International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
is due to the fact that each colour component (R,G,B) is stored 
in separate section of the matrix (e.g. the Red component is 
stored in first third of the matrix, the Green component is stored 
in the second third of the matrix, and the Blue component is 
stored in the third (last) third of the matrix. 
3.2.2. Segmentation of Pita images: Segmentation is a concept 
in which an image is subdivided into its constituent regions or 
objects. The level to which the subdivision is carried depends 
on the goal of carrying out the segmentation process (Gonzalez 
and Woods,1992; Parker,1991; Perez and Gonzalez, 1987). 
Segmentation procedures applied on monochrome images 
generally utilize one of two properties of the gray level values 
for the segmentation process, these are discontinuity and 
similarity. If the discontinuity is utilized, then the approach is to 
partition an image based on abrupt changes in gray level. The 
main areas of interest within this category are detection of 
isolated points, lines, and edges in an image. On the other hand 
if the similarity property is utilized, then factors such as region 
growing, region splitting, and thresholding processes can be 
applied. (Rosenfeld and Kak, 1982; Prat, 1978; White and 
Rohrer, 1983). The segmentation process mentioned here was 
applied only when the other algorithms couldn't. provide 
satisfactory measurements. 
Majority of image processing operations in this study were 
carried out by the use of the Image Processing Extension 
Package and its conventions of the MathCAD Professional 2000 
Program, © MathSoft Copyright (Mathsoft, 1999). 
To carry out segmentation, first a mask must be constructed to 
delineate the edge of the pita loaf and within that edge to 
delineate the border of different objects in the bread. Areas that 
were darkened by baking were delineated easily by 
segmentation process. To carry out segmentation, the image of 
the loaf was first binarized, and then upper and lower thresholds 
were chosen to include the loaf's entity and exclude its 
background. If the outside edge delineation of loaf is not 
satisfactory, then erosion can be applied to enhance the 
delineation process. 
Figures 3 and 4, show binarized image of the pita bread and its 
eroded version, it is evident in these images that the background 
was separated from the front of the image. After binarizing the 
geometric measurements are carried out to determine the 
diameter, the radius, and height of the loaf, and consequently 
the surface area and the volume of loaf before collapsing, 
simple area of loaf (after collapsing) can be measured too. 
NPLR is abbreviated for Number of Pixels in the Longest Row 
in the image, and NPLC is abbreviated for Number of Pixels in 
the Longest Column in the image. If one pixel in loaf image is 
considered to be having dimensions of (width x length- area) 
synonymous to (1 x 1 = 1), then the measurements can be done 
as follows: 
Loaf diameter: 
D=NPLR (5) 
Loaf radius: 
R=D/2 (6) 
Loaf Height 
H=NPLC (7) 
Loaf upper surface area: 
480 
S. EZXX(R 4H) (8) 
up 
Loaf simple surface area: 
—-zxR (9) 
simple 
Loaf volume: 
VurxHx(QxR-4-H06:30nm, 
The colour and extension of the dominant colour in the loaf can 
be determined by applying labeling process an the image and 
choosing the largest component in the labeled image and 
considering it the dominant colour, then setting upper and lower 
threshold for the gray scale to be included in the dominant 
colour, and calculating the number of pixels in this component. 
First the pita bread loaf entity was separated from the 
background of the image. One good and practical way of 
separating the front from the background of the loaf image is to 
associate region with its colour and implement the hue 
saturation. vector of the whole image, then carry out a 
binarization process. This can be done by converting the RGB 
representation of the image to HSV and applying an extraction 
process to emphasize the association of region with its 
corresponding colour (Zhoi, Chalana, and Kim, 1998). 
Pital := READBMR{("C:\Pics\Fig12.bmp") 
  
Pital := READ RGH"C:\Pics\Fig12.bmp") 
Pital := extract(rgb to hsv (Pital),2) 
  
Figure 2. RGB and hue saturation images of pita bread 
To separate the forefront of the image from its background, a 
binarization process at a suitable gray scale threshold is applied 
(Zhoi, Chalana, and Kim, 1998), then holes, spots, noise, and 
In 
no 
ca 
fo 
  
ni 
nc 
The 
dete 
ima,