COMP SCI 4022 - Computer Vision

North Terrace Campus - Semester 1 - 2017

Over the last 40 years, researchers in artificial intelligence have endeavoured to develop computers with the capacity to "see" the world around them. This course aims to convey the nature of some of the fundamental problems in vision, and to explain a variety of techniques used to overcome them. Vision is a rapidly evolving area of computer science, and new and emerging approaches to these problems are discussed along with more "classical" techniques. Various vision problems are considered, including: feature detection in images, e.g. edge detection, and the accumulation of edge data to form lines; recovery of 3D shape from images, e.g. the use of a stereo image pair to derive 3D surface information; forming image mosaics; video surveillance techniques, e.g. tracking objects in video; motion detection in video images, e.g. counting number of moving objects in a video; recognising and classifying objects in images, e.g. searching a video for a particular object. Several assignments will be given to enable the student to gain practical experience in tackling some of these problems.

  • General Course Information
    Course Details
    Course Code COMP SCI 4022
    Course Computer Vision
    Coordinating Unit School of Computer Science
    Term Semester 1
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 2 hours per week
    Available for Study Abroad and Exchange Y
    Course Description Over the last 40 years, researchers in artificial intelligence have endeavoured to develop computers with the capacity to "see" the world around them. This course aims to convey the nature of some of the fundamental problems in vision, and to explain a variety of techniques used to overcome them. Vision is a rapidly evolving area of computer science, and new and emerging approaches to these problems are discussed along with more "classical" techniques. Various vision problems are considered, including: feature detection in images, e.g. edge detection, and the accumulation of edge data to form lines; recovery of 3D shape from images, e.g. the use of a stereo image pair to derive 3D surface information; forming image mosaics; video surveillance techniques, e.g. tracking objects in video; motion detection in video images, e.g. counting number of moving objects in a video; recognising and classifying objects in images, e.g. searching a video for a particular object. Several assignments will be given to enable the student to gain practical experience in tackling some of these problems.
    Course Staff

    Course Coordinator: Professor Anton van den Hengel

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes

    No information currently available.

    University Graduate Attributes

    No information currently available.

  • Learning Resources
    Required Resources
    None
    Online Learning
    There are a wealth of resources online, but perhaps the most relevant are 


  • Learning & Teaching Activities
    Learning & Teaching Modes
    There will be one introductory (2 hour) lecture, and 3 additional lectures to cover the basic information required to understand the 3 practical excercises (one lecture each).  The content of the remaining remainder of the lectures will be determined by negotiation on the bulletin board associated with the course.

    The 3 assignments will take the form of conference-style papers.  A problem will be provided for each, and the assessment will reflect the degree to which the submitted paper demostrates an understanding of the problem and the relevant literature, the design of the solution, and the quality of the analysis applied to the proposed solution.
    Workload

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    Students are expected to attend 2 hours of lectures each week, and to participate in the forum.

    The assignments will require significant reading, in addition to coding and writing time.  This can be expected to consitute an additional 8 hours a week.
    Learning Activities Summary

    No information currently available.

  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    The assessment will be made up of 3 individual assignments only.  There will be no exam.
    Assessment Related Requirements
    Both the form and content of the assignments will be discussed in lectures.  Students are expected to attend and participate in this process.
    Assessment Detail

    No information currently available.

    Submission
    Performance will be assessed solely on the basis of the 3 papers to be submitted.  Latex for the papers, and all code will need to be submitted through the SVN, however, to enable authorship and originality to be determined.
    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

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