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. EE465: Introduction to Digital Image Processing
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. EE465: Introduction to Digital Image Processing
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. EE465: Introduction to Digital Image Processing
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. EE465: Introduction to Digital Image Processing
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= iterations)
Scheme 2: loop over intensity values (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