Industrial Vision Systems

Base Knowledge

NA

Teaching Methodologies

1       – Expository method: a telling method were facts, concepts, principles and generalizations are defined andpresented by the teacher and discussed with the class, followed by demonstrative examples;

2       – Experimental method: an active method were the student develops the knowledge through the use of problemsolving and project development approaches, in group dynamics and individual reflective work. Regarding the evaluation the following methods are used:

Learning Results

To project, use and apply computer vision systems to industry to assess more or less complex properties without mechanical contact between the perception system and the measurement object.

Generally speaking, students are expected to acquire skills in the design and implementation of solutions in industrial nature and eventually of an applied scientific nature.

Program

1. Introduction

2-Vision

Bases, concepts and definitions

Formation and image acquisition: geometric transformations

Low-level image processing: filters and basic operations

Morphology and morphological operations

Region and contour descriptors

Color image: the color spaces

Image recognition: models and patterns

Lighting issues and techniques

Industrial artificial vision systems

3-Other systems of perception

Laser perception systems: 1D and 2D principles and systems

The 3D “cameras”

Other forms of perception.

Grading Methods

Final evaluation
  • - one individual assignment (50%) - 50.0%
  • - one written exam (50%) - 50.0%
Periodic evaluation
  • - individual assignments (75%) - 75.0%
  • - one written exam (25%) - 25.0%

Internship(s)

NAO

Bibliography

Digital Image Processing/W. Burger, M. Burge /2007

W. Burger, M. Burge – Digital Image Processing. Springer, Nov 2007

E. R. Davies – Machine Vision: Theory, Algorithms, Practicalities. Morgan Kaufmann, 2005

M. Sonka, V. Hlavac, R. Boyle – Image Processing: Analysis and Machine Vision. Thomson Learning Vocational, 1998/2007

Rafael C. Gonzalez, Richard E. Woods – Digital Image Processing. Prentice Hall, 2007

Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins – Digital Image Processing Using Matlab. Prentice Hall, 2004

D. Ballard, C. Brown – Computer Vision, Prentice-hall, 1982 (on-line em http://homepages.inf.ed.ac.uk/rbf/BOOKS /BANDB/bandb.html