are copies of the overheads from each lecture up to the first midterm,
plus a couple extra ones (ex: suffix trees)
Lecture 1 (Wed. Jan. 5) : introduction, preview
Lecture 2 (Mon. Jan.10) : contour tracing, polygonal smoothing
Lecture 3 (Wed. Jan.12): "waterfall" smoothing of monotone chains,
Lecture 4 (Mon. Jan.17): Hysteresis smoothing, Iterative endpoints fit
Lecture 5 (Wed. Jan.19): Graph methods of polygonal chain approximation ,
speeding up computation using geometry.
- A paper (handed out at the tutorial on Jan.26) by Godfried Toussaint
- Iri-Imai algorithm
- Faster version for parallel-strip distance (using convex hulls)
Applet showing reduced DAG (first step of Iri-Imai algorithm).
More info on graph methods . Focus on sections 1,2,5
- Faster computation of Iterative Endpoints Fit algorithm, using extra
space. (notes given in overheads provided at top of page)
- Convex hulls:
Lecture 6 (Mon. Jan. 24) : Skeletonization, Medial Axis
Lecture 7 (Wed. Jan. 26) : Morphological operations on binary images
Lecture 8 (Mon. Jan. 31) : Edge detection
*** Wed. Feb. 2 was the first exam ***
Lectures 9 and 10 (Mon. Feb. 7 and Wed. Feb. 9) : Data depth
Lecture 11 (Mon. Feb. 14): Voronoi diagrams
Lecture 12 (Wed. Feb. 16): Rhythm
Voronoi Applet ... and description of quadratic algorithm (with typos)
- The two O(nlogn) algorithms (using 3D convex hulls and
sweep-line) are covered in the following two textbooks (each has a chapter
dedicated to Voronoi diagrams):
- An O(nlogn) time divide-and-conquer algorithm for computing
Voronoi diagram of a polygon (and also the medial axis) is in:
D. T. Lee, "Medial axis transformation of a planar shape," IEEE Trans.
Pattern Anal. Machine Intell., vol. PAMI-4, no. 4, pp. 363-369, July 1982.
You can find it
in the library Q327 I19 [Journal Loan] Schulich Science &
Euler's formula: V-E+F=2
A corollary is that there is at most a linear number of edges and
faces in a planar graph of n vertices.
- Lecture notes
*** Feb. 21 and 23 : reading week ***
- Guest lecture by Godfried Toussaint on
rhythm . As usual, you are only responsible for the notes relevant to
discussed in class.
Lecture 13 (Mon. Feb. 28): Bayesian decision theory
Lecture 14 (Wed. Mar. 2): Proximity graphs
Lecture 15 (Mon. Mar. 7): Parameter estimation
Lecture 16 (Wed. Mar. 9): A note on feature selection, and NN
Lecture 17 (Mon. Mar. 14): Editing data sets for better NN-classification
Lecture 18 (Wed. Mar. 16): String matching: suffix trees
Lecture 19 (Mon. Mar. 21): Return of the suffix tree
Lecture 20 (Wed. Mar. 23): Godfried's proximity graph condensing etc
Notes from class
- You can also check Erin's copy (link at top of this page)
Between the chopped off borders on her copies, and the white blob in
the middle of my copies (from flash), you can figure things out I hope.
*** Mon. Mar. 28: no class (Easter) ***
- Handout given in class. This is all you are responsible for.
*** Wed. Mar. 30: 2nd exam ***
*** Apr. 4,6,11,13 : presentations ***