Difference between revisions of "Applied Computer Vision Lecture Schedule"

From David Vernon's Wiki
Jump to: navigation, search
Line 16: Line 16:
 
| Overview of human and computer vision  
 
| Overview of human and computer vision  
 
|
 
|
−
|[http://www.vernon.eu/ACV/ACV_01.pdf Lecture 1 Slides]. Szeliski 2010, Chapter 1. ]
+
|[http://www.vernon.eu/ACV/ACV_01.pdf Lecture 1 Slides]. Szeliski 2010, Sections 1.1 and 1.2. Kragic and Vincze, 2010.  [http://www.cs.ubc.ca/~lowe/vision.html David Lowe's website of industrial vision systems].
 
|
 
|
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
 
| Wed. 30 Aug.
 
| Wed. 30 Aug.
 
| 2  
 
| 2  
−
| Formalisms for Representing Algorithms
+
| OpenCV and software development tools for course work 
−
| Definition of an algorithm. Modelling software. Relational modelling. State modelling. Practical representations. Pseudo code. Flow charts. Finite state machines. UML. Predicate logic. Analysis.
+
|
−
| [http://www.vernon.eu/04-630/04-630_Lecture_02_Formalisms_for_Representing_Algorithms.pdf Lecture 2 Slides]. Harel 2004, Chapters 1 and 2.
+
| [http://www.vernon.eu/ACV/ACV_02.pdf Lecture 2 Slides].  
−
| [http://www.vernon.eu/04-630/04-630_Assignment_1.pdf Assignment 1]
+
|
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
 
| Mon. 4 Sept.
 
| Mon. 4 Sept.
 
| 3
 
| 3
−
| Analysis of Complexity I
+
| Optics, sensors, and image formation
−
| Performance of algorithms, time and space tradeoff, worst case and average case performance. Big O notation. Recurrence relationships. Analysis of complexity of iterative and recursive algorithms. Recursive vs. iterative algorithms: runtime memory implications.
+
|
−
| [http://www.vernon.eu/04-630/04-630_Lecture_03_Analysis_of_Complexity_I.pdf Lecture 3 Slides]. Aho et al. 1983, Chapter 1.
+
| 
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
 
| Wed. 6 Sept.
 
| Wed. 6 Sept.
 
| 4
 
| 4
−
| Analysis of Complexity II
+
| Image acquisition and image representation
−
| Complexity theory: tractable vs intractable algorithmic complexity. Example intractable problems: travelling salesman problem, Hamiltonian circuit, 3-colour problem, SAT, cliques. Determinism and non-determinism. P, NP, and NP-Complete classes of algorithm.
+
|  
−
| [http://www.vernon.eu/04-630/04-630_Lecture_04_Analysis_of_Complexity_II.pdf Lecture 4 Slides]. Aho et al. 1983, Chapter 1.
+
|
−
| [http://www.vernon.eu/04-630/04-630_Assignment_2.pdf Assignment 2]
+
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
 
| Mon. 11 Sept.
 
| Mon. 11 Sept.
 
| 5
 
| 5
−
| Searching and Sorting Algorithms I
+
| Image processing
−
| Linear and binary search (iterative and recursive). In-place sorts: bubblesort (efficient and inefficient), selection sort, insertion sort.
+
| point & neighbourhood operations, image filtering, convolution, Fourier transform 
−
| [http://www.vernon.eu/04-630/04-630_Lecture_05_Searching_and_Sorting_Algorithms_I.pdf Lecture 5 Slides]. Aho et al. 1983, Chapter 8.
+
|  
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
 
| Wed. 13 Sept.
 
| Wed. 13 Sept.
 
| 6
 
| 6
−
| Searching and Sorting Algorithms I
+
| Image processing
−
| Not-in-place sorts: Quicksort, merge sort. Complexity analysis. Characteristics of a good sort. Speed, consistency, keys, memory usage, length & code complexity, stability. Other sorts ordered by complexity.
+
| Morphological operations
−
|[http://www.vernon.eu/04-630/04-630_Lecture_06_Searching_and_Sorting_Algorithms_II.pdf Lecture 6 Slides]. Aho et al. 1983, Chapter 8.
+
|  
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
 
| Mon. 18 Sept.
 
| Mon. 18 Sept.
 
| 7
 
| 7
−
| Abstract Data Types (ADT)
+
| Image processing
−
| <!-- Vector example exercise. History of abstraction. --> Abstract Data Types (ADT). Information hiding. Types and typing. <!-- Encapsulation. Efficiency. --> Design Goals. Design practices.
+
| Geometric operations
−
| [http://www.vernon.eu/04-630/04-630_Lecture_07_Abstract_Data_Types.pdf Lecture 7 Slides].
+
|
 
|
 
|
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
 
| Wed. 20 Sept.
 
