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  COMPUTER ENGINEERING  EXAM
                 TOPICS FROM SOFTWARE ENGINEERING
                  SUGGESTED COURSE:  ESE 344 SOFTWARE TECHNIQUES FOR ENGINEERS

MATERIAL INCLUDED FOR THE  Ph. D. QUALIFYING  EXAM:

A. References

      1. Digital Image Processing,
                  R. C. Gonzalez and R. E. Woods, Prentice-Hall, 2002.

      2. Digital Picture Processing, Vol. 1,
             A. Rosenfeld and A. C. Kak, Academic Press,1982. 

B. Chapters and Sections included from the references :

1.  Digital image fundamentals
     Ch. 1 and Ch. 2 in Gonzalez and Woods (new book, 2ed)
      Complete Chs. 1 and 2.

2. Image Enhancement: Spatial domain techniques
     Ch. 3 in Gonzalez and Woods (new book, 2ed).
            Complete Ch. 3.

3. Image Enhancement:  Fourier domain techniques
      Ch. 2 in Rosenfeld and Kak, Vol I:
              Only Sections 2.1 to 2.2.1 (pages  10 to 27).
      Ch. 4 in Gonzalez and Woods (new book, 2ed)
                Complete Ch. 4.

 4. Digitization
         In Ch. 4 of  Rosenfeld and Kak, Vol I:
            Sections 4.0 to 4.1.3
            Sections 4.1.5 to 4.2.1,
            Section 4.2.3
            Sections 4.3 to 4.3.2

5. Image Restoration
      Gonzalez and Woods (new book, 2ed)
       Following pages in Chapter 5:
            pages 220 to 222,
            pages 254 to 270. 

6. Color
    Gonzalez and Woods (new book, 2ed)
     Following pages in Ch. 6
       pages 282 to 302. 

7. Image Compression
    Gonzalez and Woods (new book, 2ed)
     Following pages from Ch. 8
       pages 409 to 424,
       pages 440 to 444,
       pages 467 to 485,
       pages 498 to 506, 

8. Image Reconstruction
    Rosenfeld and Kak, Vol. 1, Ch. 8,
    Following pages are included
       pages 353 to 357,
       pages 366 to 370.

C. Detailed list of topics included above:
 
1.0  Introduction
   1.1 Image formation in cameras and human eye
   1.2 Image formation in other devices (X-ray, tomography, MRI etc.)
   1.3 Continuous images are sampled and quantized to get digital images
   1.4 Digital images are two-dimensional signal data
   1.5 Examples of  digital image processing operations
   1.6 Areas of applications (industrial, medical, defence, photo-editing)
   1.7 Related areas (computer vision, graphics, image analysis, pattern recognition)

2.0 Digital Image Fundamentals
     2.1 Human visiual system (HVS)
            2.1.1 Structure of HVS
            2.1.2 perception of color, brightness, and contrast
    2.2  Digital Image Sensors
            2.2.1 Cameras,  CCDs,  scanners,  medical sensors (CAT)
    2.3 Image Formation
           2.3.1 Illuminatuion and reflection
           2.3.2 Iimage formation in a pin-hole camera
                    2.3.2.1 Perspective projection relations
          2.3.3. Image formation in a convex lens camera
                     2.3.3.1 photometric and geometric information in a 3D scene
                     2.3.3.1 .radiance, irradiance, and luminance
        2.3.4 Image Sampling and Quantization
                     2.3.4.1 spatial and  gray level resolution
    2.4 Representation of digital images as 2D arrays
           2.4.1 pixels, gray levels, their trade-off for different types of images
           2.4.2 zoom-in/zoom-out using bilinear interpolation
           2.4.3 Basic relationships between pixels
           2.4.4  neighbors, adjacency, connectivity, regions, boundaries

3.0 Image Enhancement: Spatial domain techniques
       3.1 gray level transformation
              3.1.1 negative, log, power, gamma, linear,  gray-level slicing, bit-plane slicing
     3.2  Histogram transformation
             3.2.1 Equalization
                      3.2.1.1 Theory in thencontinuous domain
                      3.2.1.2 Discrete domain algorithm
            3.2.2 Specification
                      3.2.2.1 theory in thencontinuous domain
                      3.2.2.2 discrete domain algorithm
           3.2.3 Local enhancement
           3.2.4 Mean, std, and median computaiton using histograms
       3.3 Arithmetic/logic operations
              3.3.1 add, subtract, averaging, and, or
       3.4 Filtering through convolution
              3.4.1 smoothing, noise reduction
               3.4.2 derivatives, sharpening,  deblurring
      3.5  Order-statistic filters
              (median, min, max, k-nearest neighbor mean)

