ROBOTIC VISION

Ingegneria Informatica ROBOTIC VISION

0622700054
DIPARTIMENTO DI INGEGNERIA DELL'INFORMAZIONE ED ELETTRICA E MATEMATICA APPLICATA
EQF7
COMPUTER ENGINEERING
2017/2018



YEAR OF COURSE 2
YEAR OF DIDACTIC SYSTEM 2016
SECONDO SEMESTRE
CFUHOURSACTIVITY
324LESSONS
324LAB
Objectives
The course aims at the acquisition of knowledge on the design and realization for a vision system in an industrial or robotic application.

Knowledge and understanding
Knowledge of the functions of an industrial/robotic vision system, with reference to process control, and robot control and navigation.

Applied knowledge and understanding
How to use the functions of an artificial vision library to realize vision-based applications for process control or robot control and navigation.
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)

ROBOT VISION: OBJECTIVES OF ROBOTIC VISION, STEREO VISION ALGORITHMS. VISION-BASED NAVIGATION. VISION-BASED POSITION CONTROL. APPLICATIONS FIELDS. (HOURS LECTURES/EXERCITATIONS/LABORATORY 4/0/4)

DRONE VISION: ALGORITHMS FOR DETECTING AND CLASSIFYING OBJECTS FROM AEREAL PLATFORMS. APPLICATIONS FIELDS. (HOURS LECTURES/EXERCITATIONS/LABORATORY 2/0/2)

INDUSTRIAL VISION: ARTIFICIAL VISION ON INDUSTRIAL DEVICES AND EMBEDDED SYSTEMS. ISSUES AND APPLICATIONS. (HOURS LECTURES/EXERCITATIONS/LABORATORY 4/0/4)
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 ROBOT 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.
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