Difference between revisions of "Applied Computer Vision Lecture Schedule"

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! scope="col" style="width: 3%;" | Lecture
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! scope="col" style="width: 13%;" | Reading
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! scope="col" style="width: 20%;" | Reading                   <!-- 13% -->
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! scope="col" style="width: 13%;" | Assignments
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! scope="col" style="width: 20%;" | Assignments           <!-- 13% -->
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
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| Wed. 18 Jan.
+
| Tue. 29 Aug.
 
| 1
 
| 1
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| Introduction & The Software Development Life Cycle
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| Overview
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|Motivation. Goals of the course. Syllabus and lecture schedule. Course operation. Preview of course material. Overview of labs, assignments, and exercises. Software development tools for assignments. Levels of abstraction in information processing systems. The software development life cycle: Yourdon Structured Analysis - functional,  data, and behavioural models (hierarchical decomposition trees, architecture diagrams, data flow diagrams DFD, data dictionaries, entity relationship ER diagrams, state transition diagrams). Software process models: waterfall, evolutionary, formal transformation, re-use, hybrid, spiral.
+
| Human and computer vision
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|[http://www.vernon.eu/04-630/04-630_Lecture_01_Intro_and_SW_Dev_Life_Cycle.pdf Lecture 1 Slides]. Harel 2004, Chapter 13. [http://agile.csc.ncsu.edu/SEMaterials/AgileMethods.pdf Williams 2007]. Optional: [http://www.vernon.eu/04-630/Software_Development_Life_Cycle.pdf Software Development Life Cycle], [http://www.vernon.eu/wiki/The_CINDY_Cognitive_Architecture#Software_Engineering_Standards Software Standards]
+
|[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;"
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| Mon. 23 Jan.
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| Thur. 31 Aug.
 
| 2  
 
| 2  
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| Formalisms for Representing Algorithms
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| Software tools
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| Definition of an algorithm. Modelling software. Relational modelling. State modelling. Practical representations. Pseudo code. Flow charts. Finite state machines. UML. Predicate logic. Analysis.
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| OpenCV, Software development tools for course work 
−
| [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;"
−
| Wed. 25 Jan.
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| Tue. 5 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.
+
| Illumination, projections, lenses, Gauss lens equation, field of view, depth of field, CMOS and CCD sensors, colour sensors, noise, resolution.
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| [http://www.vernon.eu/04-630/04-630_Lecture_03_Analysis_of_Complexity_I.pdf Lecture 3 Slides]. Aho et al. 1983, Chapter 1.
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|
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 30 Jan.
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| Thur. 7 Sept.
 
| 4
 
| 4
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| Analysis of Complexity II
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| Image acquisition and image representation
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| 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.
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| Sampling and quantization, Shannon's sampling theorem, Nyquist frequency, Nyquist sampling rate, aliasing, resolution, space-variant sampling, log-polar images, dynamic range, colour spaces: HIS, HLS, HSV
−
| [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. 6 Feb.
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| Tue. 12 Sept.
 
| 5
 
| 5
−
| Searching and Sorting Algorithms I
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| Image processing
−
| Linear and binary search (iterative and recursive). In-place sorts: bubblesort (efficient and inefficient), selection sort, insertion sort.
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| 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.
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|  
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 8 Feb.
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| Thur. 14 Sept.
 
| 6
 
| 6
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| Searching and Sorting Algorithms I
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| Image processing
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| 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.
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| 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. 13 Feb.
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| Tue. 19 Sept.
 
| 7
 
| 7
−
| Abstract Data Types (ADT)
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| Image processing
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| <!-- 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. 15 Feb.
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| Thur. 21 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. 20 Feb.
+
| Tue. 26 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. 22 Feb.
+
| Thur. 28 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;"
−
| Mon. 27 Feb.
+
| Tue. 3 Oct.
 
| 11
 
| 11
−
|  Queues
+
| Image features  
−
| Queue (FIFO ADT). Implementation using List ADT (array and linked-list).  Comparison of order of complexity.  Dedicated ADT. Circular queues.  Queue applications.
+
| Harris and Difference of Gaussian interest point operators
−
|  [http://www.vernon.eu/04-630/04-630_Lecture_11_Queues.pdf Lecture 11 Slides]. [http://www.vernon.eu/04-630/poisson.pdf  On the Simulation of Random Events].
+
|   
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 1 Mar.
+
| Thur. 5 Oct.
 
| 12
 
| 12
−
| Trees I
+
| Image features
−
| 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
+
| SIFT feature descriptor
−
|  [http://www.vernon.eu/04-630/04-630_Lecture_12_Trees_I.pdf Lecture 12 Slides].
+
|   
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 6 Mar.
+
| Tue. 10 Oct.
 
