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Vision
Guided Control (Early applied vision)
COMPSCI
773 S1 T
Introduction
This course introduces computational
methods and techniques used in popular vision-based research areas such as
2/3D face recognition, 3D scene reconstruction, etc... Many topics are only
overviewed, but a number of interesting theoretical and practical problems
are analysed in detail. While most seemingly simple automatic real-world
actions present a real challenge you will be able to acquire knowledge
currently used in the latest technological advances available. This course is
a must for students eager to pursue post-graduate studies and/or a career in
applied computer vision.
Design of modern control
systems involves different mathematical tools, especially, optimization
techniques, matrix analysis, and analytic 2D/3D geometry. Some tools will be
explained in brief in the lectures. Still, you are expected to learn these
methods in details and use them to complete assignments.
Programming will be undertaken in C, C++, C#. You
are expected to be pro-efficient in at least one of the above mentioned
programming language or show a strong willingness to learn. Java-only
students with a strong motivation should be able to progress enough in any of
these languages within the first few weeks of the course.
Assessment
Assessment is based on 60%
course work (30% group work, 30% individual work) and 40% open-book final
examination. Course work includes one-to-one oral test and assignments that
exploit the hardware (digital cameras and PCs) available in our research labs
at Tamaki (room 731.234). For each assignment, each group will have to write
a report which should be organised as follows:
- Each member of the group works on a distinct
part of the assignment and writes an individual report
- Each group provides a group report presenting
the group solution and achievements for each assignment. Basically the
group report should consist of an introduction of the problem and
different solutions proposed.
- Both the individual and group reports should
show students' abilities to:
- analyse a problem
- propose feasible solutions based on materials
taught during the lectures or learnt while reading research papers
- use statistical tools to assess their
experimental results
Course work
A particular feature of the course work is
the emphasis on complete system design. Therefore, instead of picking a small
part of the material covered in lectures as assignment tasks, the project in
this paper has the aim of developing a complete system to perform a specified
task. The individual assignments present intermediate steps toward achieving
this goal. At the end of the paper, there will be a competition to evaluate
your project.
The equipment in the CITR Active Vision
Lab consists of a number of PCs running Windows and a few web-cameras to be
used to perform HCI applications (face recognition, dynamic 3D face
animation, face expression recognition). We
may also use our 3D scanner and stereo-vision systems for 3D face
acquisition.
Nowadays, Human - Computer Interaction is
a hot research topic. It consists mainly on extracting information (from
audio-visual speech, visual expression, hand signs, body expression) to
interact efficiently with a robot or a machine via a computer. Potential
applications range from automatic speech recognition (ASR), videoconference,
virtual reality, communication for disabled people, user verification and
recognition (audiovisual biometrics features), to remote control of robots,
vehicles, and devices.
This year course projects will encompass
topics such as stereo vision, 3D positioning, feature extraction and
classification with a focus on real-time processes for efficient interaction.
Basically, you will have to:
- Design an interface to acquire synchronized
images and videos from 2 USB cameras.
- Find a limited set of face features using
stereo vision and potentially markers on the face.
- Your task will be to track your face (and a
subset of face markers) movement.
- Authenticate faces in the labs
- Extract faces from images and identify them
using advanced statistical analysis techniques such as PCA, LDA.
- Fuse stereo-vision face data (depth map) and
readily available face texture for 2+3D face recognition
The work is subdivided into three
assignments covering the following parts of the project:
- Calibration of stereo cameras for computing 3D positions of a desired item
in the cameras field-of-view by intersecting optical rays and visualising
3D movements of the item (you will use in this assignment the existing
Tsai calibration software (or any suitable calibration method you might
have researched). You will also do several programming tasks in camera
control, vdeo-stream synchronization, video display, etc...).
- You will use statistical analysis techniques
to recognise face. You will track and display face features using set
markers.
- Face authentication for faces localisation, face mask extraction,
and face recognition at real-time.
- 3D face authentication (fusing depth map
and face texture images)
- Whole system testing. You will integrate your previous work to:
- Effectively track faces and face features
from synchronised video-images.
- Effectively
recognise faces in 2D and 3D.
The schedule of these assignments is as
follows:
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Theme
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Due date
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Assignment 1
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Camera calibration, USB
camera image/video acquisition, and 3D GUI
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March 21 (preamble) and April 11 (full
assignment), 2008
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Assignment 2
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3D
face features tracking, 3D face animation, 2D face and face expression
recognition
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16 May 2008
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Assignment 3
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Whole System Testing
(live demo)
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Last day of lectures of the semester
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Basic Topics of the Course
Notice that there are no lectures during the Graduation Week,
May 5-9, 2008
- Camera calibration and projective geometry (for handouts: go to
"Lectures")
- 2D and 3D vision geometry
- Single camera calibration
- Stereo calibration: epipolar geometry,
triangulation
- Low-level Image processing (for handouts: go to
"Lectures")
- Colour detection and discrimination
- Binary image segmentation
- Image matching
- Video sequence processing
- Object tracking and image filtering
- Real-time image processing
- Face expression and face recognition
- Feature extraction: face
- Feature classification (PCA, LDA)
- Stereo-vision
- Epipolar geometry, rectification
- Stereo matching
- Elements of real-time control
773 Groups
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Groups
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1
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2
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3
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4
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Students Name/
UPI
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TBA
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TBA
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TBA
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TBA
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February 2005
© Department of Computer Science, The
University of Auckland
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