IA-2-022 (0) - Computer vision

• Information technology 2014 - Machine learning and decision support system development
• Information technology 2015 - Machine learning and decision support system development
• Information technology 2016 - Machine learning and decision support system development
• Information technology 2017 - Machine learning and decision support system development
• Information technology 2018 - Machine learning and decision support system development
• Information technology 2019 - Machine learning and decision support system development
• Information technology 2020 - Machine learning and decision support system development
• Information technology 2021 - Machine learning and decision support system development - advanced studies
• Information technology 2022 - Machine learning and decision support system development - advanced studies
• Information technology 2023 - Machine learning and decision support system development - advanced studies

3 (2023-01-01 to 2023-03-19)

Swedish

Compulsory

First

3

5 cr

In this study unity we will focus on the following
competences:

Machine learning, decision support system
developemtn and artificial intelligence with focus
on:

Deep learning

Object detection and localization

After completed study unit:

You know how a digital image is structured and how
different filters can be applied to modify or
enhance specific features (knowledge)

You know how commont algorithms for face detection
and recognition works (knowledge)

You understand the basics of how neural and
convolutional neural networks work (knowledge)

You can apply algorithms for face detection and
recognition in programming code (skill)

You can design and train deep learning models for
image classification and object detection (skill)

You see the potential of computer vision
applications and how they can be utilized in
various areas (approach)

Introduction to computervision and the OpenCV
Library

Pyhon and OpenCV in Anaconda

Image processing basics

Pxel relations

Statistics and Histograms

Histogram equalization

Filtering

Face detection and recognition

Introduction to neural networks

Deep learning for image clasification and object
detection

Data Processing

• Lectures - 40 hours
• Project- and production work/artistic activities - 95 hours

• Total workload of the course: 135 hours
• Of which autonomous studies: 135 hours
• Of which scheduled studies: 0 hours

Participation in tuition

QUIZ tests and
programming project

The course grade is assessed based on 4 smaller QUIZ
tests
(weight 20%) and a programming project (weight 80%).
A minimum of 50% of the total points is required
for passed course.

Karlsson Jonny

Karlsson Jonny

https://arcada.itslearning.com/ External link

No limit (28 students enrolled)

12 months after course has ended

2022-12-26 to 2023-01-22

Date will be announced later - Other assignments