Gennaro PERCANNELLA | Model and Systems for Artificial Vision
Gennaro PERCANNELLA Model and Systems for Artificial Vision
cod. 0622700025
MODEL AND SYSTEMS FOR ARTIFICIAL VISION
| 0622700025 | |
| DIPARTIMENTO DI INGEGNERIA DELL'INFORMAZIONE ED ELETTRICA E MATEMATICA APPLICATA | |
| EQF7 | |
| COMPUTER ENGINEERING | |
| 2017/2018 |
| YEAR OF COURSE 2 | |
| YEAR OF DIDACTIC SYSTEM 2016 | |
| PRIMO SEMESTRE |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| ING-INF/05 | 3 | 24 | LESSONS | |
| ING-INF/05 | 3 | 24 | LAB |
| Objectives | |
|---|---|
| THE COURSE AIMS AT PROVIDING THE COMPETENCES ON THE MAIN METHODOLOGIES AND TECHNIQUES REQUIRED TO REALIZE AN ARTIFICIAL VISION SYSTEM. KNOWLEDGE AND UNDERSTANDING KNOWLEDGE OF THE DIFFERENT TASKS CARRIED OUT WITHIN AN ARTIFICIAL VISION SYSTEM, AND IN PARTICULAR WITH REGARDS TO THE LOW LEVEL PROCESSING PHASES (ACQUISITION, FILTERING), TO THE INTERMEDIATE LEVEL PHASES (REGIONALIZATION AND CONTOURS EXTRACTION) AND TO THE HIGH LEVEL PROCESSING (SHAPE RECOGNITION, TRACKING), AS WELL AS UNDERSTANDING OF THE BASIC TECHNIQUES FOR IMPLEMENTING SUCH FUNCTIONS. APPLYING KNOWLEDGE AND UNDERSTANDING BEING ABLE TO USE THE FUNCTIONS OF AN ARTIFICIAL VISION SOFTWARE LIBRARY (OPENCV) FOR THE IMPLEMENTATION OF APPLICATIONS FOR THE ANALYSIS AND INTERPRETATION OF IMAGES AND VIDEOS. |
| Prerequisites | |
|---|---|
| IN ORDER TO ACHIEVE THE GOALS OF THE COURSE, THE KNOWLEDGE OF THE C PROGRAMMING LANGUAGE IS REQUIRED. |
| Contents | |
|---|---|
| COURSE INTRODUCTION: HISTORICAL INTRODUCTION TO THE ARTIFICIAL VISION SYSTEMS. THE PROCESSING PHASES OF AN ARTIFICIAL VISION SYSTEM. (HOURS LECTURES/EXERCITATIONS/LABORATORY 2/0/0) LOW LEVEL PROCESSING: IMAGES REPRESENTATION. COLORS REPRESENTATION. IMAGE ACQUISITION, OPTICS AND SENSORS. IMAGE FILTERING AND PROCESSING. (HOURS LECTURES/EXERCITATIONS/LABORATORY 4/2/2) INTERMEDIATE LEVEL PROCESSING: CONNECTED COMPONENTS AND SEGMENTATION. EDGE DETECTION AND CONTOUR EXTRACTION. DETECTION OF SIMPLE GEOMETRIC SHAPES: HOUGH TRANSFORM. SALIENT POINTS DETECTION. VISUAL DESCRIPTOR COMPUTATION: LBP, HOG (HOURS LECTURES/EXERCITATIONS/LABORATORY 4/2/2) HIGH LEVEL PROCESSING: INTRODUCTION TO MACHIN LEARNING, NEAREST NEIGHBOUR CLASSIFIERS (NN), NEURAL NETWORKS LEARNING VECTOR QUANTIZATION (LVQ) AND BACK PROPAGATION (BP), SUPPORT VECTOR MACHINE (SVM) (HOURS LECTURES/EXERCITATIONS/LABORATORY 4/2/0) APPLICATIONS: FACE DETECTION AND RECOGNITION: VIOLA JONES ALGORITHM, FEATURES FOR FACE DETECTION (LBP AND SALIENT POINTS) (HOURS LECTURES/EXERCITATIONS/LABORATORY 2/0/4) AUTOMATIC VIDEO INTERPRETATION: OBJECT DETECTION, BACKGROUND SUBTRACTION AND UPDATING. OBJECT TRACKING AND CLASSIFICATION, SUSPICIOUS BEHAVIOR DETECTION. APPLICATION FIELDS OF INTELLIGENT VIDEO SURVEILLANCE SYSTEMS. (HOURS LECTURES/EXERCITATIONS/LABORATORY 2/0/4) ACTION AND GESTURE RECOGNITION: ALGORITGMS FOR RECOGNIZING GESTURES AND ACTIONS. APPLICATION FIELDS. (HOURS LECTURES/EXERCITATIONS/LABORATORY 1/0/1) ROBOT VISION: OBJECTIVES OF ROBOTIC VISION, STEREO VISION ALGORITHMS. APPLICATIONS FIELDS. (HOURS LECTURES/EXERCITATIONS/LABORATORY 1/0/1) DRONE VISION: ALGORITHMS FOR DETECTING AND CLASSIFYING OBJECTS FROM AEREAL PLATFORMS. APPLICATIONS FIELDS. (HOURS LECTURES/EXERCITATIONS/LABORATORY 1/0/1) EMBEDDED VISION: ARTIFICIAL VISION ON WEARABLE COMPUTERS AND EMBEDDED SYSTEMS. ISSUES AND APPLICATIONS. (HOURS LECTURES/EXERCITATIONS/LABORATORY 1/0/1) |
