Project course in Machine Vision, 7.5 Credits

Contents

The course is about an application within one or more of the subjects Image Analysis, 3d Reconstruction, and Pattern Recognition. The relevant topics and theory about a project management model, e.g. Scrum, are introduced initially, followed by a larger software development project. The design of the project varies from year to year. Examples of possible projects are:

• Design an application that takes a set of images of a living room, builds a 3D model of it and visualizes it together with the IKEA bookcase "Billy" (or another piece of furniture).
• Design an application that given a number of scanned itemized phone bills calculates the phone subscription plan that would be the cheapest.
• Design an application that given a point cloud acquired by a laser scanner in a sparse forest, identifies trees and calculates their diameter at breast height.
• Develop a method that given inputs such as digital images and/or 3D point clouds from a forest, detects trees and classifies tree species.
• Identify and localize fruits in images using classifier fusion.
• Analyze 3D images from a Kinect-camera and detect people, trees, shrubs, and stones.

Expected learning outcomes

Knowledge and understanding
After having completed the course the student will be able to:

• explain central concepts within image analysis, 3D reconstruction, and/or pattern recognition (FSR 1),
• account for the priciples of the used project management model, e.g. Scrum (FSR 2), and

Skills and abilities
After having completed the course the student will be able to:

• demonstrate an ability to work in projects in groups of at least 4 people, including working in non-self-selected groups (FSR 3),
• analyze a problem within one or more of the subjects image analysis, 3D reconstruction, and pattern recognition (FSR 4),
• identify ambiguities in the given problem specification and to propose clarifications (FSR 5), and
• use a version control system for source code and other documentation (FSR 6).

Values and attitudes
After having completed the course the student will be able to:

• evaluate different proposed solutions for problems in one or more of the subjects image analysis, 3D reconstruction, and pattern recognition (FSR 7),
• reflect on their own effort in a project and assess the quality of the result of the group (FSR 8).

Required Knowledge

Univ: To be admitted you must have (or equivalent) 90 ECTS-credits including 60 ECTS-credits in Computing Science or 2 years of completed studies within a study programme (120 ECTS-credits). In both cases, including Statistics for Computer Scientists, 5MS005 and either Matrix Computations and Applications, 5DA003 or the courses Fundamentals of Artificial Intelligence, 5DV121, Linear Algebra 5MA019, Single variable Analysis 5MA009 and at least 7.5 ECTS-credits in Programming Methodology (e.g. 5DV157, 5DV158, 5DV176 or 5DV177).

Proficiency in English equivalent to Swedish upper Secondary course English A/5. Where the language of instruction is Swedish, applicants must prove proficiency in Swedish to the level required for basic eligibility for higher studies.

Form of instruction

The course begins with an introduction in the form of lectures to the relevant topics and the project managament model. Then follows a larger programming project that is central to the course. The aim is to get experience of working in a development team to generate a working prototype from a vague problem specification. A further goal is to individually and in groups gather knowledge necessary for the task. The work is primarily organized under an agile development model, such as Scrum. The work includes work in small and large groups and in-depth studies.

Examination modes

The examination on the course consists of a written account of the student's effort in the project, mainly as a time log (FSR 3, 8), and a written final report in the form of a home exam (FSR 1-2, 4-8). Since the practical work in a group is central to the course, the majority of the attendance during the practical work is mandatory.

On the course, the grades given are Fail (U), Pass (3) or Pass with Mark (4), or Pass with Distinction (5).

The course grade is an overall assessment of the two examinations parts and is set when both reports have been inspected. Individual students who do not pass at the end but who regularly participated in the project can get an extra task. In this case, the responsible teacher are allowed to limit the maximum grade to Pass (3). Students that have not passed at the end of the the course, optionally including time for the extra task, are given the grade Fail (U).

Limitation in the number of examinations:
Participants that do not pass the course are referred to the next instance of the course.

A student who has passed an examination may not be re-examined in order to get a higher grade. A student who has taken two tests for a course or part of a course, without passing, has the right to have another examiner appointed, unless there exist special reasons (Higher Education Ordinance Chapter 6, section 22). Request for a new examiner is made to the head of the Department of Computing Science.

Examination based on this syllabus is guaranteed for two years after the first registration on the course. This applies even if the course is closed down and this syllabus has ceased to be valid.

TRANSFER OF CREDITS
Students have the right to be tried if prior education or equivalent knowledge and skills acquired in the profession can be credited for the same education at Umeå University. Application for transfer of credits is submitted to the Student Services / Degree. For more information on credit transfer available at Umeå University's student web, www.student.umu.se, and the Higher Education Ordinance (Chapter 6). A refusal of crediting can be appealed (Higher Education chapter 12) to the University Appeals Board. This applies to the whole as part of the application for credit transfer is rejected.

Other regulations

In an exam, this course may not be included, in whole or in part, simultaneously with another course of similar content. If
in doubt, consult the student counselors at the Department of Computer Science and / or program director of the study
program.