EECS 490: Fall 2006

Lecture Notes from Fall 2004

• Chapter 3 (PDF, 3.76 MB)
• Chapter 4 (PDF, 2.47 MB)
• Chapter 5 (PDF, 3.22 MB)
• Chapter 5 Updated 10/23 (PDF, 3.42 MB)
• Chapter 6 B/W (PDF, 2.40 MB)
• Chapter 6 Gray (PDF, 3.54 MB)
• Chapter 6 Color (PDF, 8.27 MB)
• Chapter 7 (PDF, 2.85 MB)
• Chapter 9 (PDF, 1.78 MB)
• Chapter 10 (PDF, 1.40 MB)
• Chapter 13 Castleman (PDF, 860 kB)
• Linear Algebra & Eigenvectors from various sources (PDF, 2.3 MB)

Lectures - Fall 2006.

• Lecture 1 8/29 - course outline; overview of image processing - examples of digital image processing. .
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• Lecture 2 8/31 - continuation of image processing overview - spatial and frequency domain processing. Reading: GW Chapter 1 and Chapter 2.
  
• Lecture 3 9/5 - Point to point image transforms - , contrast, brightness, histogram equalization, histogram specification. Reading: GW 3.1-3.5
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• Lecture 4 9/7 - Spatial domain image processing: templates, basic filtering operations. Reading: GW 3.1-3.5
  
• Lecture 5 9/12 - Spatial domain image processing: edge operators, first and second derivative operators. Reading GW 3.5-3.6
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• Lecture 6 9/14 - Continuation of edge operators: correlation and template operators. Intro. to image geometry and warping.. Reading: GW 3.7 and lecture notes.
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• Lecture 7 9/19 - Geometric transformations, forward and reverse mapping, gray level transformation, polynomial warping. Finish up Chapter 3 of GW.. Lecture notes have been corrected along the lines of the discussion in class.
  
• Lecture 8 9/21 - Special guest lecture by Dr. Simon Melikian on high-speed methods for finding lines in images.
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• Lecture 9 9/26 - Cameras, what they do, how they work (and don't work) including perspective, aberrations and lenses.
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• Lecture 10 9/28 - Affine transformations, camera geometry, camera transformations.
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• Lecture 11 10/3 - Camera calibration, pseudoinverse and other methods of solving overdetermined systems of equations, camera coordinate frames single camera and stereo camera calibration. Introduction to color: the color spectrum, tristimulus values, additive (primary) and subtractive (secondary) colors, CIE chromaticity diagram,
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• Lecture 12 10/5 - The RGB color cube, "safe" colors, the HSI color space, point color image processing, pseudocolor, false color images, color transformations, intensity transformation in different color spaces, neighborhood operations in color spaces.
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• Lecture 13 10/10 - Intensity transformations in different color spaces, complementary colors, color calibration and device independent color models, color correction, spatial operations in color space - smoothing and sharpening, color segmentation.
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• Lecture 14 10/12 - Color segmentation, color edges, color derivative operators, noise in color.
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• Lecture 15 10/17 - Segmentation- similarity, connectivity, blob coloring, top down-bottom up algorithms; Canny edge operator, edge relaxation, introduction to Fourier transforms: 2-D Fourier transforms, basic properties of 2-D Fourier transforms - translation, rotation, superposition.
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• Lecture 16 10/19 - MATLAB: 'edge' operator, scaling, display, fft2 and iff2, shifting and centering; filtering in the spatial frequency domain - low-pass and high-pass filters, Gaussian filtering, Laplacian in the frequency domain, units in the spatial domain and the frequency domain, shifting (convolution) property of Fourier transforms
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• Lecture 17 10/26 - Ideal low-pass (ILPF) and high-pass filters (IDHP), Gaussian and Butterworth filters, wrinkle removal, relationship between high-pass and low-pass filters, high-pass edge detectors, Laplacian in the frequency domain, high-boost.
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• Lecture 18 11/02 - Holomorphic filters, wraparound (aliasing) and padding, correlation, invariance.
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• Lecture 19 11/07 - Image degradation, spatial noise and noise distributions, periodic noise, frequency domain noise analysis, mean filters, ordered list filters, alpha trimming, adaptive filters, reject filters, .
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• Lecture 20 11/09 - Null filters, noise characteristics, estimation, inverse filters, degradation models for turbulence and motion, minimum square estimation and the Wiener filter.
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• Lecture 21 11/14 - Inverse filters, Statistics: expectation, moments, mean, variance.
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• Lecture 22 11/16 - Statistics (cont.): covariance, correlation, multiple variables, covariance matrix. Optimal estimation: single and two observation estimation. Kalman filters.
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• Lecture 23 11/21 - Kalman filtering for tracking vehicles. Basic image morphology: erosion, dilation, opening and closing.
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• Lecture 24 11/28 - Image morphology: hit or miss algorithm, borders extraction, thinning and filling, convex hulls, gray scale morphology.
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• Lecture 25 11/30 - Image segmentation: mask response, finding edges, Hough transform, graph searching, global and local thresholding, adaptive (automatic) thresholding, color segmentation and region growing .
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• Lecture 26 12/5 - Image segmentation: thresholding & region growing, watershed algorithm, accumulatiuve difference images, moving objects.
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• Lecture 27 12/7 - Image representation: chain codes, thinning & skeletons, Fourier boundary descriptors, topology & Euler number, texture, moments, eigenvectors & principle component analysis, correlation, pattern recognition principles & decision rules.
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