Matrix 2 d
Upcoming SlideShare
Loading in...5
×
 

Like this? Share it with your network

Share

Matrix 2 d

on

  • 289 views

 

Statistics

Views

Total Views
289
Views on SlideShare
289
Embed Views
0

Actions

Likes
0
Downloads
0
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Matrix 2 d Presentation Transcript

  • 1. 2D Transformations with Matrices
  • 2. Matrices  a1,1  A = a2,1  a3,1  a1, 2 a2 , 2 a3, 2 a1,3   a2 , 3  a3,3   • A matrix is a rectangular array of numbers. • A general matrix will be represented by an upper-case italicised letter. • The element on the ith row and jth column is denoted by ai,j. Note that we start indexing at 1, whereas C indexes arrays from 0.
  • 3. Matrices – Addition • Given two matrices A and B if we want to add B to A (that is form A+B) then if A is (n×m), B must be (n×m), Otherwise, A+B is not defined. • The addition produces a result, C = A+B, with elements: Ci , j = Ai , j + Bi , j 1 2 5 6 1 + 5 2 + 6  6 8  3 4 + 7 8 = 3 + 7 4 + 8 = 10 12        
  • 4. Matrices – Multiplication • Given two matrices A and B if we want to multiply B by A (that is form AB) then if A is (n×m), B must be (m×p), i.e., the number of columns in A must be equal to the number of rows in B. Otherwise, AB is not defined. • The multiplication produces a result, C = AB, with elements: m (Basically we multiply Ci , j = ∑ aik bkj of A with the first column of the first row k =1 B and put this in the c1,1 element of C. And so on…).
  • 5. Matrices – Multiplication (Examples) 2×6+ 6×3+ 7×2=44 2 6 7 6 8 44 76 4 5 8 × 3 3 = 55 95       9 2 3 2 6 66 96       6 8  2 6  ×  3 3  4 5     2 6   Undefined! 2x2 x 3x2 2!=3 2x2 x 2x4 x 4x4 is allowed. Result is 2x4 matrix
  • 6. Matrices -- Basics • Unlike scalar multiplication, AB ≠ BA • Matrix multiplication distributes over addition: A(B+C) = AB + AC • Identity matrix for multiplication is defined as I. • The transpose of a matrix, A, is either denoted AT or A’ is obtained by swapping the rows and columns of A:  a1,1 A= a2,1 a1, 2 a2 , 2  a1,1 a1,3   ⇒ A' = a1, 2 a2 , 3    a1,3  a2,1   a2 , 2  a2 , 3  
  • 7. 2D Geometrical Transformations Translate Rotate Shear Scale
  • 8. Translate Points Recall.. We can translate points in the (x, y) plane to new positions by adding translation amounts to the coordinates of the points. For each point P(x, y) to be moved by dx units parallel to the x axis and by dy units parallel to the y axis, to the new point P’(x’, y’ ). The translation has the following form: x' = x + d x P’(x’,y’) dy dx P(x,y) y' = y + d y In matrix format:  x '   x  d x   y ' =  y  + d       y d x  If we define the translation matrix T =   , then we have P’ =P + T. d y 
  • 9. Scale Points Points can be scaled (stretched) by sx along the x axis and by sy along the y axis into the new points by the multiplications: We can specify how much bigger or smaller by means of a “scale factor” To double the size of an object we use a scale factor of 2, to half the size of an obejct we use a scale factor of 0.5 x' = s x x P(x,y) y s y• y P’(x’,y’) s x• x s x If we define S =  0 x y' = s y y  x'  s x  y ' =  0    0 , then we have P’ =SP sy   0 x  sy  y  
  • 10. Rotate Points (cont.) Points can be rotated through an angle θ about the origin: | OP' |=| OP |= l x' =| OP' | cos(α + θ ) = l cos(α + θ ) = l cos α cos θ − l sin α sin θ = x cos θ − y sin θ y ' =| OP' | sin(α + θ ) = l sin(α + θ ) = l cos α sin θ + l sin α cos θ = x sin θ + y cos θ P’(x’,y’) y y’ O θ P(x,y) l α x’  x'  cos θ  y ' =  sin θ    x − sin θ   x  cos θ   y    P’ =RP
  • 11. Review… • Translate: • Scale: • Rotate: P’ = P+T P’ = SP P’ = RP • Spot the odd one out… – Multiplying versus adding matrix… – Ideally, all transformations would be the same.. • easier to code • Solution: Homogeneous Coordinates
  • 12. Homogeneous Coordinates For a given 2D coordinates (x, y), we introduce a third dimension: [x, y, 1] In general, a homogeneous coordinates for a 2D point has the form: [x, y, W] Two homogeneous coordinates [x, y, W] and [x’, y’, W’] are said to be of the same (or equivalent) if x = kx’ y = ky’ W = kW’ for some k ≠ 0 eg: [2, 3, 6] = [4, 6, 12] where k=2 Therefore any [x, y, W] can be normalised by dividing each element by W: [x/W, y/W, 1]
