Rocco RESTAINO | IMAGE PROCESSING
Rocco RESTAINO IMAGE PROCESSING
cod. 0622700068
IMAGE PROCESSING
| 0622700068 | |
| DIPARTIMENTO DI INGEGNERIA DELL'INFORMAZIONE ED ELETTRICA E MATEMATICA APPLICATA | |
| EQF7 | |
| COMPUTER ENGINEERING | |
| 2022/2023 |
| OBBLIGATORIO | |
| YEAR OF COURSE 2 | |
| YEAR OF DIDACTIC SYSTEM 2017 | |
| AUTUMN SEMESTER |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| ING-INF/03 | 5 | 40 | LESSONS | |
| ING-INF/03 | 4 | 32 | LAB |
| Objectives | |
|---|---|
| THE COURSE AIMS AT ILLUSTRATING THE PRINCIPAL METHODS FOR THE ANALYSIS AND PROCESSING OF DIGITAL IMAGES AND INCLUDES LABORATORY EXERCISES. KNOWLEDGE AND UNDERSTANDING THE OBJECTIVE IS TO PRESENT THE FUNDAMENTALS OF • ACQUISITION AND PROCESSING OF IMAGES AND OTHER MULTIMEDIA DATA. • PRINCIPAL STANDARDS FOR THE REPRESENTATION OF IMAGES AND OTHER MULTIMEDIA DATA APPLYING KNOWLEDGE AND UNDERSTANDING LABORATORY EXERCISES CONSIST IN THE APPLICATION OF SOFTWARE PACKAGES FOR ACQUIRING THE ABILITY TO • APPLY THE PRINCIPAL METHODOLOGIES FOR PROCESSING IMAGES AND OTHER MULTIMEDIA DATA. • UTILIZE TOOLS (E.G., MATLAB, OPEN-CV) FOR PROCESSING IMAGES AND OTHER MULTIMEDIA DATA. |
| Prerequisites | |
|---|---|
| FOR THE SUCCESSFUL ACHIEVEMENT OF THE COURSE GOALS, UNDERGRADUATE LEVEL MATHEMATICS, WITH PARTICULAR REFERENCE TO MATRIX CALCULUS AND PROBABILITY THEORY AND BASIC KNOWLEDGE OF SIGNAL PROCESSING ARE REQUIRED. |
| Contents | |
|---|---|
| LEARNING UNIT 1: INTRODUCTION TO IMAGE ACQUISITION AND PROCESSING (LECTURE/PRACTICE/LABORATORY HOURS: 6/0/8) 1 (2H LEC): INTRODUCTION TO THE COURSE. 2 (2H LEC): PIN-HOLE MODEL REAL MODELS: DEPTH OF FIELD, OPTICAL DISTORTIONS. SHUTTERING, IRIS, APERTURE. 3 (2H LAB): INTRODUCTION TO MATRIX PROCESSING 4 (2H LEC): IMAGE CHARACTERISTICS. SAMPLING, RESOLUTION 5 (2H LAB): SOLUTION OF SYSTEMS. EIGENVALUES, EIGENVECTORS. GRAPHING. 6 (2H LAB): HISTOGRAMS OF IMAGE INTENSITIES. EMPIRICAL MOMENTS. 7 (2H LAB): STORAGE AND DISPLAY OF DIGITAL IMAGES. KNOWLEDGE AND UNDERSTANDING ACQUISITION AND STORAGE OF IMAG. APPLYING KNOWLEDGE AND UNDERSTANDING ANALYZE THE DISTRIBUTION OF PIXEL INTENSITIES IN AN IMAGE. LEARNING UNIT 2: IMAGE PROCESSING IN THE SPATIAL DOMAIN. (LEC/PRA/LAB: 6/0/8) 8 (2H LEC): POINT PROCESSING OF IMAGES. HISTOGRAMS, EQUALIZATION, CONTRAST ENHANCEMENT. 9 (2H LAB): INTENSITY TRANSFORMATIONS . 10 (2H LEC): SPATIAL TRANSFORMATIONS. LOW-PASS FILTERS. 11 (2H LEC): NONLINEAR FILTERS. HIGH-PASS FILTERS. 12 (2H LAB): LTI SIGNALS AND SYSTEMS. 13 (2H LAB): INTRODUCTION TO IMAGE PROCESSING IN OPENCV: ELEMENTARY OPERATIONS. 