EN161: Image Understanding
Fall 2007
Course Schedule

September:
Class 1
Class 2
 Class 3
Labs
  Wed. 9/5         Lecture 1
Introduction
  • Overviewing Image Understanding
  • Overview of  Course (Labs, Projects)
  • Necessary Background
  • Textbook
 Fri. 9/7         Lecture 2
Applications 		No Lab Due
Mon. 9/10         Lecture 3
Matlab & Hands-on Image Processing
  • Matlab Session on Lab 1
  • Basic Pointwise Image Operations
  • Thresholding
  • Rigid Transformations: Translation & Rotation
  • Arithmetic Operations on Images (+,-,*,/)
  • Masking (AND/OR) Windowing
  • Matlab Session Handout
 Wed. 9/12         Lecture 4
Pointwise Image Processing
  • Reading, Writing, "Displaying" Images
  • Quantization v.s. Sampling
  • Clipping, Thresholding
 Wed. 9/14
No Class
Lab 1, Due 9/17
  • Reading, Writing, Displaying Images
  • Thresholding, Bi-level Thresholding
  • Inverting, Brightness Modification, Effect of Reducing Quantization and Sampling
Mon. 9/17         Lecture 5
Basic Image Operations
  • Region of Interest (ROI)
  • Windowing, Rotation/Translation
  • 4- & 8- Connectivity 
  • Connected Components
  • Creating Synthetic Shapes and Images: Triangles, Rectangles, Polygons, Ellipses
Readings 		Wed. 9/19         Lecture 6
Basic Image operations
  • Component Labeling: Iterative & Recursive Algorithms
  • Components: Perimeter, Area, Compactness
  • Boundary Following
Readings
  • Connected Component Labelling, Haralick Ch. 2, 32-33, 36. 1, 2.
  • Segment Labelling, pp. 623-625. 1, 2.
 Fri. 9/21         Lecture 7
Basic Image operations
  • Region Filling
  • Contrast Enhancement 
  • Rank Leveling
  • Histogram Equalization 
Lab 2, Due 9/24
Image Region Processing
  • Image Acquisitions
  • Windowing, Rotation
  • Image Arthimetics, Logical Operations
  • Creating Synthetic Images
  • Connected Component Labeling
Mon. 9/24         Lecture 8
Local Image Operations / Filtering
  • Noise Removal
  • Filtering by Averaging
  • Notion of Discrete Kernel
Readings
 Wed. 9/26         Lecture 9
Filtering : Time Domain
  • Discrete and Continuous Covnolution
  • Block Averaging
  • Kernel (Mask) for Filtering
  • Filtering: Choice of Wieghts

 Fri. 9/28
No Class
Lab 3, Due 10/1
Image Enhancement and Boundary Following
  • Histogram Modification
  • Region Filling
  • Boundary Following
  • Block Averaging


October:
Lecture 1
Lecture 2
 Lecture 3
Labs
 Mon. 10/1         Lecture 10
Filtering: Frequency Domain
  • Fourier Transforms, frequency domain (F.D.)
  • Gabor Approach
  • Ideal Low-Pass Filter
  • Convolution as Product in the F.D.
  • Low-Pass Filter in the Time Domain
  • Block Averaging v.s. Low-Pass Filtering
  • FFT
 Wed. 10/3         Lecture 11
Filtering
  • Gaussian Kernels
    • Separability
    • Implementation: Pascal Triangle
  • Heat Equation
  • Nonlinear Filters: Median Filter
  • Matched Filtering
  • Sampling and Interpolation

 Fri. 10/5
No Class
Lab 4, Due 10/10
Filtering/Template Matching
  • Downsampling and Upsampling
  • Image Interpolation
  • Filtering/Convolution
  • Noise Removal
  • Template Matching
Mon. 10/8
No Class
Columbus Day
 Wed. 10/10         Lecture 12
Sampling and Interpolation
  • Nyquist Rate
  • Downsampling, Upsampling
  • Sinc, Bilinear & Bicubic Interpolations
  • Geometric Warping, Polynomial Models

Readings

 Wed. 10/12         Lecture 13
Edge Detection
  • Gaussian and Laplacian Pyramids
  • Laplacian as a Bandpass Filters
  • Mach Band Effect
  • Edge Detection: Robert's, Sobel, Prewitt
Readings
Lab 5, Due 10/15
Edge Detection and Image Warping  
  • Edge Detection by Kernel Convolution (Prewitt, Sobel, Laplacian)
  • Gradient Edge Detector
  • Image Warping
Mon. 10/15         Lecture 14
Edge Detection
  • Image Gradient: 1D & 2D
  • Numerical Differentiation
  • Difference of Gaussians (DoG)
  • Canny
  • Localization: Non-Maximum Supression
  • Hysteresis
  • Soures of Edges: Texture, Reflectance, Highlight, Shadow, etc.




 Wed. 10/17         Lecture 15
Nonlinear Edge Detection
Fri. 10/19         Lecture 16
Edge Linking
  • Edge Linking
  • Perceptual Grouping
  • Gestalt Cues: Proximity, Good Continuation, Symmetry, etc.

Readings
Lab 6, Due 10/22
Edge Detection & Linking, Corner Detection
  • Edge detection: Canny, Cubic Image Approximation
  • Dual Threshold Edge Linking
  • Boundary Following
  • Corner Detection
Mon. 10/22         Lecture 17
Readings




 Wed. 10/24         Lecture 18

 Fri. 10/26         Lecture 19


 No Lab Due
Mon. 10/29         Lecture 20






Wed. 10/31         Lecture 21
   






Tue 10/30 1-3pm

Initial Project Presentation
10 minutes for each project

November:

Lecture 1
Lecture 2
 Lecture 3
Labs
 
Fri. 11/2         Lecture 22
Lab 7, Due 11/5
Segmentation
  • Split and Merge
  • Boundary Melting
  • Seeded Region Growing (!!!!!)
  • Watershed Segmentation (*)
Mon. 11/5         Lecture 23
Active Contour

Readings



 Wed. 11/7         Lecture 24
 Wed. 11/9         Lecture 25
Lab 8, Due 11/12
Active Contours
  • Snake (GVF*)
  • Curve Evolution (!!!!!)
  • Livewire (*)
Mon. 11/12         Lecture 26
Level Set

Readings


Wed. 11/14         Lecture 27
Bubbles

Readings
Fri. 11/16         Lecture 28
Mon. 11/19         Lecture 29
Image Segmentation
Readings

 Wed. 11/21
No Class

 Thanksgiving
 Fri. 11/23
No Class
Thanksgiving
Mon. 11/26 2-4pm       


Mid Project Presentation I
 Wed. 11/28         Lecture 30






Fri. 11/30         Lecture 31

 Tue 11/27 1-3pm
Mid Project Presentation II

December:

Lecture 1
Lecture 2
 Lecture 3
Labs
 Mon. 12/3         Lecture 32


 Wed. 12/5         Lecture 33

Readings

 Fri. 12/7         Lecture 34
Lab 9, Due 12/3
Tracking
  • Object Tracking
  • Object Prediction
  • Optical Flow
Mon. 12/10         Lecture 35



Wed. 12/12         Lecture 36
 Wed. 12/14Wed. 12/14         Lecture 37

Mon. 12/17         Lecture 38


Wed. 12/19 2-3:30pm


Final Project Presentation II
Tue 12/18 2-3:30pm


Final Project Presentation I