| Teaching Methods | |
|---|---|
| THE COURSE CONTAINS THEORETICAL LECTURES, IN-CLASS EXERCITATIONS AND PRACTICAL LABORATORY EXERCITATIONS. DURING THE IN-CLASS EXERCITATIONS THE STUDENTS ARE DIVIDED IN TEAMS AND ARE ASSIGNED SOME PROJECT-WORKS TO BE DEVELOPED ALONG THE DURATION OF THE COURSE. THE PROJECTS INCLUDE ALL THE CONTENTS OF THE COURSE AND IS ESSENTIAL BOTH FOR THE ACQUISITION OF THE RELATIVE ABILITIES AND COMPETENCES, AND FOR DEVELOPING AND REINFORCING THE ABILITY TO WORK IN A TEAM. IN THE LABORATORY EXERCITATIONS THE STUDENTS IMPLEMENT THE ASSIGNED PROJECTS USING THE OPENCV SOFTWARE LIBRARIES. IN ORDER TO PARTICIPATE TO THE FINAL ASSESSMENT AND TO GAIN THE CREDITS CORRESPONDING TO THE COURSE, THE STUDENT MUST HAVE ATTENDED AT LEAST 70% OF THE HOURS OF ASSISTED TEACHING ACTIVITIES. |
| Verification of learning | |
|---|---|
| THE EXAM AIMS AT EVALUATING, AS A WHOLE: THE KNOWLEDGE AND UNDERSTANDING OF THE CONCEPTS PRESENTED IN THE COURSE, THE ABILITY TO APPLY THAT KNOWLEDGE TO SOLVE PROGRAMMING PROBLEMS REQUIRING THE USE OF ARTIFICIAL VISION TECHNIQUES; INDEPENDENCE OF JUDGMENT, COMMUNICATION SKILLS AND THE ABILITY TO LEARN. THE EXAM INCLUDES TWO STEPS: THE FIRST ONE CONSISTS IN AN ORAL EXAMINATIONS AND IN THE DISCUSSION OF MID TERM PROJECTS REALIZED DURING THE COURSES. THE SECOND STEP CONSISTS IS BASED ON THE REALIZATION OF A FINAL TERM PROJECT: THE STUDENTS, PARTITIONED INTO TEAMS, ARE REQUIRED TO REALIZE A SYSTEM, FINALIZED TO A COMPETITION AMONG THE TEAMS, DESIGNING AND METHODOLOGICAL CONTRIBUTIONS OF THE STUDENTS, TOGETHER WITH THE SCORE ACHIEVED DURING THE COMPETITION, ARE CONSIDERED FOR THE EVALUATION. THE AIM IS TO ASSESS THE ACQUIRED KNOWLEDGE AND ABILITY TO UNDERSTANDING, THE ABILITY TO LEARN, THE ABILITY TO APPLY KNOWLEDGE, THE INDEPENDENCE OF JUDGMENT, THE ABILITY TO WORK IN A TEAM. IN THE FINAL EVALUATION, EXPRESSED IN THIRTIETHS, THE EVALUATION OF THE INTERVIEW AND OF THE MID TERM PROJECTS WORK WILL ACCOUNT FOR 40% WHILE THE FINAL TERM PROJECT WILL ACCOUNT FOR 60%. THE CUM LAUDE MAY BE GIVEN TO STUDENTS WHO DEMONSTRATE THAT THEY CAN APPLY THE KNOWLEDGE AUTONOMOUSLY EVEN IN CONTEXTS OTHER THAN THOSE PROPOSED IN THE COURSE. |
| Texts | |
|---|---|
| LECTURE NOTES. SZELISKI. “COMPUTER VISION: ALGORITHMS AND APPLICATIONS”, SPRINGER M. SONKA, V. HLAVAC, R. BOYLE: "IMAGE PROCESSING, ANALYSIS AND MACHINE VISION", CHAPMAN & HALL. |
| More Information | |
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| TEACHING IN ENGLISH |
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