14 (2H LAB): IMAGE PROCESSING IN PYTHON/OPENCV. KNOWLEDGE AND UNDERSTANDING IMAGE PROCESSING USING LINEAR AND NONLINEAR FILTERS. APPLYING KNOWLEDGE AND UNDERSTANDING IMPLEMENTING LINEAR AND NONLINEAR FILTERING IN MATLAB AND PYTHON/OPENCV ENVIRONMENT LEARNING UNIT 3: TRANSFORMS AND APPLICATIONS (LEC/PRA/LAB: 8/0/8) 15 (2H LEC): RECALLS OF FOURIER TRANSFORM OF CONTINUOUS SIGNALS. IDEAL SAMPLING AND ALIASING 16 (2H LEC): DTFT AND DFT. NUMERICAL PROCESSING WITH THE DFT. 17 (2H LAB): DFT OF SIGNALS CONVERTED FROM ANALOG TO DIGITAL. 18 (2H LAB): DECIMATION AND ALIASING. 19 (2H LEC): DTFT AND TWO-DIMENSIONAL DFT AND ALIASING. TWO-DIMENSIONAL DFT. 20 (2H LEC): CALCULATION OF CONVOLUTION USING DFT. 21 (2H LAB): IMAGE PROCESSING IN THE TRANSFORMED DOMAIN. 22 (2H LAB): ELIMINATION OF NOISE IN IMAGES KNOWLEDGE AND UNDERSTANDING DEALING WITH IMAGES USING NUMERICAL PROCESSORS APPLYING KNOWLEDGE AND UNDERSTANDING IMPLEMENTING IMAGE TRANSFORMATIONS USING DFT. LEARNING UNIT 4: GEOMETRIC TRANSFORMATIONS. (LEC/PRA/LAB: 2/0/2) 23 (2H LEC): GEOMETRIC TRANSFORMATIONS. INTERPOLATION. REGISTRATION OF IMAGES. 24 (HOURS LAB 2): IMPLEMENTATION OF GEOMETRIC TRANSFORMATIONS AND INTERPOLATION TECHNIQUES. KNOWLEDGE AND UNDERSTANDING USING AFFINE TRANSFORMATIONS TO PERFORM GEOMETRIC TRANSFORMATIONS. APPLIED KNOWLEDGE AND UNDERSTANDING SKILLS IMPLEMENTING IMAGE TRANSFORMATIONS AND RESAMPLING AN IMAGE. LEARNING UNIT 5: COLOR IMAGE PROCESSING. (LEC/PRA/LAB: 4/0/2) 25 (2H LEC): COLOR IMAGES, MULTISPECTRAL. COLOR PLANES. 26 (2H LEC): PLANE CONVERSIONS. COMPRESSION AND EXPANSION OF COLOR LAYERS. 27 (2H LAB): PROCESSING OF COLOR IMAGES. KNOWLEDGE AND UNDERSTANDING REPRESENTATION OF COLOR IMAGES IN DIFFERENT COLOR SPACES. APPLYING KNOWLEDGE AND UNDERSTANDING USING COLOR SPACES FOR IMAGE PROCESSING. LEARNING UNIT 6: IMAGE ENCODING AND COMPRESSION. (LEC/PRA/LAB: 4/0/2) 28 (2H LEC): IMAGE ENCODING: QUANTIZATION AND LINEAR PREDICTION, DIGITAL IMAGE REPRESENTATION STANDARDS 29 (2H LEC): IMAGE COMPRESSION 30 (2H LAB): DFT AND WAVELET-BASED IMAGE COMPRESSION TECHNIQUES. KNOWLEDGE AND UNDERSTANDING PRINCIPLES OF IMAGE CODING AND COMPRESSION APPLYING KNOWLEDGE AND UNDERSTANDING COMPRESSING IMAGES USING TRANSFORMS LEARNING UNIT 7: EXTRACTING INFORMATION FROM IMAGES. (LEC/PRA/LAB: 4/0/8) 31 (2H LEC): EGDE-DETECTION, CONTOUR-EXTRACTION, HOUGH TRANSFORM ALGORITHMS, 32 (2H LEC): SEGMENTATION. FEATURE DETECTION 33 (2H LAB): CONTOUR DETECTION. 34 (2H LAB): CANNY'S ALGORITHM. HOUGH'S TRANSFORM. 35 (2H LAB): THRESHOLDING. 36 (2H LAB): SEGMENTATION BY CLUSTERING. KNOWLEDGE AND UNDERSTANDING REVEALING SALIENT FEATURES FROM IMAGES APPLYING KNOWLEDGE AND UNDERSTANDING DETECT CONTOURS IN AN IMAGE. PERFORM IMAGE SEGMENTATION. |
