数字图像处理_2012_-课程介绍_龚永义_

数字图像处理_2012_-课程介绍_龚永义_
主要讲述：齐次坐标、几何变换矩阵，图像的比例缩放、平移、镜像、旋转、透视变换与复合变换。

重点：几何变换的特点、齐次坐标、图像旋转、双线性插值。

难点：图像的复合变换与透视变换。

第八章图像编码（可选内容）
主要讲述：图像编码的目的与意义，编码的基本原理、方法与评价，哈夫曼编码、香农范诺编码、行程长度编码、LZW编码、算术编码、JPEG 编码。

重点：哈夫曼编码、行程长度编码、LZW编码、JPEG编码的算法与实现。

难点：算术编码、JPEG编码的算法与实现。

第九章数学形态学及其应用
主要讲述：数学形态学的基本概念，二值形态学腐蚀、膨胀、开、闭运算，击中击不中变换，灰值腐蚀、膨胀、开、闭运算，形态学滤波，骨架抽取，细化算法。

重点：二值形态学腐蚀、膨胀、开、闭运算，细化算法。

难点：击中击不中变换，骨架抽取。

第十章图像分割与边缘检测
主要讲述：图像分割的概念、原理及方法，阈值分割技术，区域增长与聚合，边缘检测与微分运算，轮廓跟踪与提取，图像匹配，投影法与差影法。

重点：判别分析法确定阈值，轮廓跟踪，模板匹配，差影法。

难点：模板匹配与直方图匹配，区域生长与区域聚合。

第十一章图像特征与理解（可选内容）
主要讲述：特征分析的基本方法，图像的几何特征、形状特征、纹理特征及其他特征的定义及
其在图像分析中的应用，中轴变换，曲线与表面的拟合。

重点：图像几何特征、形状特征，边界链码，欧拉数与孔洞数。

难点：纹理分析，中轴变换，曲线与表面的拟合。

主要教学方法与媒体要求
本课程主要采用课堂教学和课后实验相结合的方法，建议学生课后完成下列实验，巩固课堂知识。

数字图像处理课后实验内容及所需设备
章。

考核方式：考查
成绩分布：
平时成绩40%（课后实验和课堂到课率）
期终考查60%（期末大作业）
Syllabus for 《Image Processing》
Course Code: XX31310
Course Category: Special Elective Module Courses
Class Hours: 3 per week, 54 in total Course Credits: 3
Prerequisites: Higher Algebra, Linear Algebra, Probability & Statistics, C++ Programming or Matlab, Data Structure, Algorithm Analysis & Design
Objectives and Requirements:
This course try to develop an overview of the field of image processing; Understand the fundamental algorithms and how to implement them; Prepare to read the current image processing research literature, Gain experience in applying image processing algorithms to real problems.
Textbook(s):
Gonzalez R. C.,Woods R. E. Digital Image Processing(Second Edition). Peking: Electronic & Industry Press, 2002 References:
[1]Wang runsheng, Image Understanding,
Defense Science & Technology Press, 1995.
[2]Cui yi, Digital image processing
technology and applications,
Electronic & Industry Press, 1997
[3]Lv fenjun, Introduce to digital image
processing programming, TsingHua
University Press, 1999
[4]He Bing, et al, VC++ digital image
processing, People Post Press, 2001
[5]Zhang YuJing, Image Engineering:
Image Processing, TsingHua Press, 2006
Contents:
This course covers the fundamentals of digital image processing from both algorithmic and implementation perspectives. In addition to the standard images from the image processing literature, varies images will be used to test some the approaches and to illustrate the difficulties that are typically encountered in real problems of image processing. The contents include,
•Introduction: What is image processing? What are the fundamental issues in image processing? What is the role of perception?
•Basic image models and definitions: pixels, sampling, quantization, resolution, representation as a matrix, operations, camera angles and perspective transformations
•Image transformations: Fourier transform and spectral analysis, separable transformations, principal component analysis, wavelets.
•Image enhancement: histograms, subtraction, averaging, spatial
filtering and sharpening, low-pass and high-pass filtering, etc.
•Image restoration: brief overview and models (Elective)
•Image compression: fundamental principles, compression models, variable-length coding, predictive
coding, JPEG and GIF standards.
(Elective)
•Image segmentation: line and edge
detection, boundary detection,
thresholding, region-oriented
approaches.
•Topological approaches:
representations of boundaries and
regions, morphology.
•Image recognition: statistical
classifiers, neural network approaches.
(Elective)
•Advanced topics and student
presentations (Elective)
Assessment: examination
Grading:
regular grade 40% (including coursework and attendance of the class) final exam grade 60%。