| Wed. 20 Sept.
 
| 8
 
| 8
−
| Containers, Dictionaries, and Lists I
+
|Segmentation
−
| Container and dictionaries: mechanisms for accessing data in a list.  List ADT.  Implementation with arrays.
+
|Region-based approaches, binary thresholding, connected component analysis 
−
|  [http://www.vernon.eu/04-630/04-630_Lecture_08_Containers,_Dictionaries,_and_Lists_I.pdf Lecture 8 Slides].
+
|
−
| [http://www.vernon.eu/04-630/04-630_Assignment_3.pdf Assignment 3] <!-- <BR> [[Assignment 3 Status]] -->
+
|
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
 
| Mon. 25 Sept.
 
| Mon. 25 Sept.
 
| 9
 
| 9
−
| Containers, Dictionaries, and Lists II
+
| Segmentation
−
| List ADT.  Implementation with linked lists. Doubly linked lists and circular lists. Performance considerations.
+
| Edge detection
−
|   [http://www.vernon.eu/04-630/04-630_Lecture_09_Containers,_Dictionaries,_and_Lists_II.pdf Lecture 9 Slides].
+
|  
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
 
| Wed. 27 Feb.
 
| Wed. 27 Feb.
 
| 10
 
| 10
−
| Stacks
+
| Segmentation
−
| Stack (LIFO) ADT. Implementation using List ADT (array and linked-list). Comparison of order of complexity. Stack applications, including token matching, evaluation of postfix expressions, and conversion of infix expressions to postfix.
+
| Colour-based approaches; k-means clustering  
−
| [http://www.vernon.eu/04-630/04-630_Lecture_10_Stacks.pdf Lecture 10 Slides].
+
|
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
Line 92: Line 92:
 
| 12
 
| 12
 
|   
 
|   
−
| Concepts and terminology: level, height, external and internal nodes, skinny, fat, complete, left-complete, perfect, multi-way, d-ary. Types of tree: binary, binary search, B-tree, 2-3 tree, AVL, Red-Black
+
|
−
|  [http://www.vernon.eu/04-630/04-630_Lecture_12_Trees_I.pdf Lecture 12 Slides].
+
|   
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
Line 100: Line 100:
 
|
 
|
 
|  
 
|  
−
|  [http://www.vernon.eu/04-630/04-630_Lecture_13_Trees_II.pdf Lecture 13 Slides].
+
|   
−
| [http://www.vernon.eu/04-630/04-630_Lab_Exercise_1.pdf Lab Exercise 1]
+
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
 
| Wed. 11 Oct.
 
| Wed. 11 Oct.

Revision as of 16:05, 20 August 2017

|CARNEGIE MELLON UNIVERSITY AFRICA|

Date Lecture Topic Material covered Reading Assignments
Mon. 28 Aug. 1 Overview of human and computer vision Lecture 1 Slides. Szeliski 2010, Sections 1.1 and 1.2. Kragic and Vincze, 2010. David Lowe's website of industrial vision systems.
Wed. 30 Aug. 2 OpenCV and software development tools for course work Lecture 2 Slides.
Mon. 4 Sept. 3 Optics, sensors, and image formation
Wed. 6 Sept. 4 Image acquisition and image representation
Mon. 11 Sept. 5 Image processing point & neighbourhood operations, image filtering, convolution, Fourier transform
Wed. 13 Sept. 6 Image processing Morphological operations
Mon. 18 Sept. 7 Image processing Geometric operations
Wed. 20 Sept. 8 Segmentation Region-based approaches, binary thresholding, connected component analysis
Mon. 25 Sept. 9 Segmentation Edge detection
Wed. 27 Feb. 10 Segmentation Colour-based approaches; k-means clustering
Mon. 2 Oct. 11 Queue (FIFO ADT). Implementation using List ADT (array and linked-list). Comparison of order of complexity. Dedicated ADT. Circular queues. Queue applications. Lecture 11 Slides. On the Simulation of Random Events.
Wed. 4 Oct. 12
Mon. 9 Oct. 13
Wed. 11 Oct. 14
Mon. 16 Oct. 15
Wed. 18 Oct. 16
Mon. 23 Oct. 17
Wed. 25 Oct. 18
Mon. 30 Oct. 19
Wed. 1 Nov. 20
Mon. 6 Nov. 21
Wed. 8 Nov. 22
Mon. 13 Nov. 23
Wed. 15 Nov. 24
Mon. 20 Nov. 25
Wed. 22 Nov. 26
Mon. 27 Nov. 27
Wed. 29 Nov. 28
Mon. 4 Dec. 29
Wed. 6 Dec. 30


Back to Applied Computer Vision

Retrieved from "http://www.vernon.eu/w/index.php?title=Applied_Computer_Vision_Lecture_Schedule&oldid=1501"