The final section will present the result of 
applying the algorithms to the radar image in 
Figure 1. 
  
Figure 1. Original Radar Image of an Airfield. 
  
PROCEDURE 
EDGE PRESERVING SMOOTHING -> EDGE 
DETECTION (MAGNITUDE AND DIRECTION) 
—> RELAXATION FOR EDGE REINFORCEMENT 
-> THINNING -> CONNECTED COMPONENTS 
-» REGION PROPERTY CALCULATIONS -» 
EXTRACT CONNECTED COMPONENTS OF THE 
AIRFIELD -> BORDER FOLLOWING -> 
ELIMINATE PIXELS NOT ON THE OUTER- 
MOST BORDERS -> GENERATE FREEMAN CHAIN 
CODES -> POLYGON APPROXIMATION 
  
  
  
Figure 2. Procedure Used for Extracting the 
Airfield 
Edge Preserving Smoothing 
The purpose of an edge preserving smoothing algo- 
rithm is to eliminate noise and to preserve edges 
from degradation. The variation of the gray tone 
in a neighborhood around each pixel is used to 
determine the direction that is most homogeneous. 
Smoothing is then performed in this direction. 
The particular approach to edge preserving smooth- 
ing used in this research consisted of analyzing 
the gray tone variations within each 5- by 5- 
pixel area in the image. For each 5- by 5-pixel 
area, nine geometric figures are formed using the 
center pixel. Four of the geometric figures are 
pentagons. Four of the geometric figures are 
hexagons. One of the geometric figures is a 
Square. Each of the four pentagon figures is 
formed by using the center pixel and one of the 
outermost edges of the 5- by 5-pixel area. Each 
of the hexagon figures is formed by using the cen- 
ter pixel and one of the outermost corners of the 
>- by 5-pixel area. The 3- by 3-pixel square is 
formed using the center pixel and its first near- 
est neighbors. The pixels associated with each 
of the geometric figures are used to compute the 
mean and variance of the gray tone for each fig- 
ure. The pentagon and hexagon figures each have 
7 pixels associated with them. The Square has 
9 pixels associated with it. A list of nine means 
809 
and nine variances is generated from all of the 
computations involving the nine geometric figures. 
The gray tone value of the center pixel is replac- 
ed by the particular mean gray value that is asso- 
ciated with the smallest variance. The theory 
behind this edge preserving technique was devel- 
oped by Nagao and Matsuyama (Nagao and Matsuyama, 
1980). The algorithm can also be used in an iter- 
ative manner, that is, the output of one smoothing 
operation can be used as the input to another. 
Edge Detection (Magnitude and Direction) 
  
After edge preserving smoothing has been performed, 
an edge detection operator is used to enhance 
edges and to compute the direction of each edge. 
The edge detection operator used was the Sobel 
operator. This operator consists of two 3 by 3 
masks. The masks are applied to each pixel to 
calculate a magnitude image and a directional 
image. The magnitude image is the edge enhanced 
image. The directional image contains the dir- 
ection of the edge at each pixel. The direction 
of an edge is defined as the angle between the 
edge and the x-axis. The x-axis extends along the 
top row of the image with the origin at the pixel 
in the upper left-hand corner. The y-axis extends 
downward along the first column of the image. The 
magnitude image is computed by taking the square 
root of the sum of the squares of the result of 
applying the two masks at each pixel. The dir- 
ectional image is calculated by taking the inverse 
tangent of the ratio of the results of applying 
the two masks. Because the direction of an edge 
has a 180 degree ambiguity, a convention must be 
established to eliminate this ambiguity and estab- 
lish a fixed direction for each edge. The conven- 
tion used in this research was that the edge dir- 
ection was taken in such a way that the darker 
side is always on the left when facing in the 
direction of the edge. 
Relaxation for Edge Reinforcement 
  
The result of applying the Sobel edge operator 
yields an image in which some edges are defined 
very well, some edges are poorly defined, and some 
edges have holes in them. In addition, some large 
responses are obtained where there are no edges. 
These errors occur because of noise in the origi- 
nal image and also because the Sobel edge detector 
is not perfect. The purpose of the relaxation 
calculations is to enhance edges by increasing the 
gray tone value of the pixels that are really on 
edges, and to decrease the gray tone value of the 
pixels that are not on edges. Initially, the 
magnitude and direction of the edge at each pixel 
are obtained from the edge detection operation. 
The magnitude at each pixel location is divided by 
the maximum of the magnitudes over the entire 
image in order to define the probability of an 
edge at each pixel. The location of each pixel 
will be designated by the quantity (i,j), where i 
represents the row dimension and j represents the 
column dimension. The relaxation process for edge 
reinforcement consists of defining a new edge 
probability and a new edge angle at each (i,j) in 
terms of the old ones at (i,j) and its neighbors. 
The neighbors used in this research were the first 
and second nearest neighbors. This definition of 
neighbors is not a restriction on the basic techni- 
que. As explained by Schachter, et al. (Schachter, 
et al., 1977) the number of neighbors used in the 
relaxation calculations is arbitrary. However, if 
more neighbors are used, the result will be longer 
computation times. The calculation of the new 
EE 
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