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8.5. IMAGE SCALING
99
Figure 8.6: Section of Figure 8.3 Cut and Pasted Into Figure 8.2
performs the pasting comprises simple loops that copy numbers from one
array to another.
Much of the cut and paste work is done is the main routine of the
maincp program shown in listing 8.4. The main program checks the com-
mand line parameters, allocates arrays, and reads image arrays. It then
calls check
cut
and
paste
limits to ensure that the input rectangle exists and
that it will t in the output image. Listing 8.3 shows the source code for
check
cut
and
paste
limits.
8.5 Image Scaling
The rst edition of this book presented software that could scale images.
This edition covers this topic in chapter 13. The method used for scaling in
this edition is far superior to that given in the rst edition.
8.6 Blank Images
Ahandy utility program is one that creates a blank image. A blank image is
useful as a bulletin board to paste other images together. Figure 8.7 shows a
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100
CHAPTER 8. IMAGE OPERATIONS
composite made of two images pasted onto a blank image. The two images
are of a boy with one being the negative of the other (more on this in the
next section).
Figure 8.7: Two Images Pasted Onto a Blank Image
Listing 8.5 shows the create program that created the blank image. This
interprets the command line, sets up the image header, and calls with cre-
ate
allocate
ti
le or create
allocate
bmp
le. Those routines ll the blank
image with zeros.
8.7 Inverting Images
Another handy utility program inverts the pixels in an image. Some images
appear as negatives for certain image viewers. The boy in the upper part of
Figure 8.7 is the negative of the boy in the lower part. The invert program
created on from the other. The invert program reads the input image, inverts
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8.8. CONCLUSION
101
the pixels by subtracting them from the number of gray shades (0 becomes
255, 1 becomes 254, etc.), and writes the output image to disk. I don’t use
invert often, but it is essential.
8.8 Conclusion
This chapter described several image operations that provide the ability to
edit images by adding and subtracting and cutting and pasting. It described
two utility programs that create blank images and invert images. These
operations are fun because they allow you to place original images and pro-
cessing results together in combinations and display them all at once. Enjoy
and experiment. These are low-level tools that you can combine in an endless
variety of ways.
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102
CHAPTER 8. IMAGE OPERATIONS
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Chapter 9
Histogram-Based Segmentation
This chapter describes simple image segmentation based on histograms and
image thresholding. Image segmentation is the process of dividing an image
into regions or objects. It is the rst step in the task ofimage analysis. Image
processingdisplays images and alters them to make them look \better," while
image analysis tries to discover what is in the image.
The basic idea of image segmentation is to group individual pixels (dots
in the image) together into regions if they are similar. Similar can mean they
are the same intensity (shade of gray), form atexture, line up in a row, create
ashape, etc. There are many techniques available for image segmentation,
and they vary in complexity, power, and area of application.
9.1 Histogram-Based Segmentation
Histogram-based image segmentation is one of the most simple and most
often used segmentation techniques. It uses the histogram to select the gray
levels for grouping pixels into regions. In a simple image there are two
entities: the background and the object. The background is generally one
gray level and occupies most of the image. Therefore, its gray level is a large
peak in the histogram. The object or subject of the image is another gray
level, and its gray level is another, smaller peak in the histogram.
Figure 9.1 shows an image example and Figure 9.2 shows its histogram.
The tall peak at gray level 2 indicates it is the primary gray level for the
background of the image. The secondary peak in the histogram at gray level
8indicates it is the primary gray level for the object in the image. Figure
103
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104
CHAPTER 9. HISTOGRAM-BASED SEGMENTATION
22222232221222212222
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22588897777788888232
12988877707668882122
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21278221222666665222
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21221231223266622321
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25221212222222222222
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21222222221222222142
Figure 9.1: An Image Example
9.3 shows the image of Figure 9.1 with all the pixels except the 8s blanked
out. The object is a happy face.
This illustrates histogram-based image segmentation. The histogram will
show the gray levels of the background and the object. The largest peak
represents the background and the next largest peak the object. We choose
a threshold point in the valley between the two peaks and threshold the
image. Thresholding takes any pixel whose value is on the object side of the
point and sets it to one; it sets all others to zero. The histogram peaks and
the valleys between them are the keys.
The idea of histogram-based segmentation is simple, but there can be
problems. Where is the threshold point for theimage ofFigure 9.1? Choosing
the point mid-way between the two peaks (threshold point = 5), produces
the image of Figure 9.4. This is not the happy face object desired. Choosing
the valley  oor values of 4 or 5 as the threshold point, also produces a poor
result. The best threshold point would be 7, but how could anyone know
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9.1. HISTOGRAM-BASED SEGMENTATION
105
Figure 9.2: A Histogram of the Image of Figure 9.1
--------------------
--------------------
---888------88888---
---888-------888----
--8888--------8-----
----8---------------
--------------------
--------------------
----88--------8-----
---88888----8888----
----88888--888------
------8888888-------
-------888888-------
--------88----------
--------------------
--------------------
--------------------
--------------------
--------------------
Figure9.3: TheImage in Figure9.1 with All thePixelsExceptthe 8sBlanked
Out
106
CHAPTER 9. HISTOGRAM-BASED SEGMENTATION
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00000000000011100000
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00000111111111000000
00000011111110000000
00000000110000000000
00000000000000000000
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Figure 9.4: Figure 9.1 with a Threshold Point of 5
that without using trial and error?
This example is dicult because there are only ten gray levels and the
object (happy face) is small. In practice, the techniques discussed below will
perform adequately, but there will be problems. Automatic techniques will
fail.
9.2 Histogram Preprocessing
Histogram-based segmentation depends on thehistogramoftheimage. There-
fore, the image and its histogram may need preprocessing before analyzing
them. The rst step is histogram equalization, explained in Chapter 4. His-
togram equalization attempts to alter an image so its histogram is  at and
spreads out over the entire range of gray levels. The result is an image with
better contrast.
9.2. HISTOGRAM PREPROCESSING
107
Figure 9.5 shows an aerial image of several house trailers with its his-
togram. The contrast is poor and it would be very dicult to nd objects
based on its histogram. Figure 9.6 shows the result of performing histogram
equalization. The contrast is much better and the histogram is spread out
over the entire range of gray levels. Figure 9.7 shows the result of performing
high-pass ltering on the image of Figure 9.6, explained in Chapter 7. It
is easy to see the house trailers, sidewalks, trees, bushes, gravel roads, and
parking lots.
Figure 9.5: Aerial Image with Poor Contrast
The next preprocessing step is histogram smoothing. When examining
ahistogram, look at the peaks and valleys. Too many tall, thin peaks and
deep valleys will cause problems. Smoothing the histogram removes these
spikes and lls in empty canyons while retaining the same basic shape of the
histogram.
Figure 9.8 shows the result of smoothing the histogram given in Figure
9.2. You can still see the peaks corresponding to the object and background,
108
CHAPTER 9. HISTOGRAM-BASED SEGMENTATION
Figure 9.6: Result of Histogram Equalization on Figure 9.5
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