Difference between revisions of "Applied Computer Vision Lecture Schedule"
From David Vernon's Wiki
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| 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. |
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| 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 |
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Revision as of 18:47, 20 August 2017
|CARNEGIE MELLON UNIVERSITY AFRICA|
| Date | Lecture | Topic | Material covered | Reading | Assignments |
|---|---|---|---|---|---|
| Mon. 28 Aug. | 1 | Overview | Human and computer vision | Lecture 1 Slides. Szeliski 2010, Sections 1.1 and 1.2. Kragic and Vincze, 2010. | |
| Wed. 30 Aug. | 2 | Software tools | OpenCV, Software development tools for course work | Lecture 2 Slides. | |
| Mon. 4 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. | ||
| Wed. 6 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 | ||
| 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 | Image features | Harris and Difference of Gaussian interest point operators | ||
| Wed. 4 Oct. | 12 | Image features | SIFT feature descriptor | ||
| Mon. 9 Oct. | 13 | Object recognition | Template matching; normalized cross-correlation; chamfer matching | ||
| Wed. 11 Oct. | 14 | Object recognition | 2D shape features; statistical pattern recognition | ||
| Mon. 16 Oct. | 15 | Object recognition | Hough transform for parametric curves: lines, circles, and ellipses | ||
| Wed. 18 Oct. | 16 | Object recognition | Generalized Hough transform; extension to codeword features | ||
| Mon. 23 Oct. | 17 | Object recognition | Colour histogram matching and back-projection | ||
| Wed. 25 Oct. | 18 | Object recognition | Haar features and boosted classifiers | ||
| Mon. 30 Oct. | 19 | Object recognition | Histogram of Oriented Gradients (HOG) feature descriptor | ||
| Wed. 1 Nov. | 20 | Video image processing | Background subtraction and object tracking | ||
| Mon. 6 Nov. | 21 | 3D vision | Homogeneous transformations. Perspective transformation. Camera model and inverse perspective transformation | ||
| Wed. 8 Nov. | 22 | Stereo vision. | Stereo correspondence, Epipolar geometry | ||
| Mon. 13 Nov. | 23 | Optical flow | |||
| Wed. 15 Nov. | 24 | Visual attention | Saliency, Bottom-up and top-down attention | ||
| Mon. 20 Nov. | 25 | Clustering, grouping, and segmentation | Gestalt principles. Clustering algorithms | ||
| Wed. 22 Nov. | 26 | Object recognition in 3D | Object detection, object recognition, object categorisation | ||
| Mon. 27 Nov. | 27 | Affordances | |||
| Wed. 29 Nov. | 28 | Computer vision and machine learning | |||
| Mon. 4 Dec. | 29 | Computer vision and machine learning | |||
| Wed. 6 Dec. | 30 | Computer vision and machine learning |
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