  • 13. Homogeneous Transformations Now, redefine the translation by using homogeneous coordinates: x '  1  y ' = 0    1  0    x '  x  d x   y ' =  y  + d       y 0 1 0 d x x  d y  y    1 1    P' = T × P Similarly, we have: Scaling x'  s x  y ' =  0    1   0    P’ = Rotation 0 0 x  0  y    11    S × 0 sy P x'  cos θ  y ' = sin θ    1   0    P’ = − sin θ cos θ 0 x  0  y    11    0 R × P
  • 14. Composition of 2D Transformations 1. Additivity of successive translations We want to translate a point P to P’ by T(dx1, dy1) and then to P’’ by another T(dx2, dy2) P ' ' = T (d x 2 , d y 2 ) P ' = T (d x 2 , d y 2 )[T (d x1 , d y1 ) P ] On the other hand, we can define T21= T(dx1, dy1) T(dx2, dy2) first, then apply T21 to P: P ' ' = T21 P where T21 = T ( d x 2 , d y 2 )T (d x1 , d y1 ) 1 0 d x 2  1 0 d x1  =  0 1 d y 2   0 1 d y1     0 0 1  0 0 1     1 0 d x1 + d x 2  =  0 1 d y1 + d y 2    0 0  1  
  • 15. Examples of Composite 2D Transformations T(-1,2) (1,3) T(1,-1) (2,1) 1 T21 = 0  0  1 = 0  0  0 1 0 0 1 0 1  1 0 − 1 − 1 0 1 2    1  0 0 1    0 1  1  (2,2)
  • 16. Composition of 2D Transformations (cont.) 2. Multiplicativity of successive scalings P ' ' = S ( s x 2 , s y 2 )[ S ( s x1 , s y1 ) P] = [ S ( s x 2 , s y 2 ) S ( s x1 , s y1 )]P = S 21 P where S 21 = S ( s x 2 , s y 2 ) S ( s x1 , s y1 ) s x 2 = 0  0  0 sy2 0 s x 2 * s x1 = 0   0  0s x1 0  0  1  0  0 s y 2 * s y1 0 0 s y1 0 0 0  1  0 0  1 
  • 17. Composition of 2D Transformations (cont.) 3. Additivity of successive rotations P ' ' = R (θ 2 )[ R (θ1 ) P ] = [ R (θ 2 ) R (θ1 )]P = R21 P where R21 = R (θ2 ) R (θ1 ) cos θ2 = sin θ2   0  − sin θ2 cos θ2 0 cos(θ2 +θ1 ) = sin(θ2 +θ1 )   0  0cos θ1 0sin θ1  1  0  − sin(θ2 +θ1 ) cos(θ2 +θ1 ) 0 − sin θ1 cos θ1 0 0 0  1  0 0  1 
  • 18. Composition of 2D Transformations (cont.) 4. Different types of elementary transformations discussed above can be concatenated as well. P ' = R (θ )[T (d x , d y ) P ] = [ R (θ )T (d x , d y )]P = MP where M = R (θ )T ( d x , d y )
  • 19. Consider the following two questions: 1) translate a line segment P1 P2, say, by -1 units in the x direction and -2 units in the y direction. P2(3,3) P’2 P1(1,2) P’1 2). Rotate a line segment P1 P2, say by θ degrees counter clockwise, about P1. P’2 P2(3,3) P1(1,2) θ P’1
  • 20. Other Than Point Transformations… Translate Lines: translate both endpoints, then join them. Scale or Rotate Lines: More complex. For example, consider to rotate an arbitrary line about a point P1, three steps are needed: 1). Translate such that P1 is at the origin; 2). Rotate; 3). Translate such that the point at the origin returns to P1. P2 P2(3,3) T(-1,-2) P2(2,1) P1(1,2) P1 P2 R(θ) θ P1 T(1,2) P1
  • 21. Another Example. Translate Scale Translate Rotate
  • 22. Order Matters! As we said, the order for composition of 2D geometrical transformations matters, because, in general, matrix multiplication is not commutative. However, it is easy to show that, in the following four cases, commutativity holds: 1). Translation + Translation 2). Scaling + Scaling 3). Rotation + Rotation 4). Scaling (with sx = sy) + Rotation just to verify case 4: M 1 = S ( s x , s y ) R (θ ) M 2 = R (θ ) S ( s x , s y ) 0cos θ − sin θ s y 0sin θ cos θ  0 1  0 0  s x * cos θ − s x * sin θ 0 =  s y * sin θ s y * cos θ 0    0 0 1   s x = 0  0  0 0 0  1  if sx = sy, M1 = M2. cos θ = sin θ   0  − sin θ 0s x cos θ 0 0  0 1  0  s x * cos θ − s y * sin θ =  s x * sin θ s y * cos θ   0 0  0 sy 0 0 0  1  0 0  1 
  • 23. Rigid-Body vs. Affine Transformations A transformation matrix of the form r11 r  21 0  r12 r22 0 tx  ty   1  where the upper 2×2 sub-matrix is orthogonal, preserves angles and lengths. Such transforms are called rigid-body transformations, because the body or object being transformed is not distorted in any way. An arbitrary sequence of rotation and translation matrices creates a matrix of this form. The product of an arbitrary sequence of rotation, translations, and scale matrices will cause an affine transformation, which have the property of preserving parallelism of lines, but not of lengths and angles.
  • 24. Rigid-Body vs. Affine Transformations (cont.) Rigid- body Transformation Affine Transformation 45º Unit cube Scale in x, not in y Shear transformation is also affine. Shear in the x direction 1 SH x = 0  0  a 1 0 0 0  1  1 SH y = b  0  Shear in the y direction 0 1 0 0 0  1 