Difference between revisions of "Applied Computer Vision Lecture Schedule"

From David Vernon's Wiki
Jump to: navigation, search
 
(3 intermediate revisions by the same user not shown)
Line 12: Line 12:
 
! scope="col" style="width: 20%;" | Assignments            <!-- 13% -->
 
! scope="col" style="width: 20%;" | Assignments            <!-- 13% -->
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 28 Aug.
+
| Tue. 29 Aug.
 
| 1
 
| 1
 
| Overview
 
| Overview
Line 19: Line 19:
 
|
 
|
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 30 Aug.
+
| Thur. 31 Aug.
 
| 2  
 
| 2  
 
| Software tools
 
| Software tools
Line 26: Line 26:
 
|
 
|
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 4 Sept.
+
| Tue. 5 Sept.
 
| 3
 
| 3
 
| Optics, sensors, and image formation  
 
| 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.
 
|   
 
|   
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 6 Sept.
+
| Thur. 7 Sept.
 
| 4
 
| 4
 
| Image acquisition and image representation  
 
| 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
 
|
 
|
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 11 Sept.
+
| Tue. 12 Sept.
 
| 5
 
| 5
 
| Image processing
 
| Image processing
Line 47: Line 47:
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 13 Sept.
+
| Thur. 14 Sept.
 
| 6
 
| 6
 
| Image processing
 
| Image processing
Line 54: Line 54:
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 18 Sept.
+
| Tue. 19 Sept.
 
| 7
 
| 7
 
| Image processing
 
| Image processing
Line 61: Line 61:
 
|
 
|
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 20 Sept.
+
| Thur. 21 Sept.
 
| 8
 
| 8
 
|Segmentation
 
|Segmentation
Line 68: Line 68:
 
|
 
|
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 25 Sept.
+
| Tue. 26 Sept.
 
| 9
 
| 9
 
| Segmentation  
 
| Segmentation  
Line 75: Line 75:
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 27 Feb.
+
| Thur. 28 Feb.
 
| 10
 
| 10
 
| Segmentation
 
| Segmentation
Line 82: Line 82:
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 2 Oct.
+
| Tue. 3 Oct.
 
| 11
 
| 11
−
|  
+
| 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. 4 Oct.
+
| Thur. 5 Oct.
 
| 12
 
| 12
−
|   
+
|  Image features
−
|   
+
|  SIFT feature descriptor
 
|   
 
|   
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 9 Oct.
+
| Tue. 10 Oct.
 
| 13
 
| 13
−
|
+
| Object recognition
−
|  
+
| Template matching; normalized cross-correlation; chamfer matching 
 
|   
 
|   
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 11 Oct.
+
| Thur. 12 Oct.
 
| 14
 
| 14
−
|
+
| Object recognition
−
|
+
| 2D shape features; statistical pattern recognition 
 
|  
 
|  
 
|   
 
|   
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 16 Oct.
+
| Tue. 17 Oct.
 
| 15
 
| 15
−
|   
+
|  Object recognition
−
|  
+
| Hough transform for parametric curves: lines, circles, and ellipses 
 
|  
 
|  
 
|   
 
|   
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 18 Oct.
+
| Thur. 19 Oct.
 
| 16
 
| 16
−
|   
+
|  Object recognition
−
|  
+
| Generalized Hough transform; extension to codeword features 
 
|   
 
|   
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 23 Oct.
+
| Tue. 24 Oct.
 
| 17
 
| 17
−
|
+
| Object recognition
−
|  
+
| Colour histogram matching and back-projection
 
|   
 
|   
 
|   
 
|   
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 25 Oct.
+
| Thur. 26 Oct.
 
| 18
 
| 18
−
|  
+
| Object recognition
−
|  
+
| Haar features and boosted classifiers 
 
|   
 
|   
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 30 Oct.
+
| Tue. 31 Oct.
 
| 19
 
| 19
−
|   
+
|  Object recognition
−
|  
+
| Histogram of Oriented Gradients (HOG) feature descriptor 
 
|  
 
|  
 
|   
 
|   
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 1 Nov.
+
| Thur. 2 Nov.
 
| 20
 
| 20
−
|   
+
|  Video image processing
−
|   
+
| Background subtraction and object tracking  
 
|     
 
|     
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 6 Nov.
+
| Tue. 7 Nov.
 
| 21
 
| 21
−
|   
+
|  3D vision
−
|  
+
| Homogeneous transformations. Perspective transformation. Camera model and inverse perspective transformation 
 
|  
 
|  
 
|   
 
|   
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 8 Nov.
+
| Thur. 9 Nov.
 
| 22
 
| 22
−
|
+
| Stereo vision.
−
|   
+
| Stereo correspondence, Epipolar geometry  
 
|   
 
|   
 
|   
 
|   
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 13 Nov.
+
| Tue. 14 Nov.
 
| 23
 
| 23
−
|   
+
| Optical flow  
 
|   
 
|   
 
|   
 
|   
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 15 Nov.
+
| Thur. 16 Nov.
 
| 24
 
| 24
−
|   
+
|  Visual attention
−
|   
+
| Saliency, Bottom-up and top-down attention  
 
|   
 
|   
 
|   
 
|   
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 20 Nov.
+
| Tue. 21 Nov.
 
| 25
 
| 25
−
|   
+
|  Clustering, grouping, and segmentation
−
|  
+
| Gestalt principles. Clustering algorithms 
 
|   
 
|   
 
|   
 
|   
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 22 Nov.
+
| Thur. 23 Nov.
 
| 26
 
| 26
−
|   
+
|  Object recognition in 3D
−
|  
+
| Object detection, object recognition, object categorisation 
 
|   
 
|   
 
|  
 
|  
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 27 Nov.
+
| Tue. 28 Nov.
 
| 27
 
| 27
−
|   
+
| Affordances  
 
|   
 
|   
 
|  
 
|  
 
|   
 
|   
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 29 Nov.
+
| Thur. 30 Nov.
 
| 28
 
| 28
−
|   
+
| Computer vision and machine learning  
 
|   
 
|   
 
|  
 
|  
 
|   
 
|   
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Mon. 4 Dec.
+
| Tue. 5 Dec.
 
| 29
 
| 29
−
|   
+
| Computer vision and machine learning  
 
|   
 
|   
 
|  
 
|  
 
|
 
|
 
|- style="vertical-align: top;"
 
|- style="vertical-align: top;"
−
| Wed. 6 Dec.
+
| Thur. 7 Dec.
 
| 30
 
| 30
−
|   
+
| Computer vision and machine learning  
 
|   
 
|   
 
|  
 
|  

Latest revision as of 12:52, 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


Back to Applied Computer Vision

Retrieved from "http://www.vernon.eu/w/index.php?title=Applied_Computer_Vision_Lecture_Schedule&oldid=1508"