教学目标:
This is an advanced senior and graduate level elective course on Digital Image Processing and Analysis, which provides a comprehensive theory of various image processing tasks and the practical experience to simulate them. Upon the completion of this course, the students will have gained a hands-on experience about the below topics through extensive simulation assignments. ? To study the image fundamentals and mathematical transforms necessary for image processing. ? To study the image enhancement techniques ? To study image restoration procedures. ? To study the image compression procedures. ? To study the image segmentation and representation techniques.
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课程内容:
In this class, we are going to learn about the basic principles and tools used to process images and videos, and how to apply them in solving practical problems. This course on fundamentals of image analysis is core course for graduate students. The course provide general introduction on the basic principles, collection of algorithms on image processing and analysis. The main contents include: digital image fundamentals, spatial image enhancement technique, basic transformations and frequency domain filters, image restoration and reconstruction. Chapter 1: Digital Image Fundamentals 2h What is an image? Image Representation Human visual system Sampling and Fourier analysis Chapter 2:Spatial Image enhancement Technique 4h Basic grey level transformation Histogram equalization Image subtraction Image averaging Spatial filtering: Smoothing, sharpening filters – Laplacian filters Chapter 3:Basic transformations and Frequency domain filters 4h Introduction to Fourier Transform and DFT – Properties of 2D Fourier Transform – FFT – Separable Image Transforms -Walsh – Hadamard – Discrete Cosine Transform, Haar, Slant – Karhunen – Loeve transforms. Frequency domain filters : Smoothing – Sharpening filters – Homomorphic filtering Chapter 4:Image Restoration and Reconstruction 6h Model of Image Degradation/restoration process Noise models Inverse filtering Least mean square filtering Constrained least mean square filtering Blind image restoration Pseudo inverse Singular value decomposition Chapter 5:?Morphological Image Processing 4h Erosion, dilation, opening, closing Basic Morphological Algortihms: hole filling, connected components, thinning, skeletons Chapter 6: Color Image Processing 2h Color Models Color Transforms Image Segmentation Based on color Chapter 7: Image Compression 4h Fundamentals Basic Compression Methods Chapter 8: Image Segmentation and measurements 6h Edge detection – Thresholding - Region Based segmentation Boundary representation: chair codes- Polygonal approximation – Boundary segments boundary descriptors: Simple descriptors-Fourier descriptors - Regional descriptors Texture Requirement for Students: ? Emphasis on understanding Chapter 1,2,3,4,8 ? General on understanding Chapter 5,6,7 ? Homework (4 times) will designed for Matlab Programming on Image Analysis ? Grading depends on Homework (4 times), 20%, and Final Examination 80%.
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参考书目:
Textbooks: [1] Digital Image Processing (3rd edition), Rafael C. Gonzalez and Richard E. Woods, Prentice Hall, 2007, ISBN 013168728X, http://www.imageprocessingplace.com [2] Digital Image Processing Using MATLAB, Rafael C. Gonzalez, Richard E. Woods, and Steven L. Eddins, Prentice-Hall, 2003. ISBN 0130085197
Additional References: [1] Handbook of Image and Video Processing, Publisher: Academic Press; 2 edition (June 21, 2005), Alan C. Bovik (Author) [2] Computer Vision Research Groups:http://www-2.cs.cmu.edu/~cil/v-groups.html ??ITK - Segmentation & Registration Toolkit
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