麻省理工学院计算机与人工智能长期科研项目科研主题及导师介绍

2020-01-08 12:14:36编辑：景景

　　麻省理工学院计算机与人工智能长期科研项目科研主题有哪些？本文为大家详细介绍本项目科研导师，项目收获和成果，以及科研主题，并给出课题参考，供大家了解！点击了解：项目背景及优势

　　科研导师

　　Research Tutor

　　MIT 计算机科学与人工智能交通研究中心科学家;MIT UV (Universal

　　Village) 项目委员会国际主席。

　　项目收获与成果

　　Research Harvest & Results

　　∙ 表现优秀的学员获得项目导师的个性化推荐信

　　∙ 导师签字的科研证明;

　　∙ 学员将在导师的指导下完成科研课题相关论文;

　　∙ 学生个人科研报告(writing sample/graded paper)。

　　科研主题

　　Scientific Research Theme

　　machine vision[机器视觉]

　　machine learning[机器学习]

　　sensor fusion for intelligent vehicle and intelligent transportation

　　systems[智能车辆与智能交通系统的传感器器融合]

　　big data analysis and systematic development of Universal Village

　　[环球村背景下大数据分析和系统开发]

　　an extended version of Smart Cities[智慧城市]

　　Universal Village[环球村]

　　MRI image analysis[MRI 图像分析]

　　data analysis for intelligent healthcare[智能医疗数据分析]

　　(具体课题根据学生的基础，导师面试后确定。具体情况根据学⽣面试情况由美方进⾏调整，报名后 1 周安排面试，面试前辅导学⽣。)

　　参考课题示例：

　　我们将开发一个三维系统，以可靠的交通监控解决车辆和阴影重叠的问题。

　　The Problem and Goal: Traffic monitoring systems detect traffic accidents and congestion for immediate assistance and/or traffic flow control. Conventional loop detectors are installed under the pavement and require regular maintenance which is disruptive to traffic. TV-camera based systems are an alternative solution providing non-disruptive monitoring. An advantage of TV-camera-based traffic monitoring systems over loop detectors and acoustic detectors is that the traffic control centers can have a first hand view of the traffic sitution. The problem with

　　conventional TV-camera-based systems is that the systems are confused with overlapping vehicles and shadows, and the systems cannot provide highly accurate measurement results. To avoid overlapping vehicles, overhead structures are used. These structures usually cost more than the machine vision systems. Our goal is to develop a three-dimensional system for reliable traffic monitoring solving the problem of overlapping vehicles and shadows.

　　Approach:

　　Our traffic monitoring system is a TV-camera based system to provide traffic control centers with a real-time view of the traffic situation. We use our three-dimensional vision system described on page 15 to address the accuracy problems with conventional two-dimensional systems. The cameras are located on the side of the road as shown in Figure 1 and expensive overhung structure is unnecessary. The system uses three-dimensional data to eliminate problems with overlapping vehicles and shadows.The three-camera system produces an edge depth map containing the distances of the edge of objects in the image. The three

　　dimensional data is used to distinguish between vehicles and enable traffic monitoring. Figure 2 displays the center image and the edge depth map. The colors indicates the different distances of the object.

　　Figure 2: Center Image and Depth Map

　　Figure 3 is a histogram of the distance vs. number of edges in the

　　depth map. Therefore vehicles are distinguished by the distance

　　from the cameras. Each peak in the histogram correlates to an

　　object in the image.

　　科研申请案例

　　Research Application Case

　　X 同学

　　学校：Thayer Academy

　　年级：高二

　　标化成绩：托福 110 ACT34 GPA4.1 AP4 门

　　录取院校：CMU，北卡教堂山