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Course syllabus - Deep Learning for Industrial Imaging

Scope

2.5 credits

Course code

DVA476

Valid from

Autumn semester 2019

Education level

Second cycle

Progressive Specialisation

A1N (Second cycle, has only first-cycle course/s as entry requirements).

Main area(s)

Computer Science

School

School of Innovation, Design and Engineering

Ratified

2019-01-24

Literature lists

Course literature is preliminary up to 8 weeks before course start. Course literature can be valid over several semesters.

    • Ratified date: 2019-09-10
    • Latest update: 2019-09-10

    Reference Literature

    The contents of this course are very vast and you can find a lot of information on Internet and on YouTube. However, this course followed few books but are not mandatory to buy for the course. If you want to have a nice handbook for your future then you can consider to buy them.

    Gonzalez, Rafael C.; Woods, Richard E.

    Digital image processing

    [New ed.] : Upper Saddle River, N.J. : Prentice Hall, cop. 2002 - xx, 793 s.

    ISBN: 0201180758 LIBRIS-ID: 8395912

    URL: Bokens hemsida

    Demant, Christian.; Streicher-Abel, Bernd.; Garnica, Carsten.

    Industrial Image Processing : Visual Quality Control in Manufacturing

    2nd revised ed. 2013. : Berlin, Heidelberg : Springer Berlin Heidelberg, 2013 - XVII, 369 p. 252 illus., 20 illus. in color.

    ISBN: 9783642339059 LIBRIS-ID: 14809480

    URL: Table of Contents / Abstracts

    Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron

    Deep learning

    Cambridge, MA : MIT Press, [2016] - xxii, 775 pages

    ISBN: 978-0-262-03561-3 LIBRIS-ID: 19973915

Objectives

The aim of this course is to provide students with the fundamentals of image processing and deep learning models. The student will learn to design intelligent systems using deep learning, e.g., convolutional neural network for classification, annotation, and object recognition.

Learning outcomes

After completed the course, the student will be able to
1. Demonstrate the fundamental theory of image processing. 
2. Describe the fundamental needs, challenges and limitations of Big data with industrial imaging. 
3. Describe and understand the basic principles of convolution neural network. 
4. Demonstrate the ability to use tools for deep learning in industrial imaging

Course content

Image processing: Introduction of industrial imaging through big data and fundamentals of image processing techniques.  

Deep learning with convolutional neural network: overview of neural networks as classifiers, introduction to convolutional neural networks and Deep learning architecture. 

Deep learning tools: implementation of Deep learning for Image classification and object recognition, e.g. using Keras.

Specific requirements

90 credits of which at least 60 credits in Computer Science or equivalent, including at least 15 credits in programming. In addition, Swedish course B/Swedish course 3 and English course A/English course 6 are required. For courses given entirely in English exemption is made from the requirement in Swedish course B/Swedish course 3.

Grade

Two-grade scale