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###### 日期：2019-03-15 10:41

CS 659 Image Processing

Homework #3

Covering Lectures 7, 8, 9 and Reading Material HW3R

NOTES: Submit only the homework “solution” (do not include these homework questions) in

Microsoft Word format to http://moodle.njit.edu/ before the above deadline. Absolutely, no late

submission is accepted. Write the answers in your own words individually. Any plagiarism will

post a “ZERO” score or cause a “FAIL” grade. The course requires Matlab programming. You can

Submit your answer in a Microsoft Word file. Do not include the questions; just provide the

Totally, there are 5 questions. Each question is 20 points. Grading policy checks the correctness

and completion of showing resulting images, Matlab codes, and text responses.

Required images: cameraman.tif, coins.png, duck.jpg

3.1 (20 points)

(a) Describe how Hough Transform can detect a straight line. Describe the step-by-step algorithm to

implement Hough Transform for detecting a straight line.

(b) Apply the Hough transform to the following image to detect lines. Show the step-by-step results

of line detection by Hough transform.

0 0 0 0 0 0 0 1

0 0 0 0 0 0 1 0

0 0 0 0 0 1 0 0

0 0 0 0 1 0 0 0

0 0 0 1 0 0 0 0

0 0 1 0 0 0 0 0

0 1 0 0 0 0 0 0

1 0 0 0 0 0 0 0

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(c) Read the following image: cameraman.tif which is 256 ? 256 resolution. Use program to

perform Hough transform. An example of Matlab code: x=imread('cameraman.tif');

hx=hough(x); imshow(mat2gray(hx)*1.5); The multiplication of 1.5 is just to brighten up the

image. The Hough transform function can be written as follows. Show the resulting Hough

transform image.

function res=hough(image)

% HOUGH(IMAGE) creates the Hough transform corresponding to the image IMAGE

edges=edge(image,'canny');

[x,y]=find(edges);

angles=[-90:180]*pi/180;

r=floor(x*cos(angles)+y*sin(angles));

rmax=max(r(find(r>0)));

acc=zeros(rmax+1,270);

for i=1:length(x),

for j=1:270,

if r(i,j)>=0

acc(r(i,j)+1,j)=acc(r(i,j)+1,j)+1;

end;

end;

end;

res=acc;

(d) Find the maximum value of the transform using mx=max(hx(:)), and find the r and theta values

corresponding to the maximum using [r, theta]= find(hx==mx); We can create a small function,

called houghline() as below, to draw lines for us, given their perpendicular distance from the

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origin and the angle of the perpendicular from the x axis. Finally, we can draw the line on top

of the image using: imshow(x); houghline(x, r, theta); Show the resulting line overlapping with

the input image.

function houghline(image,r,theta)

% Draws a line at perpendicular distance R from the upper left corner of the

% current figure, with perpendicular angle THETA to the left vertical axis.

% THETA is assumed to be in degrees.

[x,y]=size(image);

angle=pi*(181-theta)/180;

X=[1:x];

if sin(angle)==0

line([r r],[0,y],'Color','black')

else

line([0,y],[r/sin(angle),(r-y*cos(angle))/sin(angle)],'Color','black')

end;

3.2 (20 points)

(a) Read the image “coins.png” and display its histogram plot. Inspect the histogram near the right

of the background pixels by activating data cursor. Choose the threshold value of 120 to

generate a new threshold binary image. Display original image, its histogram plot, and the

threshold binary image.

(b) Read the input image “cameraman.tif” and compute the edges in the image using different edge

detectors like Roberts, Sobel, Prewitt, Log and Canny. Show six images using subplot with title on

each: the original image, Roberts, Sobel, Prewitt, Log and Canny.

3.3 (20 points)

(a) Referring to the reading material HW3R, Fig. 4.1, design a binary image of 200 columns by 100

rows, which contains a rectangle whose length = 120 pixels and height = 60 pixels in the center.

Set the rectangle to be white and the background to be black. Use a circular structuring element

whose radius is 9 pixels to perform the following: (Hint: Matlab functions: strel('disk',9,0);

imdilate, imerode, imopen, imclose) (i) Binary dilation (ii) binary erosion (iii) binary opening,

and (iv) binary closing. Show the Matlab source code and the resulting five images: the

rectangular image, the dilated image, the eroded image, the opened image, and the closed

image.

(b) Referring to the reading material HW4R, Fig. 4.2, design a circular structuring element whose

radius is 5 pixels to perform the following operation with the input image “lena_256.bmp”:

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(Hint: Matlab functions: imdilate, imerode, imopen, imclose) (v) Grayscale dilation (vi)

grayscale erosion (vii) grayscale opening, and (viii) grayscale closing. Show the Matlab source

code and the resulting five images: the original Lena image, the dilated image, the eroded

image, the opened image, and the closed image.

(c) Hand calculate the morphological dilation and erosion of f and k as shown below. Use the

upper-left pixel of k as the origin. Note: do not consider the computation to the pixels which are

located outside the boundary of f.

f: k:

1 1

2 -3

3.4 (20 points)

(a) Hand calculates the two-step algorithm in Image Representation as follows for the city-block

distance transform on the image data below. Treat the pixels outside the image boundary to be

zeroes. Show the two output data after the first step and the second step.

(b) Repeat the same procedure for the chessboard distance transform. Show the two output data

after the first step and the second step.

bottom- to - top

right - to -left

"( ) min[ '( ), "( ) 1],

Ptop- to - bottom

left - to - right

00011100

11111100

11111111

11111111

11111111

11111110

3.5 (20 points)

Use hand calculation to obtain the crack codes for the following binary region by using the crack

following algorithm considering 8-connectedness (so this image contains one object)? Use

hand calculation to obtain the chain codes by using the border following algorithm considering

4-connectedness (so this image contains two objects)? You need to show both (a) crack and (b)

chain codes for the following image. (Note that, using the counter-clockwise approach)

5 3 3 0

1 4 2 7

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0000000

0011110

0111000

0010110

0000000