Download - Coordinates

Figure 12: HTC image processed through
the circle identifi cation routine
Figure 13: Original image taken
with iPhone 4 camera
Figure 14: iPhone 4 image converted
to binary with noise cleaned off
Figure 15: iPhone 4 image processed
using the circle identifi cation script
24 | Coordinates May 2012
and dynamic orientation, and inertial
measurements (Microstrain, 2011), (CMU,
2010). It has an orientation accuracy of:
± 0.5° typical for static test conditions
± 2.0° typical for dynamic (cyclic) test
conditions & for arbitrary orientation angles
And an accelerometer bias stability of:
± 0.005 g for ± 5 g range
± 0.003 g for ± 2 g range
Its other bias speci cations can be
found in (Microstrain, 2011). The
accelerometer and gyro on the iPhone
are a lower quality of inertia sensors
than the MicroStrain 3DM range.
Camera – Object
detection tests
Tests were conducted to assess the quality
of the images produced by various types
of smart phones and the ability to extract
feature information from such camera
photo images. Matlab was used for the
image analysis. Three phones were used in
the image analysis tests, the HTC Wild re,
the iPhone 4 and the Nokia 6500 slide. The
following are their camera speci cations:
HTC Wildfi re:– 5 megapixel colour
camera with LED ash (HTC, 2010).
iPhone 4: – 5 megapixel still
camera with LED ash, VGAquality
photos (Apple, 2010a).
Nokia 6500 slide: – a 3.2 megapixel
camera with Carl Zeiss optics, auto focus,
and 8x digital zoom. Has a powerful
double LED ash (Nokia, 2010).
Simple Shape ID – Circle
Identifi cation:
The following was used as a metric to
identify round objects in an image:
Metric = (4 x pi x area) / perimeter 2 .
This metric is equal to one only for a circle
and it is less than one for any other shape.
The discrimination process can be controlled
by setting an appropriate threshold. Different
shapes were cut out from white paper and
placed on a black background. These were
photographed using the selected smart
phones. The photographs were taken in an
indoor environment with indoor lighting
on, hence the presence of shadows as can
be seen in Figure 13. Photos were taken
using the three phones one after the other
at the same time of day and under the same
lighting conditions. The position it was
taken from was approximately the same. The
images were converted to binary and noise
in the data was cleaned off before analysis.
HTC Wildfi re
The various shapes in Figure 12 have been
highlighted in different colours and their
boundaries traced. It can be seen that the
circular shapes have a higher metric of
0.83 and 0.85 respectively. The fact that
the shapes were hand cut out, as well as
the clarity of the image are likely to be
the contributors to why the metric is <
1 for the circular shapes. In this image
a threshold of about 0.78 can be applied
to discriminate between the circular
and not circular objects in the image.
This indicates that a smart phone image
can be used for shape identi cation.
iPhone 4
The iPhone photo was more impacted by
the shadow on the image. This resulted
in the rectangular shape which was in
the shadow area being removed when
the image was converted from colour to
grayscale and then binary. Thus the circle
identi cation was not effective as the large
circle was distorted leading to a low index
value of 0.42. The smaller circle was clearly
outlined with an index value of 0.70 (lower
than that from the HTC). It is possible to
perform some further pre-processing or
to use a different grayscale threshold to
reduce the shadow effect for the iPhone
image. However for this comparison test
it was desired that the same script be run
for all the photos for proper comparison.
Nokia 6500 Slide
The original Nokia image compared to
the others initially appeared to be a lesser
quality than the others. However the
shape identi cation routine performed
well with it. The large circle had a
metric of 0.82 while the smaller circle
had a metric of 0.78 (Figure 16).