Difference between revisions of "Applied Computer Vision Lecture Schedule"

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<div style="float:right;">__TOC__</div>
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<span style="color:#AB0000" ><span style="font-size:10px">|</span></span><span style="color:#000000" ><span style="font-size:10px"><span style="text-decoration:underline">CARNEGIE MELLON UNIVERSITY AFRICA</span></span></span><span style="color:#AB0000" ><span style="font-size:10px">|</span></span>
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<small>
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{| class="wikitable"
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! scope="col" style="width: 7%;" | Date                          <!-- 7% -->
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! scope="col" style="width: 3%;" | Lecture                      <!-- 3% -->
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! scope="col" style="width: 20%;" | Topic                        <!-- 14% -->
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! scope="col" style="width: 30%;" | Material covered    <!-- 50% -->
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! scope="col" style="width: 20%;" | Reading                    <!-- 13% -->
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! scope="col" style="width: 20%;" | Assignments            <!-- 13% -->
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|- style="vertical-align: top;"
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| Tue. 29 Aug.
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| 1
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| Overview
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| Human and computer vision
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|[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]. -->
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|
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|- style="vertical-align: top;"
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| Thur. 31 Aug.
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| 2
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| Software tools
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|  OpenCV, Software development tools for course work 
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| [http://www.vernon.eu/ACV/ACV_02.pdf Lecture 2 Slides].
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|
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|- style="vertical-align: top;"
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| Tue. 5 Sept.
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| 3
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| Optics, sensors, and image formation
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| Illumination, projections, lenses, Gauss lens equation, field of view, depth of field, CMOS and CCD sensors, colour sensors, noise, resolution.
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| 
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|
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|- style="vertical-align: top;"
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| Thur. 7 Sept.
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| 4
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| Image acquisition and image representation
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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
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|
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|
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|- style="vertical-align: top;"
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| Tue. 12 Sept.
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| 5
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| Image processing
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| Point & neighbourhood operations, image filtering, convolution, Fourier transform 
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|
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|
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|- style="vertical-align: top;"
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| Thur. 14 Sept.
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| 6
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| Image processing
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| Morphological operations
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|
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|
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|- style="vertical-align: top;"
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| Tue. 19 Sept.
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| 7
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| Image processing
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| Geometric operations
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|
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|
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|- style="vertical-align: top;"
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| Thur. 21 Sept.
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| 8
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|Segmentation
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|Region-based approaches, binary thresholding, connected component analysis 
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|
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|
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|- style="vertical-align: top;"
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| Tue. 26 Sept.
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| 9
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| Segmentation
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| Edge detection
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|
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|
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|- style="vertical-align: top;"
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| Thur. 28 Feb.
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| 10
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| Segmentation
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| Colour-based approaches; k-means clustering 
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| 
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|
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|- style="vertical-align: top;"
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| Tue. 3 Oct.
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| 11
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| Image features 
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| Harris and Difference of Gaussian interest point operators
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| 
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|
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|- style="vertical-align: top;"
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| Thur. 5 Oct.
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| 12
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|  Image features
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|  SIFT feature descriptor
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| 
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|
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|- style="vertical-align: top;"
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| Tue. 10 Oct.
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| 13
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| Object recognition
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| Template matching; normalized cross-correlation; chamfer matching 
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| 
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|
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|- style="vertical-align: top;"
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| Thur. 12 Oct.
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| 14
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| Object recognition
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| 2D shape features; statistical pattern recognition 
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|
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| 
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|- style="vertical-align: top;"
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| Tue. 17 Oct.
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| 15
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|  Object recognition
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| Hough transform for parametric curves: lines, circles, and ellipses 
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|
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| 
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|- style="vertical-align: top;"
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| Thur. 19 Oct.
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| 16
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|  Object recognition
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| Generalized Hough transform; extension to codeword features 
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| 
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|
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|- style="vertical-align: top;"
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| Tue. 24 Oct.
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| 17
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| Object recognition
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| Colour histogram matching and back-projection
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| 
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| 
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|- style="vertical-align: top;"
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| Thur. 26 Oct.
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| 18
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| Object recognition
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| Haar features and boosted classifiers 
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| 
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|
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|- style="vertical-align: top;"
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| Tue. 31 Oct.
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| 19
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|  Object recognition
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|  Histogram of Oriented Gradients (HOG) feature descriptor 
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|
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| 
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|- style="vertical-align: top;"
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| Thur. 2 Nov.
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| 20
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|  Video image processing
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|  Background subtraction and object tracking 
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|   
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|
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|- style="vertical-align: top;"
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| Tue. 7 Nov.
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| 21
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|  3D vision
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| Homogeneous transformations. Perspective transformation. Camera model and inverse perspective transformation 
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|
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| 
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|- style="vertical-align: top;"
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| Thur. 9 Nov.
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| 22
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| Stereo vision.
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| Stereo correspondence, Epipolar geometry 
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| 
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| 
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|- style="vertical-align: top;"
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| Tue. 14 Nov.
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| 23
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|  Optical flow 
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| 
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| 
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|
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|- style="vertical-align: top;"
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| Thur. 16 Nov.
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| 24
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|  Visual attention
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|  Saliency, Bottom-up and top-down attention 
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| 
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| 
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|- style="vertical-align: top;"
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| Tue. 21 Nov.
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| 25
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|  Clustering, grouping, and segmentation
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| Gestalt principles. Clustering algorithms 
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| 
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| 
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|- style="vertical-align: top;"
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| Thur. 23 Nov.
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| 26
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|  Object recognition in 3D
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| Object detection, object recognition, object categorisation 
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| 
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|
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|- style="vertical-align: top;"
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| Tue. 28 Nov.
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| 27
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|  Affordances 
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| 
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|
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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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|
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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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|
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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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| 
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|}
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</small>
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----
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Back to [http://www.vernon.eu/ACV.htm Applied Computer Vision]

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


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