Image Analysis |

Introduction to Image Analysis and Machine Learning, BSc and MSc (Spring 2018)

Sun, 28 Jan 2018 00:00:00 +0000

Description

The course is an introductory course to the basics of computer vision and machine learning. The objectives of this course are to provide students with the fundamental knowledge and skills required to design, build, and evolve smaller computer vision (CV) and machine learning programs.

The course introduces specific techniques from 2D and 3D image analysis and recent techniques from machine learning (e.g. deep learning / neural nets, SVM) to solve computer vision problems. The course provides an introduction to fundamentals of image formation, camera imaging geometry, point processing, color spaces, feature detection and matching, multiview geometry, motion estimation /tracking, and object detection/ recognition.

Computer Vision is the study of enabling machines to see and interpret the visual world through images and videos. Computer vision/image analysis and machine learning have in recent years played decisive roles in the development of new innovative applications based on images (e.g. various services provided by Google, Facebook, Microsoft, Snapchat etc). Having knowledge within computer vision and machine learning (e.g. deep learning) is an important skill needed in many modern innovative businesses and is likely to become even more important in the near future.

Many successful and robust computer vision techniques (such as object recognition, tracking) heavily rely on concepts from signal analysis and machine learning. The course will introduce specific machine learning techniques and apply them to relevant computer vision problems such as recognition, matching and search.

Through the course the student should be able to use the technique in their own applications and within more advanced topics on computer vision, data science and pervasive computing.

The course gives an introduction to computer vision, image analysis, linear algebra and machine learning. In the course we will present the fundamental models used for CV and machine learning as well as techniques to implement them. You will in the exercises and mandatory assignments be getting hands-on experience with the techniques described during the lectures. In the exercises you will be using Python and image analysis / machine learning packages such as OpenCV, Tensorflow/ Keras, Numpy. In the exercises we will use images from digital cameras and web cameras to illustrate the theory. Cameras needed for the exercises and assignments will be available to the students during the course.

In particular the course covers:

Pixel-based and local processing of images (smoothing, edges, conversion between color spaces) and color image processing.
Regression, classification, recognition
Machine learning techniques such as deep learning, neural networks, support vector machines applied to computer vision problems
Geometric transformations (2D and 3D) and linear algebra
Cameras, Stereo, structured light (Kinnect).
Python, OpenCV, Numpy,Tensorflow/Keras

After completing this course, the students should be able to:

Define, describe and relate concepts and mechanisms underpinning computer vision (CV) and machine learning (ML) methods and how they are related.
Analyze and explain key aspects of building medium-sized computer vision applications.
Explain, design and implement medium-sized interactive computer vision applications using concepts from ML.
Evaluate, select and adapt appropriate computer vision and machine learning techniques by applying the theoretical concepts and practical techniques from the course.
Clearly explain and employ basic linear algebra for computer vision and machine learning.
Apply the theory and implement rudimentary research papers within CV and ML as expected on a bachelor level.
Apply the theory and implement rudimentary research papers within CV and ML as expected on a master level.

Staff

Course Manager and Teacher:
Teacher:
Teaching Assistant:

Supporting Materials

Introduction to Image Analysis and Machine Learning, BSc and MSc (Spring 2017)

Wed, 01 Feb 2017 00:00:00 +0000

Description

To this end the course introduces specific techniques from 2D and 3D image analysis and recent techniques from machine learning to solve computer vision problems. The course provides an introduction to fundamentals of image formation, camera imaging geometry, point processing, color spaces, feature detection and matching, multiview geometry, motion estimation /tracking, and object detection/ recognition. Many successful and robust computer vision techniques (such as object recognition, tracking) heavily rely on concepts from signal analysis and machine learning. The course will introduce specific machine learning techniques and apply them to relevant computer vision problems such as recognition, matching and search.

Through the course the student should be able to use the technique in their own applications and within more advanced topics on computer vision, data science and pervasive computing.

The course gives an introduction to computer vision, image analysis, linear algebra and programming. In the course we will present the fundamental models used for CV and machine learning as well as techniques to implement them. You will in the exercises and mandatory assignments be getting hands-on experience with the techniques described during the lectures. In the exercises we will use images from digital cameras and web cameras to illustrate the theory. Cameras will be available during the course.

