EPN-V2

ACIT4710 Digital Signal and Image Processing Course description

Course name in Norwegian
Digital Signal and Image Processing
Study programme
Master's Programme in Applied Computer and Information Technology
Weight
10.0 ECTS
Year of study
2020/2021
Programme description
Course history

Introduction

Exam in two parts:

  • A group project: implementation and report (about 7000 words). A group of 2-3 students will be formed during the course.
  • Individual oral exam (about 30 minutes).

Each of them carries 50% weight in the final grade. The oral examination cannot be appealed.

Both exams must be passed in order to pass the course.

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for registering for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

Recommended preliminary courses

Signal Processing or knowledge of the Fourier and Laplace transforms. Some knowledge of Matlab progamming.

Required preliminary courses

All aids are permitted for the project report, provided the rules for plagiarism and source referencing are complied with.

No aids are permitted for the oral exam.

Learning outcomes

Grade scale A-F.

Content

This course will contain the following topics:

  • Sampling, reconstruction and aliasing
  • Impulse response, Point spread function
  • Fourier series and Fourier transform
  • Frequency analysis and k-space analysis
  • Frequency response, filters and transfer functions
  • Detection methods, correlation, convolution and modulation
  • Inverse methods for image reconstruction

Teaching and learning methods

Two internal examiners. External examiner is used periodically.

Course requirements

Associate Professor Raju Shrestha

Assessment

  • Bachelor level knowledge in linear algebra, vector calculus, and basic statistics, and probability is important for understanding some of the concepts in this course.
  • Knowledge and skills in programming, particularly Python, and machine learning frameworks such as scikit-learn, TensorFlow, and Keras.

Permitted exam materials and equipment

This course covers principles of machine learning and deep learning methods and best practices in solving problems effectively. Most of the problems are related and applicable in various areas such as computer vision, surveillance, assistive technology, medical imaging, etc. Therefore, the course intends to provide case studies and examples of ML and DL in solving various problems. Students can explore the tremendous potential of modern AI, ML, and DL methods and techniques in solving problems in different application domains through project work.

Grading scale

For the final assessment a grading scale from A to E is used, where A denotes the highest and E the lowest pass grade, and F denotes a fail.

Examiners

One internal examiners. External sensor is used periodically. If oral, two internal examiners.