Difference between revisions of "Applied Computer Vision Lecture Schedule"
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− | | | + | | Tue. 29 Aug. |
| 1 | | 1 | ||
| Overview | | Overview | ||
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− | | | + | | Thur. 31 Aug. |
| 2 | | 2 | ||
| Software tools | | Software tools | ||
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− | | | + | | 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. |
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− | | | + | | 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 |
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− | | | + | | Tue. 12 Sept. |
| 5 | | 5 | ||
| Image processing | | Image processing | ||
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− | | | + | | Thur. 14 Sept. |
| 6 | | 6 | ||
| Image processing | | Image processing | ||
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− | | | + | | Tue. 19 Sept. |
| 7 | | 7 | ||
| Image processing | | Image processing | ||
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− | | | + | | Thur. 21 Sept. |
| 8 | | 8 | ||
|Segmentation | |Segmentation | ||
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− | | | + | | Tue. 26 Sept. |
| 9 | | 9 | ||
| Segmentation | | Segmentation | ||
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− | | | + | | Thur. 28 Feb. |
| 10 | | 10 | ||
| Segmentation | | Segmentation | ||
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− | | | + | | Tue. 3 Oct. |
| 11 | | 11 | ||
− | | | + | | Image features |
− | | | + | | Harris and Difference of Gaussian interest point operators |
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|- style="vertical-align: top;" | |- style="vertical-align: top;" | ||
− | | | + | | Thur. 5 Oct. |
| 12 | | 12 | ||
− | | | + | | Image features |
− | | | + | | SIFT feature descriptor |
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|- style="vertical-align: top;" | |- style="vertical-align: top;" | ||
− | | | + | | Tue. 10 Oct. |
| 13 | | 13 | ||
− | | | + | | Object recognition |
− | | | + | | Template matching; normalized cross-correlation; chamfer matching |
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|- style="vertical-align: top;" | |- style="vertical-align: top;" | ||
− | | | + | | Thur. 12 Oct. |
| 14 | | 14 | ||
− | | | + | | Object recognition |
− | | | + | | 2D shape features; statistical pattern recognition |
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|- style="vertical-align: top;" | |- style="vertical-align: top;" | ||
− | | | + | | Tue. 17 Oct. |
| 15 | | 15 | ||
− | | | + | | Object recognition |
− | | | + | | Hough transform for parametric curves: lines, circles, and ellipses |
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|- style="vertical-align: top;" | |- style="vertical-align: top;" | ||
− | | | + | | Thur. 19 Oct. |
| 16 | | 16 | ||
− | | | + | | Object recognition |
− | | | + | | Generalized Hough transform; extension to codeword features |
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|- style="vertical-align: top;" | |- style="vertical-align: top;" | ||
− | | | + | | Tue. 24 Oct. |
| 17 | | 17 | ||
− | | | + | | Object recognition |
− | | | + | | Colour histogram matching and back-projection |
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|- style="vertical-align: top;" | |- style="vertical-align: top;" | ||
− | | | + | | Thur. 26 Oct. |
| 18 | | 18 | ||
− | | | + | | Object recognition |
− | | | + | | Haar features and boosted classifiers |
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|- style="vertical-align: top;" | |- style="vertical-align: top;" | ||
− | | | + | | Tue. 31 Oct. |
| 19 | | 19 | ||
− | | | + | | Object recognition |
− | | | + | | Histogram of Oriented Gradients (HOG) feature descriptor |
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|- style="vertical-align: top;" | |- style="vertical-align: top;" | ||
− | | | + | | Thur. 2 Nov. |
| 20 | | 20 | ||
− | | | + | | Video image processing |
− | | | + | | Background subtraction and object tracking |
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|- style="vertical-align: top;" | |- style="vertical-align: top;" | ||
− | | | + | | Tue. 7 Nov. |
| 21 | | 21 | ||
− | | | + | | 3D vision |
− | | | + | | Homogeneous transformations. Perspective transformation. Camera model and inverse perspective transformation |
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|- style="vertical-align: top;" | |- style="vertical-align: top;" | ||
− | | | + | | Thur. 9 Nov. |
| 22 | | 22 | ||
− | | | + | | Stereo vision. |
− | | | + | | Stereo correspondence, Epipolar geometry |
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|- style="vertical-align: top;" | |- style="vertical-align: top;" | ||
− | | | + | | Tue. 14 Nov. |
| 23 | | 23 | ||
− | | | + | | Optical flow |
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|- style="vertical-align: top;" | |- style="vertical-align: top;" | ||
− | | | + | | Thur. 16 Nov. |
| 24 | | 24 | ||
− | | | + | | Visual attention |
− | | | + | | Saliency, Bottom-up and top-down attention |
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|- style="vertical-align: top;" | |- style="vertical-align: top;" | ||
− | | | + | | Tue. 21 Nov. |
| 25 | | 25 | ||
− | | | + | | Clustering, grouping, and segmentation |
− | | | + | | Gestalt principles. Clustering algorithms |
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|- style="vertical-align: top;" | |- style="vertical-align: top;" | ||
− | | | + | | Thur. 23 Nov. |
| 26 | | 26 | ||
− | | | + | | Object recognition in 3D |
− | | | + | | Object detection, object recognition, object categorisation |
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|- style="vertical-align: top;" | |- style="vertical-align: top;" | ||
− | | | + | | Tue. 28 Nov. |
| 27 | | 27 | ||
− | | | + | | Affordances |
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|- style="vertical-align: top;" | |- style="vertical-align: top;" | ||
− | | | + | | Thur. 30 Nov. |
| 28 | | 28 | ||
− | | | + | | Computer vision and machine learning |
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|- style="vertical-align: top;" | |- style="vertical-align: top;" | ||
− | | | + | | Tue. 5 Dec. |
| 29 | | 29 | ||
− | | | + | | Computer vision and machine learning |
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|- style="vertical-align: top;" | |- style="vertical-align: top;" | ||
− | | | + | | Thur. 7 Dec. |
| 30 | | 30 | ||
− | | | + | | Computer vision and machine learning |
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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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