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MATLAB Programming Session

MATLAB Programming Session. Program development Planning the program Using Pseudo-code Selecting the right data structures General coding procedures Naming a function uniquely The importance of comments Optimizing for speed Vectorizing your code. Planning the Program.

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MATLAB Programming Session

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  1. MATLAB Programming Session • Program development • Planning the program • Using Pseudo-code • Selecting the right data structures • General coding procedures • Naming a function uniquely • The importance of comments • Optimizing for speed • Vectorizing your code EE465: Introduction to Digital Image Processing

  2. Planning the Program • Modular programming • Break the problem down into a series of smaller independent tasks • Implement each task as a separate function • When do I decide to implement a task as a new function? • Thumb rule: if this task will be performed more than once (e.g., I might want to use this function in other programs) EE465: Introduction to Digital Image Processing

  3. Using Pseudo-code For each pixel x {    - We take a window centered in x and size 2t+1 x 2t+1,  A(x,t).   - We take a window centered in x and size 2f+1 x 2f+1, W(x,f).    wmax=0;    For each pixel y in A(x,t)  &&   y different from x  {        - We compute the difference between W(x,f) and W(y,f),   d(x,y).         - We compute the weight from the distance d(x,y),  w(x,y).                 w(x,y) = exp(- d(x,y) / h);        - If   w(x,y) is bigger than wmax then      wmax = w(x,y);         - We compute the average                average + = w(x,y) * u(y);        - We carry the sum of the weights                totalweight + = w( x, y);} EE465: Introduction to Digital Image Processing

  4. Selecting the Right Data Structure • Usually data structure selection is easy, but sometimes it gets tricky • Scenario 1: handle data with varying length (e.g., variable length code) • Scenario 2: handle a large amount of data (due to memory constraint) • Scenario 3: running speed considerations (e.g., uint8 vs. double) EE465: Introduction to Digital Image Processing

  5. General Coding Practices • Use descriptive function and variable names to make your code easier to understand • Order subfunctions alphabetically in an M-file to make them easier to find • Precede each subfunction with a lock of help text describing what that subfunction does • Don’t extend lines of code beyond the 80th column • Make sure you have saved your function in a directory recognized by MATLAB path setting EE465: Introduction to Digital Image Processing

  6. Naming a Function Uniquely • It appears a simple rule; yet it is not easy to obey (we simply can’t remember them all) • >which -all <function name> • Doesn’t long function time take more time to type? • Yes, but you only need to do it once (you can recall the command using up/down arrow key) • TAB key can also help you finish the typing automatically EE465: Introduction to Digital Image Processing

  7. The Importance of Comments • I can never emphasize its importance enough, even though I often do a poor job on commenting my own MATLAB programs • It makes it easier for both you and others to maintain the program • Add comments generously, explain each major section and any smaller segments of code that are not obvious • Know how to remove or insert commenting % EE465: Introduction to Digital Image Processing

  8. Optimizing for Speed • How to vectorize your codes? • Avoid using for loop as much as you can • Functions Used in Vectorizing (see page 58 of programming_tips.pdf) • all: True if all elements of a vector are nonzero. • any: True if any element of a vector is a nonzero number or is logical 1 (TRUE). • Sum/prod/cumsum (more general than sum) • end: END can also serve as the last index in an indexing expression. • find (you will find it the most useful) • squeeze: remove singleton dimensions. • permute,/ipermute, reshape, sort, repmat, shiftdim, ndgrid EE465: Introduction to Digital Image Processing

  9. Vectorize Your Code • Even if loop is inevitable, try to reduce the number of iterations • Example: histogram calculation for a given grayscale image sized 512-by-512 • Scheme 1: loop over row and column indexes (512×512=262144 iterations) • Scheme 2: loop over intensity values 0-255 (256 iterations) • Scheme 2 is better! EE465: Introduction to Digital Image Processing

  10. MATLAB Programming Tip #1 • Use functions provided by MATLAB • Although we can implement any function from the scratch, the ones offered by MATLAB are optimized in terms of efficiency and robustness. • Examples: use bwdist in our implementation of finding skeleton EE465: Introduction to Digital Image Processing

  11. MATLAB Programming Tip #2 • Use as few loops as possible • Example: implementation of histogram calculation • Scheme 1: loop over every position in the image, i.e., i=1-M, j=1-N • Scheme 2: loop over every intensity value, i.e., 0-255 EE465: Introduction to Digital Image Processing

  12. MATLAB Programming Tip #3 • Parallelism is preferred by MATLAB • Example: in finding skeleton, we can find the local maximum in a parallel fashion, which is much more efficient than looping over every pixel % find the local maximum n=[0 1;-1 0;0 -1;1 0]; sk=dt>0; for i=1:4 sk=sk&(dt>=circshift(dt,n(i,:))); end EE465: Introduction to Digital Image Processing

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