course-details-portlet

TDT4265 - Computer Vision and Deep Learning

About

Course content

Computer vision techniques build the basis for an automatic understanding and interpretation of digital images that today can be recorded with a multitude of different imaging devices (e.g. mobile phones, webcams, 3D depth-images, MRI, Ultrasound... ).
The content of the course ranges from the classical feature extraction and classification approach of vision to the more modern machine / deep learning based way of making sense of images and video. The course also contains a short summary of the programming skills and mathematical background needed as well as a recap of basic image processing & analysis methods in order to make sure that everybody is on the same page.
Topics covered are the human eye and the image formation process, filtering in the spatial and frequency domain, segmentation and mathematical morphology, Hierarchical Structures and Scale space, Principal Components Analysis (PCA), feature extraction (e.g. Harris, SIFT) and matching (e.g. RANSAC), unsupervised and supervised classification & recognition as well as various machine learning methods, stereo vision and essential & fundamental matrixes, optical flow, tracking (Kalman/Particle) and last but not least deep learning for vision, i.e. fully connected feedforward NNs (Shallow ANNs, forward pass, matrix-based notation and activation functions etc.), learning (cost functions, gradient descent and backpropagation etc.), generalization (overfitting, regularization, initialization, hyper-parameters, vanishing gradients / unstable gradients and deep NNs etc.), CNNs and Image Classification (different layers, especially the conv layer, learning features and sharing parameters, AlexNet and later versions etc.), object detection and semantic segmentation (R-CNN, Fast R-CNN, Faster R-CNN, R-FCN, YOLO / YOLO v2, SSD and Mask R-CNN etc.).
Examples will be taken from key application domains like medicine, autonomy (drones, cars and ships) & robotics, industrial inspection etc.

Learning outcome

The course provides an overview and understanding of several fundamental techniques in Computer Vision. Advanced knowledge in this field is becoming increasingly more and more important. This is in particular true in view of the ever increasing availability of cameras and other imaging devices in nearly all areas of our society. The course helps to build the skills to design and construct advanced computer vision modules that function within a system to achieve the vision system's goals. Application fields include industrial areas, autonomy (drones, cars and ships), robotics and medical image analysis. The learned subjects can be the basis of employment in industry or the public sector, or could be followed for doctoral research in Norway or overseas.

Learning methods and activities

Lectures and exercises. Lectures will be given in English. An important part of the exercises is a project that address a real-world problem.

Further on evaluation

Due to the Covid-19 situation, the assessment form has been changed for spring 2021. See the course description in Norwegian for more information.

Course materials

Book: Digital Image Processing, Rafael C. Gonzalez, Richard E. Woods (Publisher: Pearson)
Book: Neural Networks and Deep Learning, Michael Nielsen (online)
Book: Deep Learning, Ian Goodfellow et. al. (online)

Credit reductions

Course code Reduction From To
SIF8066 7.5
More on the course

No

Facts

Version: 1
Credits:  7.5 SP
Study level: Second degree level

Coursework

Term no.: 1
Teaching semester:  SPRING 2021

No.of lecture hours: 2
Lab hours: 2
No.of specialization hours: 8

Language of instruction: English

Location: Trondheim

Subject area(s)
  • Informatics
  • Technological subjects
Contact information
Course coordinator:

Department with academic responsibility
Department of Computer Science

Phone:

Examination

  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

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