Module Code - Title:

RE4012 - MACHINE VISION

Year Last Offered:

2025/6

Hours Per Week:

Lecture

2

Lab

2

Tutorial

1

Other

0

Private

5

Credits

6

Grading Type:

N

Prerequisite Modules:

Rationale and Purpose of the Module:

This module covers one of the key enabling technologies that is necessary for modern robotics design and auto eVehicles, machine vision. At the end of this module students will be able to use common techniques for the design, specification and practical implementation of modern vision systems. The module updates and replaces RE4017 Machine Vision level 9 module with an appropriately coded module for masters level courses in year 4 and or 5 of 5 year integrated BE / ME programmes. This module is to be offered on the Master of Engineering in Electronic and Computer Engineering using module ID 3298 Machine Vision.

Syllabus:

Image Formation: Pin-hole camera model, Projective geometry, colour space RGB & HSL Image Distortion and camera calibration Image Acquisition: Lenses, Camera Systems, Sampling. Low-Level Image Processing for Machine Vision: Filtering, Edge-Detection, Thinning, Photometric Stereo, Shape-From-Shading, Interest point detection. Motion: Motion Field and Optical Flow High-Level Image Processing: Region Segmentation And Labelling, Classification, Object Detection. Neural Approaches To Image Processing. Structure From Motion. Example Application (Picking Parts From A Bin). Stereovision Visual Servoing; Position Based and Image Based Visual Servoing.

Learning Outcomes:

Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)

1 Describe the major algorithms and techniques employed in machine vision systems. 2 Critique approaches to machine vision, outlining the strengths and weaknesses of common approaches. 2 Design and implement computer programs to perform low-level machine vision operations: filtering, edge-detection, thinning, photometric stereo, shape-from-shading; in a suitable computer language. 3 Design and implement computer programs to perform high-level machine-vision operations: segmentation, labeling, classification and detection; in a suitable computer language. 4 Design and implement neural-based image classifiers. 5 Design a complete machine-vision system for an application such as part-picking.

Affective (Attitudes and Values)

None

Psychomotor (Physical Skills)

None

How the Module will be Taught and what will be the Learning Experiences of the Students:

Lectures / Labs / Tutorials

Research Findings Incorporated in to the Syllabus (If Relevant):

Prime Texts:

Duda, R.O. & Hart, P.E (2001) Pattern Classification, 2nd ed. , Wiley

Gonzalez, R.C, Woods, R.E. & Eddins, S.L. (2004) Digital Image Processing using Matlab. , Pearson

Morris, T. (2004) Computer Vision and Image Processing. , Macmillian

Other Relevant Texts:

Bishop, C. (2006) Pattern Recognition and Machine Learning. , Springer

Billingsley, J. (ed.) (2000) Mechatronics and Machine Vision. , Research Studies Press

Jain, R., Kasturi, R. & Schunck, B.G. (1995) Machine Vision, , McGraw-Hill

Programme(s) in which this Module is Offered:

BEECENUFA - ELECTRONIC AND COMPUTER ENGINEERING

BEROENUFA - Robotics Engineering

Semester(s) Module is Offered:

Spring

Module Leader:

TIM.A.BROPHY@UL.IE