4. Image Enhancement: Fourier domain techniques
        4.1  One-dimensional  Fourier Transform
                 4.1.1 continuous domain
                 4.1.2  discrete domain
        4.2 Two-dimensiona l  Fourier Transform
                 4.2.2  continuous domain
                 4.2.3  discrete domain
        4.3 Filtering
                4.3.1 Computational algorithm
                 4.3.2 Relation to spatial domain filtering
                            4.3.2.1 Convolution theorem
                4.3.3 Smoothing filters
                           4.3.3.1 ideal low-pass, Butterworth,   Gaussian
                4.3.4 Sharpening filters
                          4.3.3.1 ideal low-pass, Butterworth,  Gaussian, derivative
                4.4 Homomorphic filtering
               4.5 Properties of  Fourier Transform
               4.6 Computational implementation

10. Sampling and quantization
      (Reference Ch. 4 of Rosenfeld and Kak, leave out topics related to random field models of images)
       10. 1 Sampling using an array of points
       10.2 Sampling theorem
       10.3 Aliasing, sampling using orthonormal functions
       10.4 Optimal quantization

5.0  Image Restoration
       5.1 Model of image degradation/restoration
        5.2  Noise models (uniform and Gaussian)
        5.3 Linear shift-invariant degradations
        5.4 OTF, MTF, PTF,
        5.5 PSF, LSF, ESF
        5.6 Estimation of  degradation OTF/PSF
        5.7 Motion deblurring
        5.8 Inverse filtering
        5.9 Weiner Filtering
        5.10 Geometric transformations

6.0  Color Image Processing
        6.1 light spectrum, visible wavelengths
         6.2 Color models
                6.2.1 RGB
                6.2.2 CMYK
                6.2.3 HSI
                           6.3.1 Transformation of RGB to HSI and back
                6.2.4 CIE Lab
                6.2.5 Color correction (white balancing, tone, etc)
          6.3 Color image processing (Histogram specification and filtering)

7.0 Image Compression
       7.1 Fundamentals
              7.1.1 Coding redundancy
              7.1.2 Interoixel redundancy
              7.1.3 Psychovisual redundancy
              7.1.4 Fidelity criteria
      7.2 Image compression models
             7.2.1 Source encoder and decoder
             7.2.2  Channel encoder and decoder
             7.2.3 Measuring information
      7.3 Error-free compression
             7.3.1 Huffman, B2, Binary-shift, Huffman-shift, coding
             7.3.2 Arithmetic, and LZW coding
      7.4 Lossy compression
              7.4.1 Transform coding model
               7.4.2 Discrete Cosine Transform
             7.4.3 Bit allocation based on zonal and threshold coding
               7.4.4 An Example: Sequential baseline JPEG
               7.4.5 JPEG 2000 and MPEG basics

8.0 Image reconstruction
       8.1 Radon transform
       8.2 Examples of projection data
            (x-ray, emission, and ultra sound tomography)
       8.3 Fourier slice theorem
       8.4 Filtered backprojection
       8.5 Algebraic reconstruction techniques

Updated: Feb. 26, 2003.

Reference Book:
      Robert L. Kruse,  Alexander Ryba
      Datastructures and Program Design in C++
      Prentice-Hall, 1999.
      ISBN 0-13-768995-0

Topics from ESE 344 with Section numbers from the
Reference book:

       Programming Principles  :  Ch. 1
       Arrays and Stacks:   Ch. 2
       Queues:  Ch. 3,  Sections 4.0 to 4.4
       Recursion: Sections 5.0 to 5.2
       Lists: Sections 6.0 to 6.3
       Searching: Sections 7.0 to 7.3
       Sorting: Sections 8.1, 8.2, and 8.8.
       Hashing: Section 9.6
       Binary Trees: Sections 10.0 to 10.3
       Trees: Sections 11.0 to 11.1
       Graphs: Sections 12.0 to 12.3