1. Interactive E-Learning System Using Pattern Recognition and Augmented Reality
Author : Sang Hwa Lee, Junyeong Choi, and Jong-Il Park
Consumer Electronics, IEEE Transactions on
Volume 55, Issue 2, May 2009 Page(s):
Speaker: Po-Kai Shen
Advisor: Tsai-Rong Chang
Student ID: M98G0214
Date: /4/13

2. Outline Introduction Overview of proposed e-learning system
Design of interactive makers
Recognition of image and object
Application to public education system
Conclusions

3. Introduction The students study their textbooks with auxiliary
audiovisual contents which are played on the
personal computer and specific terminals.
Goal is to design a mentoring system for self-
studying, which lets the students learn the
audio-visual contents interactively.
Design colorband and polka-dot patterns for
object-based user interaction.

4. Overview of proposed
e-learning system
Fig. 1.

5. Design of interactive makers
Polka-dot Pattern Recognition
Usually, the hexagonal array patterns are robust
for perspective distortion caused by different
camera viewpoints
Fig. 2.

6. Design of interactive makers
Polka-dot Pattern Recognition
High pass filter
The candidate position is first searched in the coarse grid
in the original image.
When a position satisfies the conditions of polka-dot
marker by (1) and (2)

7. Design of interactive makers
Fig. 3.
(a)
(b)

8. Design of interactive makers
Color-band Recognition
Fig. 4.
The blue color is usually best recognized and most stable in
the lighting variation.
The hue components in HSV color space are used for robust
detection in various lighting conditions.

9. Design of interactive makers
Color-band Recognition
Fig. 5.
Recognition results of color-band markers. Two markers areconsistently detected when they are moving.

10. Recognition of image and object
Feature Extraction
The distinct feature points are determined by Hessian matrix at image point x(x, y) and scale parameter σ

11. Recognition of image and object
Feature Extraction
Fig. 6.
26 neighbors to decide the feature points. The red pixel is the
current point at x and σ. The neighboring scale images are considered to decide the feature points.

12. Recognition of image and object
Feature Extraction
Haar filter
Fig. 7.

13. Recognition of image and object
Feature Extraction
Haar filter
Hessian filter +
SURF
(speeded up robust
Feature)
The square region is divided into 16 subregions, and 25 pixels
(5x5) are sampled in each subregion. And we calculate 4-D
vector for every subregion
4-D vector for each subregion, the descriptor vector to
describe the feature point becomes 64-D (4x16) vector. This
64-D descriptor vector is an ID number of each feature point.

14. Recognition of image and object
Feature Matching
Exploit the sign of Laplacian operation for fast feature matching =

15. Recognition of image and object
Feature Matching
Fig. 8.
(a)
(b)

16. Recognition of image and object
Image and Object Recognition
Fig. 9.
(a)
(b)
(c)
(d)

17. Recognition of image and object
Image and Object Recognition

18. Recognition of image and object
Image and Object Recognition
(a)
(b)
Fig. 10.

19. Recognition of image and object
Image and Object Recognition
(c)
(d)
Fig. 10.

20. Application to public
education system
Fig. 11.
(a)
(b)

21. Application to public
education system
Fig. 12.
(a)
(b)

22. Conclusions Expected that the proposed e-leaning
system becomes popular faster
Recognition errors are reduced and
authoring tools are provided to produce
the educational contents and scenarios

23. Thank you