Contact Information:
Course instructor:
Dr.K. Palaniappan
Dept. of CS
329 Engineering Building West
Univ. of Missouri-Columbia
Columbia, MO 65211
palaniappank@missouri.edu
(573) 884-9266, (573) 882-6265

Course TA:
Anoop Haridas
Dept. of CS
347 Engineering Building West
Univ. of Missouri-Columbia
Columbia, MO 65211
ahkrc@mizzou.edu
Office Hours Wed: 1-3pm


Catalog Description

Fundamentals of digital image processing hardware and software including digital image acquisition, image display, image enhancement, image transforms and segmentation.

Goals

To develop theoretic and algorithmic principles behind the acquisition, display, manipulation and processing of digital images. Practical laboratory tutorials and assignments will explore the methods used to digitize, transfer, display, organize, process and compare digital images and image sequences. Image processing software such as the IISS, Khoros and SGI ImageVision Library will be used to implement algorithms for and understand image file formats, representation and conversion methods, contrast enhancement by histogram manipulation, image statistics, image smoothing and sharpening using filtering algorithms, geometric transforms, frequency domain transform methods, inverse filtering, segmentation using region growing and boundary detection, etc.

Syllabus

  1. Introduction to image and video processing, analysis and understanding with applications to computer vision and pattern recognition
  2. Image perception, sampling and quantization
  3. Image representation, modeling and display
  4. Image enhancement: spatial domain vs frequency domain
  5. Image processing software
  6. Enhancement via point operations - contrast stretching, colormap look-up tables, clipping, thresholding, negation, gray-level slicing, bit-plane slicing, range compression, algebraic operators
  7. Enhancement via histogram modeling - histogram modification, histogram equalization, histogram specification
  8. Processing via algebraic operations - input and output histograms, sums and differences of images, averaging noisy images, background removal, change or motion detection
  9. Processing via spatial operations - weighted local area smoothing or averaging, derivative filters (Roberts, Prewitt, Sobel, Kirsch, Laplacian), sharpening filters, unsharp masking, median filtering, gray level interpolation, spatial transformations, applications of geometric operations (cameral calibration, image registration, map projection, morphing)
  10. Linear systems theory and 2-D convolution for image processing
  11. Frequency domain approach and the 2-D Fourier transform
  12. Discrete Fourier Transform and the FFT
  13. Discrete image transforms - Discrete Cosine Transform, Eigenvector-based Transforms
  14. Image restoration, inverse filtering and deconvolution
  15. Hough Transform
  16. Image segmentation via optimal histogram thresholding methods, connected components
  17. Image and region statistics
  18. Mathematical morphology
  19. Texture analysis
  20. Color models and multispectral images

Prerequisites (preferred)

Knowledge of these topics will be very helpful:

Required Text

Other Texts and Sources

Additional References