The document discusses the Linear Hough Transform, a technique invented by Paul Hough in 1962 for detecting lines in images through a method of analyzing edge points in Hough space. It outlines the basics of image processing, the Hough Transform's algorithm, and includes MATLAB code for practical implementation, alongside the advantages and disadvantages of the method. While effective for line and circle detection, the method has limitations in handling complex shapes requiring multiple parameters.

1. LINEAR HOUGH TRANSFORM
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2. What is HT?
Q. What is this?
It is an image consist of lines in some sort of fashion
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3. Introduction:
 It is invented by Paul Hough.
 The Hough transform was patented in 1962 with
name ‘method and means for detecting complex
patterns ’ the patent uses slope intercept model for
straight lines.
 It is redefined by R. O. Duda and P.E. Hart in 1972
and known as ‘GENERALIZED HOUGH
TRANSFORM’.
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4. Basics of Image Processing:
 Image: image is two dimensional matrix. The
elements of matrix represent intensity levels.
 Types of image:
1)Binary image.
2)Grayscale image.
3)True Colour image.
4)Indexed image.
grayscale image
binary image
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5. Basics of Image Processing:
 Edge: edge is high frequency content in image. It is
the portion of image where intensity changes
significantly.
%matlab command
im=imread('C:Documents and Settingsvaibhav patilDesktopprintoutscharlie_grayscale.jpg');
Im1=rgb2gray(im);
im2 = im2bw(im,level);
im3= edge(Im);
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6. Basics of Image Processing
 Hough space: Hough space is same as Cartesian co-
ordanate system except,
 it’s x-axis represent angles in accumulator.
 It’s y-axis represent distance of line from agreed
origin.
Ө=0 Ө=180
ρ =0
ρ =100
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7. Linear Hough Transform
 The linear Hough transform is popularly used for
detecting lines.
 The dimension of accumulator equals to number of
unknown parameters i.e. 2
 One dimension of this matrix is quantized angle ө
and other is distance ρ.
 Each element of matrix has a value equal to number
of points that are positioned on line represented by
quantized parameters.
Edge
detection
LHT
Image
o/p
Accumulator
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8. Algorithm
 For each data point, a number of lines are plotted
going through it, all at different angles.
 For each solid line a line is plotted which s
perpendicular to it and which intersects the origin
these.
 The length and angle of each perpendicular is
measured and saved in accumulator.
 This is repeated for each point.
 A graph of the line lengths for each angle, known as a
Hough space graph, is then created.
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9. Let see how it works?
Angle Distance
0 40
30 69.6
60 81.2
90 70
120 40.6
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10. Continued….
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11. Accumulator plotted in Hough space
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12. Example:
bit map image image
edge detected image
(degrees)
x
R (x )
0 20 40 60 80 100 120 140 160 180
-150
-100
-50
0
50
100
150
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13. Matlab program:
 %haugh transform
 t= imread('C:Documents and Settingsvaibhav patilDesktopuntitled.bmp');
 v=rgb2gray(t);
 I=imresize(v,[255,255]);
 %convert image into edge image
 BW = edge(I);
 subplot(2,1,1);
 imshow(I);
 title('bit map image image');
 subplot(2,1,2);
 imshow(BW);
 title('edge detected image');
 %calculate hough transform
 theta = 0:180;
 [R,xp] = radon(BW,theta);
 figure;
 imagesc(theta, xp, R);
 xlabel('theta (degrees)');
 ylabel('xprime');
 title('R_{theta} (xprime)');
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14. Advantages & Disadvantages
 Advantages:
1)Conceptually simple technique.
2)Handles missing occluded data gracefully.
3) Can be adapted for many other forms.
 Disadvantages:
1)Large storage space required.
2)Checks for only one type of object.
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15. Conclusion
Although it is the commonly preferred method for
lines & circle detection, the HT in general has
several limitations making it challenging to detect
anything other than lines and circles. This is
especially the case when more parameters are
needed to describe shapes, this add more complexity.
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16. THANK YOU
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17. Questions
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