6. ABSTRACT
As the techniques of creating augmented reality become more advanced, there is
an increase in its use to create engaging digital teaching materials or a more efficient
e-Learning environment. Recently, most relevant applications are limited to
marker-based augmented reality techniques. However, markerless augmented reality
techniques are more flexible and not limited by the use of markers, providing a
broader range of applications. Visual tracking techniques is a critical core technique
of augmented reality, and its use is often affected by the four factors of
environmental lighting, image angle recognition, image resolution and image texture
recognition. On the other hand, in a “markerless augmented reality e-Learning
system”, the complexity and moving speed of the tracked object will also strongly
affect tracking recognition. Only with high quality object identification and tracking
abilities will the learners recognize objects and move them as they wish when
operating the system, making it more applicable for its purpose.
The purpose of this thesis is to provide recommendations on the use of
augmented reality techniques to create digital learning material and e-Learning
environments, and to analyze limiting factors in tracking techniques currently
employed in “markerless augmented reality e-Learning systems”. We also
recommend apply object tracking techniques for real-time tracking in which object
speed has reduced effects on tracking ability. The methods that this study suggests
will be able to raise the practicality and popularization of markerless augmented
reality systems on a technical basis. On the basis of application, the methods will
also allow people to understand the relevant issues in the creation of augmented
reality digital learning materials and e-Learning environments. For example, when
applied to the military equipment maintenance in the army of Taiwan, such digital
learning schemes can effectively reduce personnel training time and costs, helping to
vi
7. improve maintenance quality and hence the performance of military equipments.
When applied to training schemes of military tactics, the contents can be made
dynamic to increase learners’ motivation and interest.
Keywords: Marker-based Augmented Reality, Markerless Augmented Reality,
e-Learning, Virtuality Environment, Feature Tracking。
vii
51. 1 0 0 0
R −Rt
q ≅ K �0 1 0 0� � T � ������ = K[R |−Rt]������ = M������
0 1
0 0 1 0 (2.20)
公式(2.20)中 K 為相機校正矩陣公式如(2.21),當中 Cx 與 Cy 分別代表 x 與
y 上的偏移量, 而 fx 與 fy 分別代表在真實的感光耦合元件(Charge Coupled
Devices, CCD)相機中,在物理意義上 X 方向與 Y 方向之量值不一定為 1 比 1,
而影像上的像素並非 1 比 1,則必須在 X 與 Y 方向各導入一個比例參數,故
fx 與 fy 分別代表比例參數的座標點;另在感光耦合元件相機中,物理意義上的
像素也不一定是矩形,故 s 則代表 X 軸與 Y 軸的歪斜參數。
fx s cx
K = �0 fy cy �
0 0 1 (2.21)
R −Rt
� ������ 代表相機座標系統( Q c ),展開後公式(2.22),其中
0T 1
������為世界座標系統三維座標點,t 為位移轉換代表攝影機在世界座標系統位置,
公式(2.20)中�
R 為旋轉矩陣代表攝影機之方向。
R1 R2 R3 t x Xm
R6 t y Ym
�R 4 R5 �� �
R7 R8 R9 t z Zm
0 0 0 1 1 (2.22)
38
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