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 | |
| 2019/2020 |
| OBBLIGATORIO | |
| YEAR OF COURSE 1 | |
| YEAR OF DIDACTIC SYSTEM 2017 | |
| SECONDO SEMESTRE |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| ING-INF/03 | 4 | 32 | LESSONS | |
| ING-INF/03 | 3 | 24 | EXERCISES | |
| ING-INF/03 | 2 | 16 | 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 | |
|---|---|
| SYSTEMS AND DEVICES FOR IMAGE ACQUISITION (VISIBLE, INFRARED, MULTI-BAND). PIN-HOLE MODEL FOR GEOMETRIC CHARACTERIZATION. REAL MODELS: DEPTH OF FIELD, OPTICAL DISTORTIONS. SHUTTERING, IRIS, DIAPHRAGM (HOURS LECTURES/EXERCITATIONS/LABORATORY 4/0/2). IMAGE FEATURES: SAMPLING, RESOLUTION (HOURS LECTURES/EXERCITATIONS/LABORATORY 2/0/2). BINARY, GRAY-LEVEL, COLOR, MULTISPECTRAL IMAGES. COMPRESSION AND EXPANSION OF COLOR LEVELS. COLOR PLANS AND COLOR DEPTH. RGB, YUV, YCBCR COLOR MODELS. COLOR PLANES CHACATERISTICS AND CONVERSIONS. COLOR. CORRECTION: WHITE-BALANCING ALGORITHMS. (HOURS LECTURES/EXERCITATIONS/LABORATORY 2/0/2) DIGITAL IMAGES REPRESENTATION STANDARDS (HOURS LECTURES/EXERCITATIONS/LABORATORY 2/0/0). POINT PROCESSING OF IMAGES. HISTOGRAMS, EQUALIZATION, CONTRAST ENHANCEMENT (HOURS LECTURES/EXERCITATIONS/LABORATORY 2/0/4). FILTERING TECHNIQUES. LINEAR AND NON-LINEAR FILTERING. INTERPOLATION (HOURS LECTURES/EXERCITATIONS/LABORATORY 6/0/8). EGDE-DETECTION CONTOUR-EXTRACTION, SEGMENTATION ALGORITHMS (HOURS LECTURES/EXERCITATIONS/LABORATORY 4/0/8). TRANSFORMS: FOURIER, HOUGH, WAVELET (HOURS LECTURES/EXERCITATIONS/LABORATORY 6/0/8). IMAGE CODING: QUANTIZATION AND LINEAR PREDICTION, IMAGE COMPRESSION: JPEG, JPEG2000 (HOURS LECTURES/EXERCITATIONS/LABORATORY 2/0/4). OTHER MULTIMEDIA DATA (HOURS LECTURES/EXERCITATIONS/LABORATORY 2/0/4) |
| 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 WITH THE OPEN-CV LIBRARY, AS FOR EXAMPLE: POINT OPERATIONS, FILTERING, TRANSFORMS, EGDE-DETECTION, CONTOUR-EXTRACTION, SEGMENTATION, STEREO IMAGES AND CALIBRATIONS. |
| 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 THE DISCUSSION OF A 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 PROJECT CONSISTS IN THE REALIZATION OF A COMPUTER PROGRAM, 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 OF THE PROJECT AND THE RELATED ORAL DISCUSSION WILL ACCOUNT FOR 50%, WHILE THE INTERVIEW FOR 50%. THE CUM LAUDE MAY BE GIVEN TO STUDENTS WHO DEMONSTRATE THAT THEY CAN AUTONOMOUSLY APPLY THE ACQUIRED KNOWLEDGE TO DIVERSE PROBLEMS. |
| Texts | |
|---|---|
| R.C. GONZALES, R.E. WOODS. DIGITAL IMAGE PROCESSING, PEARSON R.C. GONZALES, R.E. WOODS, S.L. EDDINS. DIGITAL IMAGE PROCESSING USING MATLAB, GATESMARK PUBLISHING A.K. JAIN, FUNDAMENTALS OF DIGITAL IMAGE PROCESSING, PRENTICE HALL |
| More Information | |
|---|---|
| TEACHING WILL BE PROVIDED BY THE LECTURER. LABORATORY EXERCISES WILL BE CARRIED OUT INTERACTIVELY. TEACHING IN ITALIAN. |
BETA VERSION Data source ESSE3 [Ultima Sincronizzazione: 2021-02-19]
