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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
  
2.1 Diameter and Radius 
The pita bread diameter (D) can be measured from an upper 
view (or front view) image of the loaf. If an upper view image 
is used to measure the diameter, then the number of pixels from 
one point on the perimeter of the loaf to its counterpart point on 
the other end of the perimeter can be calculated by moving in 
45 degrees toward the centre point of the loaf circle. If the 
number of pixels calculated is multiplied by the pixel’s radius 
(R) of the loaf which is half the diameter (R=D/2), figure 1. 
If the diameter (D) is calculated from a front view image of the 
loaf, then the diameter will be in this case equal to the number 
of pixels along one horizontal line (the diameter itself) 
extending between the most left point and the most right point 
of the loaf’s perimeter, multiplied by the pixel width. 
  
  
  
  
  
Figure 1. Arabic pita bread 
2.2 Height 
The loaf height is the highest point in the loaf. It can be 
determined by calculating the number of pixels along a vertical 
line extending from the peak point of the loaf and base line of 
the loaf and multiplying the number of pixels by the pixel’s 
height, figure 1. 
2.3 Loaf area 
Loaf area can be calculated in one of two ways. The first one 
simple calculation in which the loaf is considered to be a flat 
disk, the area simply calculated as: 
$ = 1x R (1) 
simple 
In the second and more accurate case, the loaf is considered to 
be a sphere sector; this case is true right after the baking phase 
and before the collapse of the loaf. Here the loafs surface (face) 
area is calculated as: 
S, zzx(R 4 H^) (2) 
up 
2.4 Loaf volume 
Loaf crust volume is calculated according to the following 
equation: 
V-zxHx(xR - H^)/6 (3) 
479 
3. VISUAL FEATURES EXTRACTION 
3.1 Colour Features Measurements 
At the beginning and during the backing phase, all conditions 
are set to produce pita bread loaf with ideal characters. 
Conditions such as dough's water content (humidity), thickness 
of loaf dough, movement speed of backing belts, and backing 
heat are all important factors in producing ideal crusty brownish 
pita bread. 
In many cases, however, the resultant dominant backing colour 
of the bread is not always the same as the wanted backing 
colour. The ideal backing colour is determined by choosing 
samples from areas that were successfully backed to the wanted 
brownish colour, then, the average value of the grey level of 
these samples is determined and adopted as the grey level 
representing the ideal backing colour. 
Dominant colour is defined as a spectrum range with minimum 
and maximum grey level values centred around the grey level 
value associated with the greatest number of pixel found in the 
pita bread image. The two maximum/minimum thresholds can 
be adjusted at will; this will result in significant flexibility in 
determining the dominant colour by including/excluding wider 
range of the colour spectrum (grey level). Light and dark areas 
in the pita bread loaf are those areas exposed to less or 
excessive backing heat. In the context of this study they are 
defined as the summation of lighter/darker pixels deviated from 
the ideal backing threshold. 
The average colour of the whole pita bread's loaf is an 
important factor in determining the success in obtaining the 
required backing colour or approximating that goal. The 
average colour of the whole pita bread's loaf (Average Grey 
Scale: AGS) is determined by computing the summation of the 
grey levels of all pixels (Grey Scale of Pixels: GSP) and 
dividing it by the number of pixels in the loaf (Number of All 
Pixels: NAP). The following equation illustrates the calculation 
of the average grey level of the loaf. 
D> GSP 
AGS = —— 
NAP 
(4) 
3.2 Image Processing Procedures 
Visual feature extraction was carried out on the pita bread 
images using a combination of program code developed 
specifically for this project and the commercially available 
image processing extension package of the MathCAD 2000 
program. Image processing procedures that were implemented 
in this study are the followings: 
3.2.1. Reading Image Files: Images of the incoming loafs of 
bread are captured in the JPEG image file format. Then the 
binary data is converted to a matrix carrying the values of the 
grey scale of the pixels as it its elements, and each element in 
this matrix will be associated with row and column coordinates. 
If the image captured is saved as a row monochrome image, 
then the image can simply be converted to an (M*N) element 
matrix, where M is the image's rows, and N is the image's 
columns. On the other hand if the image captured is saved as 
RGB coloured image, then the image can be converted to an (M 
x 3N) element matrix, where M is the image's rows, and 3*N is 
the image's columns. This apparent triplication of the columns