| 13
 
| 13
−
| Trees II
+
| Object recognition
−
| Binary trees and binary search trees. Tree traversals: inorder, preorder, postorder.
+
| Template matching; normalized cross-correlation; chamfer matching 
−
|  [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. 8 Mar.
+
| Thur. 12 Oct.
 
| 14
 
| 14
−
| Trees III
+
| Object recognition
−
| Height-balanced trees: AVL Trees, RR, RL, LR, LL rotations.  
+
| 2D shape features; statistical pattern recognition  
−
| [http://www.vernon.eu/04-630/04-630_Lecture_14_Trees_III.pdf Lecture 14 Slides].
+
|  
−
| [http://www.vernon.eu/04-630/04-630_Assignment_4.pdf Assignment 4] <!-- <BR> [[Assignment 4 Status]] -->
+
|
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 13 Mar.
+
| Tue. 17 Oct.
 
| 15
 
| 15
−
| Trees IV
+
| Object recognition
−
| Height-balanced trees: Red-Black Trees, single promotion, zig-zag promotion, recolouring and restructuring.  
+
| Hough transform for parametric curves: lines, circles, and ellipses  
−
| [http://www.vernon.eu/04-630/04-630_Lecture_15_Trees_IV.pdf Lecture 15 Slides].
+
|  
−
| [http://www.vernon.eu/04-630/04-630_Lab_Exercise_2.pdf Lab Exercise 2]
+
|
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 15 Mar.
+
| Thur. 19 Oct.
 
| 16
 
| 16
−
| Trees V
+
| Object recognition
−
| Tree applications. Fixed-length codes & variable length codes. Optimal code trees. Huffman's algorithm. <!-- and implementation. -->
+
| Generalized Hough transform; extension to codeword features  
−
|  [http://www.vernon.eu/04-630/04-630_Lecture_16_Trees_V.pdf Lecture 16 Slides].
+
|   
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 20 Mar.
+
| Tue. 24 Oct.
 
| 17
 
| 17
−
| Heaps
+
| Object recognition
−
| Priority queues. Heap basics. Types of heap: min heaps and max heap. Heap characteristics.  Implementation of heap. Heap operations: delete max/min, down heap, up heap, merge, construct, heapify. Complexity of operations.  Heap sort.  <!-- Operating systems heaps.  d-ary heaps. Leftist heaps. -->
+
| Colour histogram matching and back-projection
−
|  [http://www.vernon.eu/04-630/04-630_Lecture_17_Heaps.pdf Lecture 17 Slides].
+
|   
−
| [http://www.vernon.eu/04-630/04-630_Lab_Exercise_3.pdf Lab Exercise 3] <BR> [http://www.vernon.eu/04-630/04-630_Lab_Exercise_3_Alternative.pdf Lab Exercise 3 Alternative]
+
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 22 Mar.
+
| Thur. 26 Oct.
 
| 18
 
| 18
−
| Graphs I
+
| Object recognition
−
| Types of graphs: directed, undirected, weighted, unweighted, cyclic, acyclic, directed acyclic, simple, non-simple, implicit, explicit, embedded, topological. Adjacency matrix representation. Adjacency list representation.  
+
| Haar features and boosted classifiers  
−
|  [http://www.vernon.eu/04-630/04-630_Lecture_18_Graphs_I.pdf Lecture 18 Slides]
+
|   
−
| [http://www.vernon.eu/04-630/04-630_Assignment_5.pdf Assignment 5] <!-- <BR> [[Assignment 5 Status]] -->
+
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 27 Mar.
+
| Tue. 31 Oct.
 
| 19
 
| 19
−
| Graphs II
+
| Object recognition
−
|  Graph traversal: breadth-first search and depth-first search; implementation and application.  Topological sort. <!-- Euler's theorem. -->
+
|  Histogram of Oriented Gradients (HOG) feature descriptor 
−
|   [http://www.vernon.eu/04-630/04-630_Lecture_19_Graphs_II.pdf Lecture 19 Slides]
+
|  
−
|  [http://www.vernon.eu/04-630/04-630_Lab_Exercise_4.pdf Lab Exercise 4]
+
|   
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 29 Mar.
+
| Thur. 2 Nov.
 
| 20
 
| 20
−
| Graphs III
+
| Video image processing
−
| Spanning trees and minimum spanning trees, Kruskal's algorithm, Prim's algorithm.  
+
| Background subtraction and object tracking  
−
|   [http://www.vernon.eu/04-630/04-630_Lecture_20_Graphs_III.pdf Lecture 20 Slides]
+
|  
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 3 Apr.
+
| Tue. 7 Nov.
 
