Heterogeneous object modeling: A review

A review on the recent development in heterogeneous object modeling is provided in this paper. General problems and prevalent solutions to the modeling of heterogeneous objects are investigated. Vigorous heterogeneous object representations are roughly classified into three major categories: evaluated models, unevaluated models and composite models. We reveal their similarities and study their strengths and weaknesses in terms of the representational capacities, intuitiveness, exactness, compactness, efficiency and other criteria. Different design paradigms are briefly reviewed to provide an overview of the state-of-the-art in heterogeneous object design. Finally, open problems and possible future directions are discussed.     

Testing the robustness of CIECAM02

CIE TC8‐01 has adopted a new color appearance model: CIECAM02¹ replaces the CIECAM97s.² The new model consists of a number of refinements and simplifications of the CIECAM97s color appearance model. This article describes further tests to the robustness of the forward and reverse modes. © 2005 Wiley Periodicals, Inc. Col Res Appl, 30, 99–106, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20087     

三维目标旋转畸变不变光学相关滤波器组的设计

研究了三维目标旋转畸变不变光学相关滤波器组的设计原理，完成了滤波器组参数的计算和编码，以及光学相关识别的计算机模拟。     

水下成像的现状和发展动向

本文以下水成像在军事，海洋开发工程应用为基础，论述了水下观测和成像中采用微光电视摄像机和激光器件及其系统的技术现状和发展动向。     

面向对象方法在7号信令软件设计中的应用

随着交换技术的飞速发展，程控交换软件的规模越来越大，改动也越来越频繁，如何使代码对数据的改动具有相对的稳定性成为程控软件设计的一个首要问题。本文以ＣＣＳ７（７号公共信道信令）为例，从对象模型、动态模型、功能模型３个方面阐述了ＯＯ（面向对象）方法在ＣＣＳ７软件设计中的应用。这一方法，也可应用于程控软件的设计。     

复杂场景实时目标检测方法

针对含有非平稳背景的复杂场景,提出一种基于空间混合高斯模型的实时目标检测方法.该方法以混合高斯模型作为颜色分布的统计框架,并在空间邻域中利用背景与前景隶属度之间的竞争确定像素的归属,提高了检测准确率;同时通过基于偏差均值的匹配判断和低权重模型的移除提升了混合高斯统计框架的性能,以实时地对运动目标进行准确的检测.实验结果表明,文中方法对非平稳背景有很好的适应能力,在检测准确率和运行效率上均优于其他检测方法.     

关于JSP线程安全问题的思考

JSP中变量的定义有全局和局部之分,局部变量并不会带来安全问题,而对全局变量的使用,就要慎之又慎。内置对象application的使用要特别注意线程安全。此外,JavaBean的使用也要注意线程安全问题,特别是当scope属性设置为application的时候。     

应用列对象的Tag属性实现多表数据动态查询

探讨在PB开发的应用程序中利用数据窗口列对象的Tag属性,结合自行设计的一个更改SELECT语句中的Where子句的全局函数,实现应用程序多表数据的动态查询。该种方法是PB开发的应用程序中实现数据查询的一种高效、简洁且行之有效的方法。     

基于地面先验的3D目标检测算法

为了提高自动驾驶汽车环境感知的性能,增强单目相机对障碍物三维和边界信息的感知能力,提出了一种基于地面先验的3D目标检测算法。基于优化的中心网络(CenterNet)模型,以DLA(deep layer aggregation)为主干网络,增加目标3D边沿框中心点冗余信息预测。根据自动驾驶场景的地面先验信息,结合针孔相机模型,获取目标3D中心深度信息,以优化深度网络学习效果。使用KITTI 3D数据集评测算法性能,结果表明:在保证2D目标检测准确性的基础上,该算法运行帧率约20 fps,满足自动驾驶感知实时性要求;同时相比于CenterNet模型,在平均方位角得分(average orientation score)和鸟视图平均准确率(bird eye view AP)上分别有4.4和4.4%的性能提升。因而,该算法可以提高自动驾驶汽车对障碍物三维和边界信息的感知能力。     

Environmental Perception with Spatial Regularization Correlation Filter for Visual Tracking

With the introduction of correlation filtering (CF), the performance of visual object tracking is significantly improved. Circular shifts collecting samples is a key component of the CF tracker, and it also causes negative boundary effects. Most trackers add spatial regularization to alleviate boundary effects well. However, these trackers ignore the effect of environmental changes on tracking performance, and the filter discriminates poorly in the background interference. Here, to break these limitations, we propose a new correlation filter model, namely Environmental Perception with Spatial Regularization Correlation Filter for Visual Tracking. Specifically, we use the Average Peak to Correlation Energy (APCE) and the response value error between the two frames together to perceive environmental changes, which adjusts the learning rate to make the template more adaptable to environmental changes. To enhance the discriminatory capability of the filter, we use real background information as negative samples to train the filter model. In addition, the introduction of the regular term destroys the closed solution of CF, and this problem can be effectively solved by the use of the alternating direction method of multipliers (ADMM). Extensive experimental evaluations on three large tracking benchmarks are performed, which demonstrate the good performance of the proposed method over some of the state-of-the-art trackers.