基于霍夫直线检测与二维透视变换的图像校正恢复算法

为了解决当前文档图像场景变换随机性较强,存在目标边缘检测和图形转换不准确的问题,导致图像校正质量较低的不足,分别从霍夫直线检测与二维透视变换的角度出发,提出了基于霍夫直线检测与二维透视变换的文档图像校正恢复技术.首先,根据聚类分割与形态学处理,得到包含文档目标在内的二值图像,达到去除噪声干扰的目的.然后,基于霍夫直线检测,计算文档边缘直线坐标,耦合二值图像目标轮廓,建立文档直线检测算子.最后,综合平移变换、仿射变换和刚体变换,构建二维透视变换矩阵,重新分配图像坐标,达到图像校正转换的目的.实验测试结果显示,与当前图像校正机制相比,校正技术拥有更高的图像恢复质量与抗干扰性.所提算法具有理想的校正恢复质量,在二维码识别等领域具有重要应用价值.     

恶意软件检测中解决样本不平衡问题的策略

互联网技术已经使人们的生活和工作发生了巨大的改变．然而,人们在享受互联网提供的便利的同时,也承受着恶意程序带来的威胁．在数字化时代的今天,与恶意程序的对抗已成为信息领域的焦点．由于恶意软件检测中的恶意软件样本难于获取,同时,标记大量的样本也需要花费大量的人力和物力,所获得的恶意软件样本远远少于正常软件样本,因此各类的训练样本之间存在分布不平衡的分类问题．为了解决该问题,本文提出采用SMOTE过采样方法,通过合理的增加少数类样本来解决样本不平衡问题．     

基于电子眼的应急物流运输路径自动规划研究

传统应急物流运输路径自动规划方法无法对灾区路况进行整体把握,导致规划的路径中出现道路损毁的情况,车辆无法到达,影响安全的同时,绕路延长运输时间。因此,设计一种基于电子眼的应急物流运输路径自动规划方法。首先确定电子眼的相关参数,设计其工作逻辑,完成传输图像视频的遍历检测,为路径规划提供参考,选择GIS系统中的栅格数据,作为路径规划的评估依据,最后建立应急物流路径规划模型,重点考虑的就是行车速度和时间,建立目标函数和约束函数,求解后得到应急物流运输路径自动规划的结果。通过算例仿真的实验结果,证明设计的规划方法得到的路径方案能够减少运输时间和信息量,验证了设计的路径自动规划方法具有一定的可靠性。     

低碳经济背景下塑料包装的逆向物流回收体系研究

随着低碳经济理念的提出和发展,塑料包装废弃物的回收处理问题逐渐成为人们讨论的热点话题。为解决塑料包装回收问题,垃圾回收站、包装生产商和快递企业等分别作为主导建立了符合低碳经济发展理念的逆向物流回收体系。但目前塑料包装的逆向物流回收体系仍旧存在诸多问题,需要政府、企业、消费者多方合作,不断完善塑料包装逆向物流回收体系,提升塑料包装回收利用的有效性,为低碳经济的发展和健康运行提供支撑。     

Hierarchical Learning-Guided human motion quality assessment in big data environment

Image information may be distorted during acquisition, processing, compression, and transmission. It is necessary to propose an intelligent image quality assessment model toward big data environment to quantify the degree of distortion of the image. This paper proposes a quality assessment model for human motion images. In complex scenes, the human body's action posture can be taken as an important feature point. Usually, in different scenes, the parts that affect the quality of the human body's posture are different. In other words, the weights of feature points that affect quality are different in different scenarios. However, due to the categorization of human movements, we can learn the quality assessment methods of different types of movements through sample training. Inspired by feature learning in the field of machine learning, we propose a hierarchical quality learning approach. We cast quality assessment as quality feature learning and layer by layer. The hierarchical quality learning method is based on deep reinforcement learning. The key part is that the method focuses on the region that containing more information on the features of the quality and enlarges the region layer by layer. Finally, we can determine the part of the body that affects the quality assessment. We compare this method with the subjective quality assessment results of the human observers and find that the proposed method achieves effective performance in big data environment to evaluate human motion quality.     

Human action recognition based on convolutional neural network and spatial pyramid representation

Detecting and recognizing human action in natural scenarios, such as indoor and outdoor, is a significant technique in computer vision and intelligent systems, which is widely applied in video surveillance, pedestrian tracking and human-computer interaction. Conventional approaches have been proposed based on various features and achieved impressive performance. However, these methods failed to cope with partial occlusion and changes of posture. In order to address these limitations, we propose a novel human action recognition method. More specifically, in order to capture image spatial composition, we leverage a three-level spatial pyramid feature extraction scheme, where each pyramid is encoded by local features. Thereafter, regions generated by a proposal algorithm are fed into a dual-aggregation net for deep representation extraction. Afterwards, both local features and deep features are fused to describe each image. To describe human action category, we design a metric CXQDA based on Cosine measure and Cross-view Quadratic Discriminant Analysis (XQDA) to calculate the similarity among different action categories. Experimental results demonstrate that our proposed method can effectively cope with object scale variations, partial occlusion and achieve competitive performance.     

一种图像高精度匹配方法

针对传统图像匹配精度不高、速度较慢的情况,为提高速度抑制噪声,提出一种高精度图像匹配方法。利用Harris提取角点进行灰度相关匹配找到粗匹配点,再利用Ransac算法得到较高精度匹配点,根据得到的匹配点求出基础矩阵。最后利用基础矩阵得到极线约束对Ransac得到的较高精度匹配点去除极少数误匹配点解算基础矩阵,利用第二次解算的基础矩阵求出高精度极线方程,并利用极线方程对Harris角点进行一维搜索匹配,找到高精度匹配点,进行仿真实验。反复实验表明,方法精度高、速度快,是一种实用的高精度图像匹配方法。     

计算机联网软件的制式安全评价分析

介绍了开展计算机制式安全评价的主要目的是为了确保评价的主要目的,便于信息的收集,另外一点就是选择合适的评价方式,开展评价工作。最后一方面内容就是开展好评价工作总结等等。讨论了计算机联网软件的制式安全评价的意义,首先确保了计算机运行操作体系的安全性,其次是监测网络的安全,降低故障出现的概率。因此得出了开展计算机联网软件的制式安全评价在网络技术使用过程中有着非常重要的地位。     

基于模糊-PID控制策略的变频恒压供水系统的仿真研究

针对恒压供水系统普遍存在的惯性大、非线性、随机性及纯滞后性的特点,设计了一种基于模糊 -PID控制策略的变频恒压供水系统。硬件部分介绍了系统的结构及组成,软件设计部分首先构建系统数学模型,研究了PID控制与模糊控制理论,并最终将模糊 -PID控制策略用于对系统的控制,同时兼具传统PID控制精度高及模糊控制动态响应特性好的优点。最后用MATLAB进行仿真的结果表明:模糊 -PID控制器能够使系统的动态、稳态性能优化,具有一定的应用前景。