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To fulfill this assignment, you need to submit these files (all packed in one `.zip`

file) into the upload system:

- answers to the Assignment Questions`answers.txt`

- a script for data initialization, calling of the implemented functions and plotting of their results (for your convenience, will not be checked)`assignment_basics.m`

- a function implementing the matrix manipulation tasks specified in the section Matrix manipulation`matrix_manip.m`

and`compute_letter_mean.m`

- functions specified in the section Simple data task`compute_lr_histogram.m`

,`initial1_mean.png`

and`initial2_mean.png`

- images specified in the section Simple data task`initials_histograms.png`

** Use template of the assignment.** When preparing a zip file for the upload system, do not include any directories, the files have to be in the zip file root.

Beware of using `for`

loops! :)

Follow the instructions on https://www.fel.cvut.cz/user-info/matlab.html.

Statistical Pattern Recognition Toolbox contains variety of algorithms which you will implement by yourself during this course (you can use them as an inspiration) and contains also many support functions which we will use throughout the semester.

Download the latest version of the toolbox from https://gitlab.fel.cvut.cz/smidm/statistical-pattern-recognition-toolbox. Unpack the content of the `.zip`

file into your working directory (you should be able to find it next week!). To be able to use the toolbox, you have to set the path to it:

- Run Matlab
- Change the path to the directory where you unpacked the toolbox (use cd command or the path combo box at right top of the Matlab window)
- Running
`stprpath`

will set the path and do initialization of the toolbox

Now you can switch to any other directory and still use the functions from the STPR toolbox.

To verify the paths try to run `demo_ocr`

.

We will be using MATLAB programming language during the whole semester. It is necessary for you to be as familiar with it as possible, since the labs are time limited and the tasks are often challenging. It may happen that you spend more time looking for MATLAB syntax of some function instead of solving the problem itself.

For the case you are not too sure about your MATLAB skills, here are few useful links:

- Getting started with MATLAB. Nicely structured tutorial from Indiana University.
- MATLAB tutorial from Clarkson University.
- One A4 paper cheatsheet pdf.
- Image processing hints.
- How to make your code in MATLAB run fast BMVA Summer School: Matlab Programming Laboratory (Chapter 2, Vectorisation)

In most of the assignments there will be a set of simple questions. Answering the questions should help you to solve the assignment. All questions need to be answered correctly to get the points for the assignment.

Fill the correct answers to your `answers.txt`

file.

- MATLAB function name to generate matrix of zeros (without parentheses)
- Symbol(s) used in MATLAB for matrix product
- Symbol(s) used in MATLAB for element-wise product
- MATLAB way to generate vector [0 1 2 3 4 5 … 100] (check all correct answers):
- a) seq(0,100)
- b) xrange(100)
- c) 0:100
- d) <0,100>
- e) linspace(0,100,101)

- Select the second to last (předposlední) row of matrix A (check all correct answers):
- a) A(:,-1)
- b) A(-1,:)
- c) A(:,end-1)
- d) A(end-1,:)
- e) A[:,end-1]
- f) A[end-1,:]

Example `answers.txt`

(bogus answers only, fill the correct ones):

question1 : gen_zero_matrix question2 : 'x*' # enclose special characters in tick marks, also known as single quotation marks. question3 : '^' # enclose special characters in tick marks, also known as single quotation marks. question4 : [a,d,e] question5 : [b,f]

**In the first part of today’s assignment, you will start with some simple matrix manimulation tasks.
AVOID THE USE OF ANY LOOPS IN YOUR PROGRAM!**

Your goal is to complete a function `output = matrix_manip(A, B)`

, where `A`

and `B`

are input matrices and `output`

is a resulting structure containing results of operations described below.

To have some data to work with, use matrices `A`

and `B`

from `assignment_basics.m`

:

A = [16 2 3 13; 5 11 10 8; 9 7 6 12; 4 14 15 1] B = [ 3 4 9 4 3 6 6 2 3 4; 9 2 10 1 4 3 7 1 3 5]

Function should work on general input matrices, not only for above prescribed `A`

and `B`

matrices or matrices with the same dimensions. Dimensions of the input matrices will be always suitable for all of the following tasks.

