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2-D Geometry. The Image Formation Pipeline. Outline. Vector, matrix basics 2-D point transformations Translation, scaling, rotation, shear Homogeneous coordinates and transformations Homography, affine transformation. Notes on Notation (a little different from book).
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Outline • Vector, matrix basics • 2-D point transformations • Translation, scaling, rotation, shear • Homogeneous coordinates and transformations • Homography, affine transformation
Notes on Notation (a little different from book) • Vectors, points: x, v (assume column vectors) • Matrices: R, T • Scalars: x, a • Axes, objects: X, Y, O • Coordinate systems: W, C • Number systems: R, Z • Specials • Transpose operator: xT (as opposed to x0) • Identity matrix: Id • Matrices/vectors of zeroes, ones: 0, 1
Block Notation for Matrices • Often convenient to write matrices in terms of parts • Smaller matrices for blocks • Row, column vectors for ranges of entries on rows, columns, respectively • E.g.: If A is 3 x 3 and :
2-D Transformations • Types • Scaling • Rotation • Shear • Translation • Mathematical representation
2-D Scaling sx 1 Horizontal shift proportional to horizontal position
2-D Scaling sy 1 Vertical shift proportional to vertical position
µ 2-D Rotation
µ 2-D Rotation
µ Matrix form of 2-D Rotation (this is a counterclockwise rotation; reverse signs of sines to get a clockwise one)
µ Matrix form of 2-D Rotation
2-D Shear (Horizontal) Horizontal displacement proportional to vertical position
2-D Shear (Horizontal) (Shear factorh is positive for the figure above)
Representing Transformations • Note that we’ve defined translation as a vector addition but rotation, scaling, etc. as matrix multiplications • It’s inconvenient to have two different operations (addition and multiplication) for different forms of transformation • It would be desirable for all transformations to be expressed in a common, linear form (since matrix multiplications are linear transformations)
Example: “Trick” of additional coordinate makes this possible • Old way: • New way:
Translation Matrix • We can write the formula using this “expanded” transformation matrix as: • From now on, assume points are in this “expanded” form unless otherwise noted:
Homogeneous Coordinates • “Expanded” form is called homogeneous coordinates or projective space • Technically, 2-D projective space P2 is defined as Euclidean space R3¡ (0, 0, 0)T • Change to projective space by adding a scale factor (usually but not always 1):
Homogeneous Coordinates: Projective Space • Equivalence is defined up to scale, (non-zero for finite points) • Think of projective points in P2 as rays in R3, where z coordinate is scale factor • All Euclidean points along ray are “same” in this sense
Leaving Projective Space • Can go back to non-homogeneous representation by dividing by scale factor and dropping extra coordinate: • This is the same as saying “Where does the ray intersect the plane defined by z = 1”?
Homogeneous Coordinates: Rotations, etc. • A 2-D rotation, scaling, shear or other transformation normally expressed by a 2 x 2 matrix R is written in homogeneous coordinates with the following 3 x 3 matrix: • The non-commutativity of matrix multiplication explains why different transformation orders give different results—i.e., RT TR In homogeneous form In homogeneous form
2-D Transformations • Full-generality 3 x 3 homogeneous transformation is called a homography
2-D Transformations • Full-generality 3 x 3 homogeneous transformation is called a homography Translation components
2-D Transformations • Full-generality 3 x 3 homogeneous transformation is called a homography Scale/rotation components
2-D Transformations • Full-generality 3 x 3 homogeneous transformation is called a homography Shear/rotation components
2-D Transformations • Full-generality 3 x 3 homogeneous transformation is called a homography Homogeneous scaling factor
2-D Transformations • Full-generality 3 x 3 homogeneous transformation is called a homography When these are zero (as they have been so far), H is an affine transformation