©College of Computer and Information Science, Northeastern UniversityJune 26, 20151 CS U540 Computer Graphics Prof. Harriet Fell Spring 2009 Lecture 11.

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Presentation transcript:

©College of Computer and Information Science, Northeastern UniversityJune 26, CS U540 Computer Graphics Prof. Harriet Fell Spring 2009 Lecture 11 – January 28, 2009

©College of Computer and Information Science, Northeastern UniversityJune 26, Today’s Topics Linear Algebra Review  Matrices  Transformations New Linear Algebra  Homogeneous Coordinates

©College of Computer and Information Science, Northeastern UniversityJune 26, Matrices We use 2x2, 3x3, and 4x4 matrices in computer graphics. We’ll start with a review of 2D matrices and transformations.

©College of Computer and Information Science, Northeastern UniversityJune 26, Basic 2D Linear Transforms

©College of Computer and Information Science, Northeastern UniversityJune 26, Scale by.5 (1, 0) (0.5, 0) (0, 1) (0, 0.5)

©College of Computer and Information Science, Northeastern UniversityJune 26, Scaling by.5 y x y x

©College of Computer and Information Science, Northeastern UniversityJune 26, General Scaling y x y x

©College of Computer and Information Science, Northeastern UniversityJune 26, General Scaling sxsx 1 1 sysy y x y x

©College of Computer and Information Science, Northeastern UniversityJune 26, Rotation   sin(  ) cos(  ) -sin(  ) cos(  )

©College of Computer and Information Science, Northeastern UniversityJune 26, Rotation y x y x 

©College of Computer and Information Science, Northeastern UniversityJune 26, Reflection in y-axis

©College of Computer and Information Science, Northeastern UniversityJune 26, Reflection in y-axis y x y x

©College of Computer and Information Science, Northeastern UniversityJune 26, Reflection in x-axis

©College of Computer and Information Science, Northeastern UniversityJune 26, Reflection in x-axis y x y x

©College of Computer and Information Science, Northeastern UniversityJune 26, Shear-x s

©College of Computer and Information Science, Northeastern UniversityJune 26, Shear x y x y x

©College of Computer and Information Science, Northeastern UniversityJune 26, Shear-y s

©College of Computer and Information Science, Northeastern UniversityJune 26, Shear y y x y x

©College of Computer and Information Science, Northeastern UniversityJune 26, Linear Transformations Scale, Reflection, Rotation, and Shear are all linear transformations They satisfy: T( a u + b v) = a T(u) + b T(v)  u and v are vectors  a and b are scalars If T is a linear transformation  T((0, 0)) = (0, 0)

©College of Computer and Information Science, Northeastern UniversityJune 26, Composing Linear Transformations If T 1 and T 2 are transformations  T 2 T 1 (v) = def T 2 ( T 1 (v)) If T 1 and T 2 are linear and are represented by matrices M 1 and M 2  T 2 T 1 is represented by M 2 M 1  T 2 T 1 (v) = T 2 ( T 1 (v)) = (M 2 M 1 )(v)

©College of Computer and Information Science, Northeastern UniversityJune 26, Reflection About an Arbitrary Line ( through the origin ) y x y x

©College of Computer and Information Science, Northeastern UniversityJune 26, Reflection as a Composition y x

©College of Computer and Information Science, Northeastern UniversityJune 26, Decomposing Linear Transformations Any 2D Linear Transformation can be decomposed into the product of a rotation, a scale, and a rotation if the scale can have negative numbers. M = R 1 SR 2

©College of Computer and Information Science, Northeastern UniversityJune 26, Rotation about an Arbitrary Point y x  y x  This is not a linear transformation. The origin moves.

©College of Computer and Information Science, Northeastern UniversityJune 26, Translation y x y x (a, b) This is not a linear transformation. The origin moves. (x, y)  (x+a,y+b)

©College of Computer and Information Science, Northeastern UniversityJune 26, Homogeneous Coordinates x x y z y Embed the xy-plane in R 3 at z = 1. (x, y)  (x, y, 1)

©College of Computer and Information Science, Northeastern UniversityJune 26, D Linear Transformations as 3D Matrices Any 2D linear transformation can be represented by a 2x2 matrix or a 3x3 matrix

©College of Computer and Information Science, Northeastern UniversityJune 26, D Linear Translations as 3D Matrices Any 2D translation can be represented by a 3x3 matrix. This is a 3D shear that acts as a translation on the plane z = 1.

©College of Computer and Information Science, Northeastern UniversityJune 26, Translation as a Shear x x y z y

©College of Computer and Information Science, Northeastern UniversityJune 26, D Affine Transformations An affine transformation is any transformation that preserves co-linearity (i.e., all points lying on a line initially still lie on a line after transformation) and ratios of distances (e.g., the midpoint of a line segment remains the midpoint after transformation). With homogeneous coordinates, we can represent all 2D affine transformations as 3D linear transformations. We can then use matrix multiplication to transform objects.

©College of Computer and Information Science, Northeastern UniversityJune 26, y x Rotation about an Arbitrary Point y x  

©College of Computer and Information Science, Northeastern UniversityJune 26,  y x Rotation about an Arbitrary Point   T(-c x, -c y ) R(  ) T(c x, c y )

©College of Computer and Information Science, Northeastern UniversityJune 26, Windowing Transforms (a,b) (A,B) (c,d) (C,D) (C-c,D-d) (A-a,B-b) translate scale translate

©College of Computer and Information Science, Northeastern UniversityJune 26, D Transformations Remember: A 3D linear transformation can be represented by a 3x3 matrix.

©College of Computer and Information Science, Northeastern UniversityJune 26, D Affine Transformations

©College of Computer and Information Science, Northeastern UniversityJune 26, D Rotations