A scheme for applying emerging computer vision technologies to interactive e-learning
- Publication Type:
- Thesis
- Issue Date:
- 2005
Closed Access
Filename | Description | Size | |||
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01Front.pdf | contents and abstract | 732.76 kB | |||
02Whole.pdf | thesis | 138.65 MB |
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NO FULL TEXT AVAILABLE. Access is restricted indefinitely. ----- In traditional e-learning applications, instructional video capturing is used merely for
streaming purposes. The recent advancements in video signal processing, computer
vision and pattern recognition (CVPR) technologies have led to instructional video also
becoming the input for many automated and intelligent e-learning systems. At the same
time, the increases in consumer-grade PC processing speed and camera resolutions have
made CVPR technologies more accessible to ordinary computer users and have the
potential to apply to their everyday learning.
Noticing this trend, in our work, we have focused our attention on e-learning
systems that are synchronous, peer-to-peer based and instructor uses traditional
teaching equipment, where the properties include accurate bidirectional interactions,
low-cost hardware, system portability and vision software versatilities are emphasized
Our research also indicates that existing vision-based e-learning systems have not
focused on these areas and fulfilled the corresponding requirements adequately. Our
work, therefore, is an effort to address the challenges brought by these properties and to
apply our computer vision research findings to help resolve the challenges.
In this thesis, the work is presented through a scheme of attained results.
Categorically, these results are presented from the perspectives of interactive
multimedia systems, e-learning computer vision algorithms and processing
implementations.
From a multimedia system perspective, we have developed a few interactive
subsystems where our work has represented both novelty and successful attention to our
challenges. Our work includes a semi-passive control subsystem for a low-cost and low-precision
PTZ camera able to be operated according to both instructor-led laser pointer
guidance and by a student in a remote location. We also present a novel student-to-instructor
communication method using a low-cost laser pointer robot that can
automatically pan and tilt to a specified region.
We also present a real-time teaching object recognition subsystem, where both
hand-held and wall-based teaching objects can be detected during synchronous e-learning.
By using unique instructor's facial pose estimation and laser pointer guidance
techniques, this system has achieved robustness, efficiencies and unambiguous
detection.
From a computer vision algorithms perspective, we will present results in
researching and customizing various emerging CVPR algorithms to suit e-learning
applications. These results include robust object recognition using local invariant
features under reference scales; robust laser pointer detection by bimodal Gaussian
Mixture Model training with integral images features; Application of mean-shift
tracking to e-learning and safeguarded with efficient global target regaining. In these
algorithms, the efficiency for single PC execution is also emphasized.
From a processing implementation perspective, we will present results using a
latency-insensitive CVPR scheduling method by our unique execution parameter
generation functions for the expensive vision algorithms used. We also present a
method for peer-to-peer CVPR processing load-sharing on student's PC. SIMD
optimization results and studies into automatic SIMD compilation on vision algorithms
are also discussed.
Finally, the overall results obtained are presented though our prototype IVDA
instructional video streaming system, encapsulating most of our work. Apart from
presenting a state-of-art vision-based e-learning system, we will also illustrate how the
results obtained have addressed and resolved the challenges in our focused e-learning
areas.
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