| 21
 
| 21
−
| Graphs IV
+
| 3D vision
−
| Dijkstra's shortest path algorithm. Floyd-Warshall's all-pairs algorithm.   
+
| Homogeneous transformations. Perspective transformation. Camera model and inverse perspective transformation  
−
|   [http://www.vernon.eu/04-630/04-630_Lecture_21_Graphs_IV.pdf Lecture 21 Slides]
+
|  
−
|  [http://www.vernon.eu/04-630/04-630_Lab_Exercise_5.pdf Lab Exercise 5]
+
|   
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 5 Apr.
+
| Thur. 9 Nov.
 
| 22
 
| 22
−
| Complex Networks I
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| Stereo vision.
−
| Euler's theorem: the Bridges of Königsberg. Networks vs. graphs. Degree, average degree, and degree distribution. Bipartite networks. Path length, BFS, Connectivity, Components.  Clustering coefficient.  Random graph model. Small world phenomena. Scale free networks.  
+
| Stereo correspondence, Epipolar geometry  
−
|  [http://www.vernon.eu/04-630/04-630_Lecture_22_Complex_Networks_I.pdf Lecture 22 Slides]
+
|   
−
|  [http://www.vernon.eu/04-630/04-630_Assignment_6.pdf Assignment 6] <!-- <BR> [[Assignment 6 Status]] -->
+
|   
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 19 Apr.
+
| Tue. 14 Nov.
 
| 23
 
| 23
−
| Complex Networks II
+
| Optical flow 
−
|  Communities. Fundamental Hypothesis. Connectedness and Density Hypothesis. Strong and weak communities. Graph partitioning. Community detection. Hierarchical clustering. Girvan-Newman Algorithm. Modularity. Random Hypothesis. Maximum Modularity Hypothesis. Greedy algorithm for community detection by maximizing modularity. Overlapping communities. Clique percolation algorithm and CFinder.
+
|   
−
|  [http://www.vernon.eu/04-630/04-630_Lecture_23_Complex_Networks_II.pdf Lecture 23 Slides]
+
|   
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 24 Apr.
+
| Thur. 16 Nov.
 
| 24
 
| 24
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| Algorithmic Strategies
+
| Visual attention
−
| Classes of algorithms. Brute force. Divide and conquer. Greedy algorithms. Dynamic programming. Combinatorial search. Backtracking. Pruning. Branch and bound. <!-- Heuristics and heuristic algorithms. Probabilistic algorithms. -->
+
|  Saliency, Bottom-up and top-down attention 
−
|  [http://www.vernon.eu/04-630/04-630_Lecture_24_Algorithmic_Strategies.pdf Lecture 24 Slides]
+
|   
−
| [http://www.vernon.eu/04-630/04-630_Lab_Exercise_6.pdf Lab Exercise 6]
+
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 26 Apr.
+
| Tue. 21 Nov.
 
| 25
 
| 25
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| Hashing
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|  Clustering, grouping, and segmentation
−
|Dictionaries. Hashing. Hash functions. Collision resolution. Complexity. Applications. <!-- Using keys to address data. Mappings: injection, surjection, bijection. Map ADT. Hash functions. Hash tables: current value tables, direct access tables. Managing collisions: chaining, overflow areas, re-hashing, linear probing, quadratic probing.  Evaluating hash functions: prime division, mid-square, folding, load factor. Example application: dictionaries. Generating hash functions and using hash structures. -->
+
| Gestalt principles. Clustering algorithms  
−
|  [http://www.vernon.eu/04-630/04-630_Lecture_25_Hashing.pdf Lecture 25 Slides]
+
|   
−
|  [http://www.vernon.eu/04-630/04-630_Assignment_7.pdf Assignment 7]
+
|   
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 3 May
+
| Thur. 23 Nov.
 
| 26
 
| 26
−
| Analysis of Correctness
+
| Object recognition in 3D
−
|Types of software defects. Code module design. Syntactic, semantic, logical defects. (Semi-)formal verification: partial vs. total correctness. Invariant assertion method. Simple proof strategies: by contradiction, counterexample, induction. Dynamic testing: unit tests, test harness, stubs, drivers, integration testing, regression testing. Static tests: reviews, walkthroughs, inspections, reviewing algorithms and software. Pair programming. Verification and validation strategies.
+
| Object detection, object recognition, object categorisation 
−
| [http://www.vernon.eu/04-630/04-630_Lecture_26_Analysis_of_Correctness.pdf Lecture 26 Slides]
+
|  
 
|  
 
|  
−
<!-- |- style="vertical-align: top;"
+
|- style="vertical-align: top;"
−
| Wed. 3 May
+
| Tue. 28 Nov.
 
