标监控的有限状态机模型,并依据粗糙集理论对目标监控决策表进行了约简,引入了非值约简描述方法,得到了最小规则集.在多目标环境下的移动机器人实验,验证了所提出方法的有效性.     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

Learning to See: Making Sense of the Mathematics of Change in Middle School

In this article, we will describe the results of a study of 6th grade students learning about the mathematics of change. The students in this study worked with software environments for the computer and the graphing calculator that included a simulation of a moving elevator, linked to a graph of its velocity vs. time. We will describe how the students and their teacher negotiated the mathematical meanings of these representations, in interaction with the software and other representational tools available in the classroom. The class developed ways of selectively attending to specific features of stacks of centimeter cubes, hand-drawn graphs, and graphs (labeled velocity vs. time) on the computer screen. In addition, the class became adept at imagining the motions that corresponded to various velocity vs. time graphs. In this article, we describe this development as a process of learning to see mathematical representations of motion. The main question this article addresses is: How do students learn to see mathematical representations in ways that are consistent with the discipline of mathematics? This revised version was published online in July 2006 with corrections to the Cover Date.