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Syllabus 2019-20 - 13113012 - Industrial Perception Systems (Sistemas de percepción industrial)
- Level 1: Tutorial support sessions, materials and exams in this language
- Level 2: Tutorial support sessions, materials, exams and seminars in this language
- Level 3: Tutorial support sessions, materials, exams, seminars and regular lectures in this language
| DEGREE: | Grado en Ingeniería electrónica industrial |
| FACULTY: | SCHOOL OF ENGINEERING OF JAÉN |
| ACADEMIC YEAR: | 2019-20 |
| COURSE: | Industrial Perception Systems |
| NAME: Industrial Perception Systems | |||||
| CODE: 13113012 | ACADEMIC YEAR: 2019-20 | ||||
| LANGUAGE: English | LEVEL: 2 | ||||
| ECTS CREDITS: 6.0 | YEAR: 4 | SEMESTER: SC | |||
| NAME: SATORRES MARTÍNEZ, SILVIA MARÍA | ||
| DEPARTMENT: U133 - ING. ELECTRÓNICA Y AUTOMATICA | ||
| FIELD OF STUDY: 520 - INGENIERÍA DE SISTEMAS Y AUTOMÁTICA | ||
| OFFICE NO.: A3 - 426 | E-MAIL: satorres@ujaen.es | P: 953213381 |
| WEBSITE: No procede | ||
| ORCID: https://orcid.org/0000-0003-0154-4125 | ||
| LANGUAGE: - | LEVEL: 2 | |
I. INTRODUCTION
Lesson 1. An Introduction to Industrial Perception Systems
- Concepts and definitions. Introduction to Computer Vision.
- Fields of application.
- Devices.
II. IMAGE PROCESSING CHAIN
Lesson 2. Image Acquisition.
- Vision sensors.
- Optical systems.
- Lighting in machine vision.
Lesson 3. Pre-processing
- Contrast.
- Noise reduction.
- Image enhacement.
Lesson 4. Edge extraction
- Introduction.
- Gradient detectors.
- Laplacian detectors.
- Canny edge detector.
Lesson 5. Image segmentation.
- Introduction.
- Line detection using hough transform.
- Thresholding.
- Region-based segmentation.
Lesson 6. Features extraction
- Introduction.
- Boundary descriptors.
- Regional descriptors.
Lesson 7. Object recognition.
- Foundations.
- Decision functions.
- Parametric classifiers.
- Non-parametric classifiers.
III. MACHINE VISION SYSTEMS
- Industrial machine vision inspection.
- Robot vision.
PRACTICE
- P0. Matlab introduction.
- P1. Graphical user interfaces.
- P2. Noise removing.
- P3. Edge detection (I).
- P4. Edge detection (II).
- P5. Segmentation (I).
- P6. Segmentation (II).
- P7. Segmentation (III).
- P8. Feature extraction (I).
- P9. Feature extraction (II).
- P10. Object recognition (I).
- P11. Object recognition (II).
Lecture and programming practices are part of the activities included in the course. In addition, industrial applications using computer vision will be presented (Project based learning).
Students with special educational needs should contact the Student Attention Service (Servicio de Atención y Ayudas al Estudiante) in order to receive the appropriate academic support
In order to pass the course the following issues have to be fulfilled:
- Work lab attendant is mandatory (only one session could be made up for at the end of the course)
- Pass the final written exam (theory and problems).
- Pass the programming work.
Final written exam (assessed comp.): CB3R, CB5R, CEX7, CEX8
Programming work (assessed comp.): CB3R, CB5R, CEX7, CEX8
The following learning results are achieved when the student passes the course: Result 34, Result35, Resultado 36, Result 37, Result 38, Result 39.
- Digital Image processing using MATLAB. Edition: 2nd ed., 16th reprint. Author: González, Rafael C.. Publisher: New Delhi : McGraw Hill Education, 2016 (Library)
- MATLAB: advanced GUI development. Edition: -. Author: Smith, Scott T.. Publisher: Indianapolis : Dog Ear, 2006 (Library)
- Computer vision. Edition: -. Author: Shapiro, Linda G.. Publisher: Upper Saddle River (New Jersey): Prentice Hall, 2001 (Library)