| Teaching Methods | |
|---|---|
| THE COURSE INCLUDES LECTURES AND LABORATORY PRACTICE. IN LABORATORY HOURS SOME APPLICATION EXAMPLES OF THE PRESENTED METHODS ARE ASSIGNED AND INTERACTIVELY COMPLETED, IN THE MATLAB ENVIRONMENT AND IN PYTHON WITH THE OPEN-CV LIBRARY, AS FOR EXAMPLE: POINT OPERATIONS, FILTERING, TRANSFORMS, EGDE-DETECTION, CONTOUR-EXTRACTION, SEGMENTATION, COREGISTRATION. |
| Verification of learning | |
|---|---|
| THE FINAL EXAM IS AIMED AT EVALUATING: THE KNOWLEDGE AND UNDERSTANDING OF THE CONCEPTS PRESENTED IN THE COURSE, THE ABILITY TO APPLY THAT KNOWLEDGE TO SOLVE PROBLEMS OF IMAGE ANALYSIS AND THE DESIGN OF DIGITAL IMAGE PROCESSORS, INDEPENDENCE OF JUDGMENT, COMMUNICATION SKILLS AND THE LEARNING ABILITY. THE EXAM CONSISTS OF A DISCUSSION OF THE MINI-PROJECTS ASSIGNED DURING THE COURSE AND OF A FINAL PROJECT, WHOSE PURPOSE IS TO ASSESS THE ABILITY TO APPLY KNOWLEDGE AND THE INDEPENDENCE OF JUDGMENT, AND AN ORAL INTERVIEW, FOR ASSESSING THE ACQUIRED KNOWLEDGE, THE UNDERSTANDING ABILITY, THE LEARNING SKILL, AND THE ORAL PRESENTATION. THE MINI-PROJECTS AND THE FINAL PROJECT CONSIST IN THE REALIZATION OF COMPUTER PROGRAMS, BASED ON THE OPEN-CV LIBRARY, FOR THE SOLUTION OF THE PROBLEMS PRESENTED IN THE COURSE, INCLUDING: 1) POINT PROCESSING OF IMAGES; 2) FILTERING TECHNIQUES. LINEAR AND NON-LINEAR FILTERING. INTERPOLATION 3) EGDE-DETECTION CONTOUR-EXTRACTION, SEGMENTATION ALGORITHMS; 4) USE OF FOURIER, HOUGH, WAVELET TRANSFORMS. THE ORAL EXAMINATION WILL COVER ALL THE TOPICS OF THE COURSE AND ASSESSMENT WILL TAKE INTO ACCOUNT THE KNOWLEDGE DEMONSTRATED BY THE STUDENT AND THE DEGREE OF ITS DEPTH, PROVEN ABILITY TO LEARN, THE QUALITY OF THE PRESENTATION. IN THE FINAL EVALUATION, EXPRESSED IN THIRTIETHS, THE EVALUATION AND THE DISCUSSION OF THE MINI-PROJECTS WILL ACCOUNT FOR 15%, THE FINAL PROJECT AND THE RELATED ORAL DISCUSSION WILL ACCOUNT FOR 40%, WHILE THE INTERVIEW FOR 45%. THE CUM LAUDE MAY BE GIVEN TO STUDENTS WHO DEMONSTRATE THAT THEY CAN AUTONOMOUSLY APPLY THE ACQUIRED KNOWLEDGE TO DIVERSE PROBLEMS. |
| Texts | |
|---|---|
| R.C. GONZALEZ, R.E. WOODS. DIGITAL IMAGE PROCESSING, 4TH ED. PEARSON, 2017 R.C. GONZALEZ, R.E. WOODS, S.L. EDDINS. DIGITAL IMAGE PROCESSING USING MATLAB, 3RD ED., GATESMARK PUBLISHING, 2020 |
| More Information | |
|---|---|
| LABORATORY EXERCITATIONS WILL BE CARRIED OUT INTERACTIVELY. TEACHING IN ITALIAN. |
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