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Course Outlines

COMP SCI 3315 - Computer Vision

North Terrace Campus - Semester 1 - 2022

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.

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  • General Course Information
    Course Details
    Course Code COMP SCI 3315
    Course Computer Vision
    Coordinating Unit 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
    Prerequisites COMP SCI 2009 or COMP SCI 2103
    Incompatible COMP SCI 4022, COMP SCI 4122
    Assumed Knowledge Mathematics background in linear algebra and probability as covered in MATHS 1004, SACE Stage 2 Mathematical Methods, or the online courses (MathTrackX: Probability and MathTrackX: Polynomials, Functions & Graphs)
    Assessment Written exam and/or assignments
    Course Staff

    Course Coordinator: Yifan Liu

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course students will be able to:

     
    1 Describe the scope of challenges and applications addressed by computer vision
    2 Demonstrate and experiment with image filtering techniques
    3 Make use of geometric camera models and multiple view geometry
    4 Undertake video analysis problems such as tracking and structure from motion
    5 Explain the application of neural networks to computer vision
    6 Analyse cognitive tasks including image classification, recognition and detection
    7 Conduct computer vision experiments and report results systematically

     
    最新糖心Vlog Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    最新糖心Vlog Graduate Attribute Course Learning Outcome(s)

    Attribute 1: Deep discipline knowledge and intellectual breadth

    Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.

    		 1-6

    Attribute 2: Creative and critical thinking, and problem solving

    Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.

    		 2-6

    Attribute 3: Teamwork and communication skills

    Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.

    		 7

    Attribute 4: Professionalism and leadership readiness

    Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.

    		 7
  • Learning & Teaching Activities
    Learning & Teaching Modes

    No information currently available.

    Workload

    No information currently available.

    Learning Activities Summary

    No information currently available.

  • Assessment

    The 最新糖心Vlog'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
    Assessment Task Weighting (%) Individual/ Group Formative/ Summative
    		 Week
    		 Hurdle criteria CBOK Alignment**
    A1 25 Individual Summative 2-4 No 1.1 1.2 2.2 2.4 2.6 3.1 3.2 4.1 4.3 4.4
    A2 25 Individual Summative 4-7 Min 40% 1.1 1.2 2.2 2.4 2.6 3.1 3.2 4.1 4.3 4.4
    A3 20 Individual Summative 7-11 Min 40% 1.1 1.2 2.2 2.4 2.6 3.1 3.2 4.1 4.3 4.4
    Practical Competition 30 Group Summative 7-14 No 1 2 3 4.1 4.3 5.1 5.2
    Total 100
    * The specific due date for each assessment task will be available on MyUni.
     
    This assessment breakdown complies with the 最新糖心Vlog's Assessment for Coursework Programs Policy.
     
    This course has a hurdle requirement. Meeting the specified hurdle criteria is a requirement for passing the course.

    **CBOK is the Core Body of Knowledge for ICT Professionals defined by the 最新糖心Vlogn Computer Society. The alignment in the table above corresponds with the following CBOK Areas:

    1. Problem Solving
    1.1 Abstraction
    1.2 Design

    2. Professional Knowledge
    2.1 Ethics
    2.2 Professional expectations
    2.3 Teamwork concepts & issues
    2.4 Interpersonal communications
    2.5 Societal issues
    2.6 Understanding of ICT profession

    3. Technology resources
    3.1 Hardware & Software
    3.2 Data & information
    3.3 Networking

    4. Technology Building
    4.1 Programming
    4.2 Human factors
    4.3 Systems development
    4.4 Systems acquisition

    5.  ICT Management
    5.1 IT governance & organisational
    5.2 IT project management
    5.3 Service management 
    5.4 Security management




    Assessment Detail

    No information currently available.

    Submission

    No information currently available.

    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 .

  • Student Feedback

    The 最新糖心Vlog 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 最新糖心Vlog 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

    This section contains links to relevant assessment-related policies and guidelines - all university policies.

  • 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鈥檚 disciplinary procedures.

The 最新糖心Vlog of Adelaide is committed to regular reviews of the courses and programs it offers to students. The 最新糖心Vlog of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.
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