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ĐH Bách Khoa – ĐHQG Tp. HCM Faculty of Electrical and Electronics Engineering Department of Telecommunication Engineering

Tp.HCM, ngày 19/11/2013

Undergraduate Syllabus

INTRODUCTION TO DIGITAL AND IMAGE PROCESSING Course Code : 405402 - Credit - No. hours - Grading

: 3 (3.1.4) TCHP: Total: 75 LT: 60 BT: 15 TH: ĐA: : Quizzes: 15% Homework: 15% Midterm exam: 20% Writing exam - 90 min Project: 20% Grading scale 10/10 Final exam: 30% Writing exam - 120 min - Pre-requisite : - Digital Signal Processing - Program : - Level : Senior EE Core course - Note :

BTL/TL:

MS:

1. Course Objectives: The aim of this course is to introduce to the students the basics principle and methods of digital image and video processing. The students will gain overview about the available techniques and possibilities of this field. They will learn basic image transforms, segmentation algorithms and problems of object measurements. They will be able to perform the basic techniques and apply them in practice. The lecture serves as the base for all those who want to attend to the topic in more details. 2. Course outline: This course provides an introduction to the basic concepts and techniques used in digital image and video processing. Two-dimensional sampling and quantization are studied, and the human visual system is reviewed. Edge detection and feature extraction algorithms are introduced for dimensionality reduction and feature classification. High-pass and bandpass spatial filters are studied for use in image enhancement. Applications are discussed in frame interpolation, filtering, coding, noise suppression, and video compression. Some attention will be given to object recognition and classification, texture analysis in remote sensing, and stereo machine vision. 3. Textbook and references: [1] [2] [3] [4]

R. Gonzalez and R. Woods, Digital Image Processing, 3rd Ed., Prentice-Hall 2007. A. K. Jain, Fundamentals of Digital Image Processing, Prentice–Hall, 1989. W. K. Pratt, Digital Image Processing, Wiley, 1991. A. M. Tekalp, Digital Video Processing, Prentice–Hall, 1995 4. Learning Outcomes

Students who complete this course will be able to: PĐT, Mẫu 2008-ĐC

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Syllabus : 409401

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1. Acquire the fundamental concepts of a digital image processing system 2. Examine various types of images, intensity transformations and spatial filtering. 3. Identify and exploit analogies between the mathematical tools used for 1D and 2D signal analysis and processing. 4. Design and implement with Matlab algorithms for digital image processing operations such as histogram equalization, enhancement, restoration, filtering, and denoising. 5. Analyze 2D signals in the frequency domain through the Fourier transform. 6. Use the mathematical principles of digital image enhancement. 7. Implement image process and analysis algorithms. 8. Apply image processing algorithms in practical applications. 9. Describe and apply the concepts of feature detection and contour finding algorithms. 10. Evaluate the methodologies for image segmentation, restoration, topology, etc. 11. Know basic features of MPEG-2, MPEG-4, and H.264 video compression standards. (a,b,c,e,h,i,j,k,l,m) Mapping of course outcomes to program outcomes: Course Outcomes 1 2 3 4 4 5 6 7 8 9 10 11

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5. Learning Strategies & Assessment Scheme: Learning strategies: - Read lecture notes and reference books before each class - Participate lecture hours, apply theory to solve exercises and homeworks Assessment scheme: - Quizzes (15%) - Homeworks (15%) - Mid-term exam (20%) - Project (20%) - Final exam (30%) 6. Instructor: • Dr. Che Viet Nhat Anh

- Faculty of Electrical and Electronics Engineering Tr.2/5


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7. Course content: Week

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Content Chapter 1: Introduction to Image and Video Processing and its applications 1.1 Digital Image and Video Processing Processing vs Image and Video Analysis vs Computer Vision. 1.2 The electromagnetic spectrum, applications of image and video processing. 1.3 Human visual perception 1.4 Image sensing and acquisition 1.5 Image sampling and quantization. 1.6 Some basic relationships between pixels. 1.7 Applications of digital image and video processing Chapter 2: Intensity Transformations and Spatial Filtering 2.1 Background 2.2 Some Basic Intensity Transformation Function 2.3 Histogram Processing 2.4 Fundamental of Spatial Filtering 2.5 Smoothing spatial Filters 2.6 Sharpening Spatial Filters Chapter 3: Filtering in the Frequency Domaine 3.1 Two dimensional sampling 3.2 The 2-D Discrete Fourier Transform (DFT) and Its Inverse 3.3 Some properties of the 2-D DFT 3.4 The basics of filtering in the frequency domain 3.5 Image smoothing using frequency domaine filters 3.6 Image sharpening using frequency domaine filters 3.7 Selective filtering 3.8 Implementation Chapter 4: Image Restoration and Reconstruction 4.1 Noise Models 4.2 Restoration in the Presence of Noise Only - Spatial Filtering 4.3 Periodic noise reduction by frequency domain filtering 4.4 Linear, Position-Invariant Degradations 4.5 Estimating the Degradations Function 4.6 Inverse Filtering 4.7 Minimum Mean Square Error (Wiener) Filtering 4.8 Constrained Least Square Filtering 4.9 Gerometric Mean Filering 4.10 Image Reconstruction from Projections 4.5 Stability of control system describing by state equation 4.6 Stability analysis using Matlab

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Week

Content Chapter 5 Color Image Processing 5.1 Color Fundamental 5.2 Color Models 5.3 Pseudocolor Image Processing 5.4 Basic of full-color image processing 6, 7 5.5 Color transformations 5.6 Smoothing and sharpening 5.7 Image segmentation based on color 5.8 Noise in color images 5.9 Color image compression Review of midterm exam 8-9 Midterm exam Chapter 6: Image Compression 6.1 Fundamentals 10 6.2 Some basic compression methods 6.3 Digital Image Watermarking Chapter 7: Morphological 7.1 Introduction 7.2 Erosion and Dilation 11 7.3 Opening and Closing 7.4 The Hit-or-Miss Transformation 7.5 Some Basic Morphological Algorithms 7.6 Gray-Scale Morphology Chapter 8: Image Segmentation 8.1 Introduction 8.1 Point, Line, and Edge Detection 12,13 8.2 Thresholding 8.3 Region-Based Segmentation 8.4 Segmentation Using Morphological Watersheds 8.5 The use of motion in segmentation Chapter 9: Representation and description 9.1 Representation 9.2 Boundary descriptors 13-14 9.3 Regional descriptors 9.4 Use of principle components for description 9.5 Relational descriptors Chapter 10: Object recognition 10.1 Patterns and Pattern classes 15 10.2 Recognition based on decision-theoretic methods 10.3 Structural methods Chapter 11: Video processing 16 11.1 Video Scanning and Display 11.2 Video Compression Midterm exam content * Chapter 1 - 5 Final exam content ** Chapter 5-11

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8. Contact information: + Dr. Che Viet Nhat Anh, Tel: 0938020128, Email: nhat-anh.che@hcmut.edu.vn HoChiMinhCity, November 19th, 2013 Dean of Faculty of Electrical and Electronics Engineering

Dr. Do Hong Tuan

Instructor

Dr. Che Viet Nhat Anh

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46 405402 intro to image and video processing