Applied Computer Vision Lecture Schedule
From David Vernon's Wiki
|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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