In particular the course covers:

Pixel-based and local processing of images (smoothing, edges, conversion between color spaces) and color image processing.
Segmentation
Object recognition.
Geometric transformations (2D and 3D)
Cameras, Stereo, structured light (Kinnect).
Machine learning techniques applied to computer vision problems such as regression, classification techniques and will touch upon deep learning.

After completing this course, the students should be able to:

Define, describe and relate concepts and mechanisms underpinning computer vision (CV) and machine learning (ML) methods and how they are related.
Analyze and explain key aspects of building medium-sized computer vision applications.
Explain, design and implement medium-sized interactive computer vision applications using concepts from ML.
Evaluate, select and adapt appropriate computer vision and machine learning techniques by applying the theoretical concepts and practical techniques from the course.
Clearly explain and employ basic linear algebra for computer vision and machine learning.
Apply the theory and implement rudimentary research papers within CV and ML as expected on a bachelor level.
Apply the theory and implement rudimentary research papers within CV and ML as expected on a master level.

Staff

Course Manager and Teacher:
Teaching Assistant:
Teaching Assistant:

Supporting Materials

Introduction to Graphics and Image Analysis (Spring 2016)

Wed, 03 Feb 2016 00:00:00 +0000

Description

The objectives of this course are to provide students with the fundamental knowledge, comprehension, and skills required to design, build, and evolve smaller computer vision (CV) and computer graphics applications (CG).

Computer vision (image analysis) and computer graphics play decisive roles in our society in relation to automated processes in industry and in our daily lives. The dramatic increase of cameras in mobile devices and other consumer products (QRCodes, Kinnect and many others) makes it evident that developing applications based on efficient and accurate techniques are needed to keep up with the large amounts of data produced by cameras.

2D and 3D compute graphics (CG) on the other hand has been an integral part of our daily interaction with computers and (obviously) has a huge application domain (games, displays etc.), but has also lead to the developments of GPU’s. While seemingly different, computer vision/image analysis and computer graphics have quite a lot in common. The basic commonalities and difference between CV and CG will be covered in the course.

The objectives of this course are to provide students with the fundamental knowledge, comprehension, and skills required to design and build smaller computer vision and computer graphics applications on e.g. a PC or a mobile phone.

Through the course the student should be able to use the technique in more advanced topics on game engines, graphics, computer vision and pervasive computing. The course is an introductory course to the basics of computer vision and computer graphics and the intention is that the student will have sufficient knowledge to follow more advanced courses on game engines, graphics, computer vision and object recognition.

The course gives an introduction to computer graphics, computer vision/image analysis, linear algebra and GPU programming. In the course we will present the fundamental models used for CV and CG as well as techniques to implement them. You will in the exercises and mandatory assignments be getting hands-on experience with the techniques described during the lectures. In the exercises we will use images from digital cameras and web cameras to illustrate the theory. Web cameras can be borrowed.

In particular we will describe:

Pixel-based and local processing of images (smoothing, edges, conversion between color spaces) and color image processing.
Segmentation and object recognition and a brief introduction to machine learning.
Geometric transformations (2D and 3D).
Cameras, Stereo, structured light (Kinnect).
Texture-mapping, shadows, hidden surface removal and lighting.
Basics of GPU programming.

Python will the main platform for the course yet students may chose to use C#.

Staff

Course Manager and Teacher:
Teacher:
Teaching Assistant:
Teaching Assistant:

Supporting Materials

Introduction to Graphics and Image Analysis (Spring 2015)

Wed, 28 Jan 2015 00:00:00 +0000

Description

In particular we will describe:

Pixel-based and local processing of images (smoothing, edges, conversion between color spaces) and color image processing.
Segmentation and object recognition and a brief introduction to machine learning.
Geometric transformations (2D and 3D).
Cameras, Stereo, structured light (Kinnect).
Texture-mapping, shadows, hidden surface removal and lighting.
Basics of GPU programming.

Python will the main platform for the course yet students may chose to use C#.

Staff

Course Manager and Teacher:
Teaching Assistant:

Image Analysis |

Introduction to Image Analysis and Machine Learning, BSc and MSc (Spring 2018)

Description

Contents

Staff

Supporting Materials

Introduction to Image Analysis and Machine Learning, BSc and MSc (Spring 2017)

Description

Contents

Staff

Supporting Materials

Introduction to Graphics and Image Analysis (Spring 2016)

Description

Contents

Staff

Supporting Materials

Introduction to Graphics and Image Analysis (Spring 2015)

Description

Contents

Staff

Supporting Materials