CSE 4360 / 5364 – Autonomous Robots Homework 2: Computer Vision
CSE 4360 / 5364 – Autonomous Robots
Homework 2- Fall 2020
Due Date: Dec. 1 2020 Problems marked with ∗ are mandatory only for students of CSE 5364 but will be graded for extra
credit for students of CSE 4360.
Edge Detection, Template Matching, and Blob Coloring
Basic feature extraction and region growing techniques are important steps in the first stages of im-
age processing. In this homework assignment you are going to implement edge detection, template
matching, and blob coloring as basic techniques.
Programming will follow the same procedure as in the last assignment. You are provided with a C
library containing facilities to display images which will link with the file process image.c. Your code
for the assignment should be written in this file in the function process image(image, size, proc img).
To start, you have download the appropriate code repository for your machine. This directory contains
the following files:
Imakefile This file is used to create a machine specific Makefile by typing xmkmf.
process image.c This is the file you have to edit in order to implement your visual routines.
lib/libCamera.a This library contains the graphical interface.
pictures A directory containing a number of example images.
The Image Processing Environment
To generate the vision program you have to type make. This will create the graphical image interface
Vision. This interface should look as follows:
2020 Manfred Huber Page 1
CSE 4360 / 5364 – Autonomous Robots
Homework 2- Fall 2020
Due Date: Dec. 1 2020 Problems marked with ∗ are mandatory only for students of CSE 5364 but will be graded for extra
credit for students of CSE 4360.
Edge Detection, Template Matching, and Blob Coloring
Basic feature extraction and region growing techniques are important steps in the first stages of im-
age processing. In this homework assignment you are going to implement edge detection, template
matching, and blob coloring as basic techniques.
Programming will follow the same procedure as in the last assignment. You are provided with a C
library containing facilities to display images which will link with the file process image.c. Your code
for the assignment should be written in this file in the function process image(image, size, proc img).
To start, you have download the appropriate code repository for your machine. This directory contains
the following files:
Imakefile This file is used to create a machine specific Makefile by typing xmkmf.
process image.c This is the file you have to edit in order to implement your visual routines.
lib/libCamera.a This library contains the graphical interface.
pictures A directory containing a number of example images.
The Image Processing Environment
To generate the vision program you have to type make. This will create the graphical image interface
Vision. This interface should look as follows:
2020 Manfred Huber Page 1
CSE 4360 / 5364 – Autonomous Robots Homework 2: Computer Vision
The graphical interface consists of two separate windows containing a set of buttons and the actual
image display window. The most important features for this assignment are the Load, Save, and
Process buttons. Load will open a small window to enter the name of an image file for processing.
Save permits to save a processed image to a file. There are some peculiarities to this interface. Do not
hit return after entering the name of a file. This will simply insert a new line into the filename. To
load (or save) the file you have to press the ok button in the filename window.
The image files used here consist of 2 integers indicating the width and height of the image in pixels
followed by the array of pixel values (each pixel is an unsigned char, i.e 0 – 255). A number of
image files can be found in the pictures directory. If you want you can also add your own pictures (a
description of how to convert images into this format is given below).
The Process button will call the function process image (your routine) and the resulting processed
image will be displayed. The original image is overwritten and the processed image can be saved
using the Save button.
Generating your own Images (Only in Linux Version)
A number of test images are provided in the pictures subdirectory. However, you can also add your
own images. For this purpose, a conversion program that runs on Linux (including Omega.uta.edu)
is provided. The program xv cam allows you to transform a large number of image formats into the
format used here (LPR format). To do this, just load your favorite image and save it in the LPR format.
The only fact to consider is that the image interface will not use anything beyond a 512×512 image
resolution (the rest of the image will be truncated when loading it into the Vision program.
In the same way a processed image can be converted into your favorite image format (such as GIF or
JPEG).
The Assignment
In this assignment you are to implement three basic vision processing techniques. To implement
these you have to write the required routines as the function process image(). This function has the
following structure:
void process_image(image, size, proc_img)
unsigned char image[DIM][DIM];
int size[2];
unsigned char proc_img[DIM][DIM];
{
…
}
The parameters of this function are image which is the original image, size which is the width and
height of this image, and proc img which is the image resulting from your processing. If this resulting
image does not have the same dimensions as the original image, then you have to update size to the
dimensions of the new image.
2020 Manfred Huber Page 2
CSE 4360 / 5364 – Autonomous Robots Homework 2: Computer Vision
1. Implement Edge Detection Using Sobel Templates
Edge detection is one of the most basic feature extraction techniques. One of the most common
techniques is using convolution with edge templates representing different directional edges
with Sobel templates among the most frequently used.
3×3 Sobel templates for vertical and horizontal edges are represented as:
vertical:
−1 0 1
−2 0 2
−1 0 1
, horizontal:
−1 −2 −1
0 0 0
1 2 1
For this part you are to implement edge detection using convolution with the two Sobel tem-
plates above. Your code, after computing the convolution, should display the resulting feature
map in the result image (you will have to normalize the convolution results to fall into the range
between 0 and 255). Edge detection with each of the templates should be performed separately.
You should hand in a short description, the code (submitted electronically), and printouts of
the images resulting from your processing (one for each of the two edge orientation templates)
applied to the Nedderman Hall and the chess board images, nedderman.lpr and chess.lpr.
2. Implement Template Matching Using Normalized Convolution
Implement normalized convolution for template matching. To do this you should rename the
process image.c file from the previous part of the assignment and write a new proc img func-
tion. Your routine has to permit specifying a region of the image which will subsequently be
used as the template (thus finding all instances of similar objects in the image). Using this tem-
plate you are to implement template matching using normalized cross correlation (convolution
which adjusts for the image and template means as well as for the image and template variances
– i.e. contrast). You can either use global or local image normalization. As in the previous part,
you should write the result of the convolution back into the result image, normalizing the values
to be between 0 and 255 (and of the correct data type).
You can select a portion of the image using the mouse. Pressing the left mouse button will
set one corner of a rectangle and releasing the button will set the opposite corner. The image
coordinates of this rectangle can be read from the variable roi, where roi.x and roi.y are the left
upper corner and roi.width and roi.height are the width and height of the selected rectangle.
Again, you should hand in a short description, the code (submitted electronically), and printouts
of the images resulting from your processing applied to the Nedderman Hall and the chess board
images, nedderman.lpr and chess.lpr.
3.∗ Implement Segmentation Using Blob Coloring
Here you have to implement blob coloring to identify regions with a common intensity in the
image. To do this you should again rename the process image.c file from the previous part of
the assignment and write a new proc img function which performs blob coloring. The result of
this operation should consist of a number of regions, each with its own, unique color (intensity).
Again you should hand in a short description, the code (submitted electronically), and the result
of your algorithm on the image blocks2.lpr from the pictures directory.
2020 Manfred Huber Page 3