| 27
 
| 27
−
| Automata & Computability Theory
+
| Affordances 
−
| Regular Languages. Finite Automata. Nondeterminism.- Regular Expressions. Nonregular Languages. Context-free Languages. Context-free Grammars.  Pushdown Automata. Deterministic Context-Free Languages. The Church-Turing Thesis. Turing Machines. Variants of Turing Machines. The Definition of Algorithm. Decidability. Decidable Languages. Undecidability. Reducibility.
+
|   
 
|  
 
|  
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| 
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|- style="vertical-align: top;"
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| Thur. 30 Nov.
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| 28
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| Computer vision and machine learning 
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| 
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|- style="vertical-align: top;"
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| Tue. 5 Dec.
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| 29
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|  Computer vision and machine learning 
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| 
 +
|
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|
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|- style="vertical-align: top;"
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| Thur. 7 Dec.
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| 30
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|  Computer vision and machine learning 
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| 
 
|  
 
|  
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-->
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Back to [[Applied Computer Vision]]
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Back to [http://www.vernon.eu/ACV.htm Applied Computer Vision]

Latest revision as of 13:52, 22 August 2017

|CARNEGIE MELLON UNIVERSITY AFRICA|

Date Lecture Topic Material covered Reading Assignments
Tue. 29 Aug. 1 Overview Human and computer vision Lecture 1 Slides. Szeliski 2010, Sections 1.1 and 1.2. Kragic and Vincze, 2010.
Thur. 31 Aug. 2 Software tools OpenCV, Software development tools for course work Lecture 2 Slides.
Tue. 5 Sept. 3 Optics, sensors, and image formation Illumination, projections, lenses, Gauss lens equation, field of view, depth of field, CMOS and CCD sensors, colour sensors, noise, resolution.
Thur. 7 Sept. 4 Image acquisition and image representation Sampling and quantization, Shannon's sampling theorem, Nyquist frequency, Nyquist sampling rate, aliasing, resolution, space-variant sampling, log-polar images, dynamic range, colour spaces: HIS, HLS, HSV
Tue. 12 Sept. 5 Image processing Point & neighbourhood operations, image filtering, convolution, Fourier transform
Thur. 14 Sept. 6 Image processing Morphological operations
Tue. 19 Sept. 7 Image processing Geometric operations
Thur. 21 Sept. 8 Segmentation Region-based approaches, binary thresholding, connected component analysis
Tue. 26 Sept. 9 Segmentation Edge detection
Thur. 28 Feb. 10 Segmentation Colour-based approaches; k-means clustering
Tue. 3 Oct. 11 Image features Harris and Difference of Gaussian interest point operators
Thur. 5 Oct. 12 Image features SIFT feature descriptor
Tue. 10 Oct. 13 Object recognition Template matching; normalized cross-correlation; chamfer matching
Thur. 12 Oct. 14 Object recognition 2D shape features; statistical pattern recognition
Tue. 17 Oct. 15 Object recognition Hough transform for parametric curves: lines, circles, and ellipses
Thur. 19 Oct. 16 Object recognition Generalized Hough transform; extension to codeword features
Tue. 24 Oct. 17 Object recognition Colour histogram matching and back-projection
Thur. 26 Oct. 18 Object recognition Haar features and boosted classifiers
Tue. 31 Oct. 19 Object recognition Histogram of Oriented Gradients (HOG) feature descriptor
Thur. 2 Nov. 20 Video image processing Background subtraction and object tracking
Tue. 7 Nov. 21 3D vision Homogeneous transformations. Perspective transformation. Camera model and inverse perspective transformation
Thur. 9 Nov. 22 Stereo vision. Stereo correspondence, Epipolar geometry
Tue. 14 Nov. 23 Optical flow
Thur. 16 Nov. 24 Visual attention Saliency, Bottom-up and top-down attention
Tue. 21 Nov. 25 Clustering, grouping, and segmentation Gestalt principles. Clustering algorithms
Thur. 23 Nov. 26 Object recognition in 3D Object detection, object recognition, object categorisation
Tue. 28 Nov. 27 Affordances
Thur. 30 Nov. 28 Computer vision and machine learning
Tue. 5 Dec. 29 Computer vision and machine learning
Thur. 7 Dec. 30 Computer vision and machine learning


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