《视觉计算》实验教学大纲
课程代码 | 045101901 |
课程名称 | 视觉计算 |
英文名称 | VisualComputing（MOOC） |
课程类别 | 选修课 |
课程性质 | 选修 |
学时 | 总学时：48 实验：16 实习：0 其他：0 |
学分 | 2.5 |
开课学期 | 第六学期 |
开课单位 | 计算机科学与工程学院 |
适用专业 | 计算机科学与技术（全英创新班）、（全英联合班） |
授课语言 | 英文授课 |
先修课程 | 数学分析、图像处理 |
毕业要求（专业培养能力） | 本课程对学生达到如下毕业要求有如下贡献： 1.研究：培养学生具备计算机系统相关知识并对计算机工程复杂问题进行研究，具有计算机系统研发基本能力、具备问题分析和建模的能力，具有系统级的认知能力和实践能力，掌握自底向上和自顶向下的问题分析方法。 2.使用现代工具：能够针对计算机工程复杂问题，开发、选择与使用恰当的技术、资源、现代工程工具和信息技术工具。 |
课程培养学生的能力（教学目标） | 完成课程后，学生将具备以下能力： （1）了解视觉信息和计算分析的基本原理，了解如何使用一些数学理论和方法进行图像和视频处理。[1] （2）掌握频谱分析和数字滤波的基本知识，并有良好的基础进一步研究模式识别和计算机视觉。[1] （3）了解视觉计算的基本概念，原理及有效的数学工具。[2] （4）学生能够处理视觉计算中的一些基本问题。[2] |
课程简介 | 视觉计算是计算机科学与技术学科的一门课程，它结合了频谱分析、数字滤波、图像处理、模式识别和计算机视觉。本课程对那些希望进一步学习和在计算机科学与技术开展初步研究的学生有很大帮助。学习本课程后，学生应该了解视觉信息和计算分析的基本原理，了解如何使用一些数学理论和方法进行图像和视频处理，掌握频谱分析和数字滤波的基本知识，并有良好的基础进一步研究模式识别和计算机视觉。教师使得学生了解视觉计算的基本概念，原理及有效的数学工具。学生能够处理视觉计算中的一些基本问题。 |
主要仪器设备与软件 | 仪器设备：PC 软件：MATLAB |
实验报告 | 实验报告需包含以下部分：
总结：总结实验的收获，包括对理论知识理解的加深、对研究方法的掌握和对现代工具使用等。 |
考核方式 | 本课程注重过程考核，成绩比例为： 实验报告：60% 平时表现：40% |
教材、实验指导书及教学参考书目 | 现用教材：M.S.Nixon, A.S. Aguado. Feature Extraction & Image Processing forComputer Vision. (3rd edition). 主要参考资料： [1] H. Ji. Mathematic inVisual Data Processing. National University of Singapore. 2011. [2] G. Aubert and P.Kornprobst, Mathematical problems in image processing, AppliedMathematical Sciences, Vol 147, Springer-Verlag, 2006. [3] S. Mallat, A wavelet tourof signal processing, AP Professional, London,1997. [4]I. Pitas, Digital image processing algorithm and Applications,John Wiley & Sons, New York, 2000. |
制定人及发布时间 | 许勇，2019.4.1 |
《视觉计算》实验教学内容与学时分配
实验项目编号 | 实验项目名称 | 实验学时 | 实验内容提要 | 实验类型 | 实验要求 | 每组人数 | 主要仪器设备与软件 |
1 | 图像基础知识 | 4 | 主要内容：图像矩阵，色彩模式，比特图像，卷积。 要求：了解图像矩阵，色彩模式，比特图像，卷积的代码实现。 | 综合性 | 必做 | 1 | 仪器设备：PC 软件：MATLAB |
2 | 图像处理 | 4 | 主要内容：图像噪声，图像去噪，图像直方图。 要求：了解图像噪声，图像去噪，图像直方图等图像处理相关概念和方法。 | 综合性 | 必做 | 1 | |
3 | 傅里叶分析 | 4 | 主要内容：傅里叶级数，傅里叶变换 要求：使用MatLab实现傅里叶级数，傅里叶变换。 | 综合性 | 必做 | 1 | |
4 | 离散傅里叶分析 | 4 | 主要内容：离散傅里叶变换，二维离散傅里叶变换 要求：使用MatLab实现离散傅里叶变换，二维离散傅里叶变换 | 综合性 | 必做 | 1 |
“VisualComputing（MOOC）”Syllabus
CourseCode | 045101901 |
CourseTitle | VisualComputing（MOOC） |
CourseCategory | ElectiveCourses |
CourseNature | ElectiveCourse |
ClassHours | 48 |
Credits | 2.5 |
Semester | 6th |
Institute | Schoolof Computer Science and Engineering |
ProgramOriented | |
TeachingLanguage | English |
Prerequisites | mathematicalanalysis, image processing |
StudentOutcomes (Special Training Ability) | 1. Research: with the abilityto conduct investigations on the complex engineering problemsbased on scientific principles and adopting scientific methods,including the experiment designs, analyzing and interpretation ofdata, and to obtain valid conclusions by information synthesis. 2.Applying the Modern Tools: with the ability to develop, select anduse the appropriate techniques, resources, and modern tools and ITtools, including prediction and simulation, to solve the complexengineering activities in information security and understand thelimitations. |
TeachingObjectives | After teaching, students havethe following abilities： (1)know the basic principleand analysis of visual information and its computing, understandhow to use some mathematical theories and methods for image andvideo processing.[1] (2)know the basic knowledgeof spectral analysis and digital filtering, and have a good basefor further studying about pattern recognition and computervision.[1] (3)make students to know thebasic concepts of visual computing, and the principle and usage ofeffective mathematical tools.[2] (4)Ableto deal with some basic program in visual computing.[2] |