- Find the transpose of the matrix
`A`

and return it in`output.A_transpose`

. Example result:>> output.A_transpose output.A_transpose = 16 5 9 4 2 11 7 14 3 10 6 15 13 8 12 1

**Hint:**Search documentation with**lookfor**command. - Select the third column of the matrix
`A`

and return it in`output.A_3rd_col`

.>> output.A_3rd_col output.A_3rd_col = 3 10 6 15

**Hint:**You need to use matrix indexing with colon`:`

character. - Select last two rows from last three columns of the matrix A and return the matrix in
`output.A_slice`

.>> output.A_slice output.A_slice = 7 6 12 14 15 1

- Find all positions in
`A`

greater then 3 and increment them by 1. Afterwards add column of ones to the matrix. Save the result to`output.A_gr_inc`

.>> output.A_gr_inc output.A_gr_inc = 17 2 3 14 1 6 12 11 9 1 10 8 7 13 1 5 15 16 1 1

**Hint:**Try`>`

operator on the whole matrix. Use`[]`

to construct the new matrix and**ones**function to create a vector with ones. - Create matrix
`C`

such that $C_{i,j} = \sum_{k=1}^n A\_gr\_inc_{i,k} \cdot (A\_gr\_inc^T)_{k,j}$ and store it in`output.C`

.>> output.C output.C = 499 286 390 178 286 383 351 396 390 351 383 296 178 396 296 508

**Hint:**No loops are needed, try it on a paper with a 2×2 matrix. - Compute $\sum_{c=1}^n c \cdot \sum_{r=1}^m A\_gr\_inc_{r,c}$:
>> output.A_weighted_col_sum output.A_weighted_col_sum = 391

**Hint:**You will need colon : special character to generate ranges, per element array multiplication`.*`

and`sum()`

function. - Subtract a vector $(4,6)^T$ from all columns of matrix
`B`

. Save the result to matrix`output.D`

.>> output.D output.D = -1 0 5 0 -1 2 2 -2 -1 0 3 -4 4 -5 -2 -3 1 -5 -3 -1

**Hint:**Use`repmat`

command to replicate a matrix . - Select all column vectors in the matrix
`D`

, which have greater euclidean distance than the average euclidean distance.>> output.D_select output.D_select = 0 5 0 -2 -4 4 -5 -5

**Hint:**You can find useful element-wise power of two`.^2`

and`find`

function that returns indices of non zero elements of a matrix. Euclidean length of a vector can be calculated with the`norm`

function, but for calculating length of all column vectors in a matrix you need to do it with your own code. No loops are needed!

**In this part of the assignment, you are supposed to work with a simple input data which contains images of letters. We will use similar data structures later on during the labs. Do the following:**

*Note:* Plotting and displaying of images should be implemented only in `assignment_basics.m`

.

- In the
`data_33rpz_cv01.mat`

data file, the following variables are stored:`images`

(3D array of 2000 10×10 grayscale images)`Alphabet`

(letters in the`images`

, not full alphabet is included)`labels`

(indexes of the`images`

into`Alphabet`

array).

- For a given letter, compute its mean image. This means taking all images in the dataset displaying that letter, and making pixel-wise mean. Use your name initials (if present in the dataset) and save them as
`initial1_mean.png`

and`initial2_mean.png`

(use any letter if any of your initials is not present in the dataset). You will probably encounter a problem with non-decimal numbers in the image matrix. You can avoid it by using the`uint8`

function before returning the mean image.- For the purpose of mean image calculation, complete the function:
letter_mean = compute_letter_mean(letter_char, Alphabet, images, labels)

where`letter_char`

is a character (e.g. 'A', 'B', 'C') representing the letter whose mean we want to compute,`Alphabet`

,`images`

and`labels`

are loaded from the provided data, and`letter_mean`

is the resulting mean image.

- Compute an image feature
*x*- a sole number characterizing an image. It is defined as:x = sum of pixel values in the left half of image - sum of pixel values in the right half of image

Then make a histogram of feature values of all images of a letter. Complete a function for the feature histogram computation:lr_histogram = compute_lr_histogram(letter_char, Alphabet, images, labels, num_bins)

where`letter_char`

is a character representing the letter whose feature histogram we want to compute,`Alphabet`

,`images`

and`labels`

are loaded from the provided data,`num_bins`

is the number of histogram bins and`lr_histogram`

is the resulting histogram (`num_bins`

long vector containing counts of items in the corresponding bins).- For reference the following histogram was computed for letter A with 10 bins:
>> compute_lr_histogram('A', Alphabet, images, labels, 10) ans = 1 1 3 6 12 27 24 20 5 1

**Hint**: use`hist`

function to compute the histogram.- Plot feature histograms of your initials into one figure to compare them and save the figure as
`initials_histograms.png`

.

courses/ae4b33rpz/labs/01_intro/start.txt · Last modified: 2017/10/06 13:53 by sochmjan