CourseDescription | Visual Computing is one of thecourses of computer science and technology, which combinesspectral analysis and digital filtering, image processing, patternrecognition and computer vision. This course is beneficial tothose students for further study and start-up research in computerscience and technology. After studying of this course, studentsshould know the basic principle and analysis of visual informationand its computing, understand how to use some mathematicaltheories and methods for image and video processing，knowthe basic knowledge of spectral analysis and digital filtering,and have a good base for further studying about patternrecognition and computer vision. Teacher should make students toknow the basic concepts of visual computing, and the principle andusage of effective mathematical tools. The students are able todeal with some basic program in visual computing. |
Instrumentsand Equipments | Equipment:PC Software:MATLAB |
ExperimentReport | The following parts should beincluded in the experiment report: 1. Introduction: the purpose,principle and environment of the experiment should be conciselyintroduced. 2. Procedure: the design,process and result of the experiment should be explained indetail. The problems encountered in the experiment and solutionsare briefly described 3.Summary: summarize the results of the experiment, including thedeepening of understanding of theoretical knowledge, the grasp ofresearch methods and the use of modern tools. |
Assessment | Report：60% Performancein lab：40% |
TeachingMaterials and Reference Books | Textbook: M.S. Nixon, A.S. Aguado. Feature Extraction & Image Processing for Computer Vision.(3rd edition). Reference Reading : [1] H. Ji. Mathematic inVisual Data Processing. National University of Singapore. 2011. [2] G. Aubert and P.Kornprobst, Mathematical problems in image processing, AppliedMathematical Sciences, Vol 147, Springer-Verlag, 2006. [3] S. Mallat, A wavelet tourof signal processing, AP Professional, London,1997. [4]I. Pitas, Digital image processing algorithm and Applications,John Wiley & Sons, New York, 2000. |
Preparedby Whom and When | Yong Xu,2019.04.01 |
“VisualComputing” ExperimentalTeaching Arrangements
ExperimentItem | ClassHours | ContentSummary | Category | Requirements | Numberof Students Each Group | Instruments,Equipments and Software | |
ImageFundamental | 4 | Contents:Image matrix,Color Mode, Bit image,Convolution. Requirements:Learn how to handle Image matrix, Color Mode, Bit image andConvolution in MatLab. | Comprehensive | Elective | 1 | Equipment: PC Software:MATLAB | |
2 | ImageProcessing | 4 | Contents: Imagenoise, Image denoise, Image histogram Requirements:: Get familiar with basic image processing concepts and methods. | Comprehensive | Elective | 1 | |
3 | FourierAnalysis | 4 | Contents: Fourierseries, Fourier transform Requirements:Implement Fourier series and Fourier transform using MatLab | Comprehensive | Elective | 1 | |
4 | DiscreteFourier Analysis | 4 | Contents:Discrete Fourier Transform, 2D DiscreteFourier Transform Requirements:Implement Discrete Fourier Transform and 2DDiscrete Fourier Transform using MatLab. | Comprehensive | Elective | 1 |
