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illumination environments, such as watching
television or reading but they have little use in
mobile environments, since they reduce the visual
field and present unrealistic images which prevent
the user from getting a real insight into the distance
at which obstacles are.
Most of the LV pathologies are characterized by
a slow progression with residual vision deteriorating
gradually with time; therefore the patients have
requirements that change as the disease advances.
Moreover the LV diseases affect unevenly to
different areas of visual field, thus a non-uniform
processing adapted to the affected needs and visual
field may be useful.
The systems mentioned above do not enable
totally customize the processing to the visual needs
and disease progression.
In this context the main contribution of the
present system is a new platform with allows
implementing and testing different kinds of image
enhancements adapted to the visual needs of each
affected, to his visual field, and to the evolution of
his disease. So as to customize the enhancements the
system has a graphical user interface. Moreover we
have developed different kinds of image
enhancements which improve the image contrast
even in low light environments where low vision
affected experiment several difficulties. The
designed system achieves real time image
processing (above 25 frames per second video-rate)
using a last generation Graphic Processor Unit
(GPU) integrated in a light weight netbook.
Even though embedded solutions based on DSPs
and/or FPGA may provide speed performance,
modern GPUs integrated in small size portable
computers can also provide the minimum latency
and frame rate required as they have multiple scalar
processors. The main advantage of GPU-based
systems is that they are easier and faster to
customize to the needs each visual impaired than
other implementations. It also provides facilities for
rapid development and testing of new image
enhancements.
2 SYSTEM SPECIFICATIONS
The proposed system can be viewed as a SW/HW
platform for low vision support, which aims to easily
implement and test different types of visual
correctors tailored to the needs of each affected, and
his visual field. Therefore the system aims to
transform images taken from the patient's
environment and tries to convey the best information
possible through his visual rest, applying different
transformations to the input image.
The main characteristics are:
(1) Customizable System: The system is able to
perform a sequence of transformations totally
adapted to the visual requirements, and visual
field of each low vision affected.
(2) Portability: The image processing device needs
to be carried by the patient in mobile
environments such as walking and similar tasks.
(3) Real Time Processing: The system is able to
perform different image enhancements in real-
time by using a low-power GPU embedded in a
light weight netbook.
(4) Flexibility: The system can combine several
types of visual enhancements including digital
zooming, spatial filtering, edge extraction and
tone-mapping and works properly in non uniform
illumination environments.
2.1 Architecture
The developed platform runs over a netbook ASUS
EEPC 1201 PN. It uses the netbook’s CPU and a
GPU NVIDIA ION2 connected via PCI-express.
In the CPU runs the main application, and is
where the user can define the processing to be
performed according to the visual needs of each LV
using a graphical user interface (UI). The UI is
based in the system RETINER (Morillas et al.,
2007) and a platform for speeding up non-uniform
image processing (Ureña et al., 2010). The
application performs algebraic optimizations based
on the convolution properties to simplify filter
stages.
After the optimization we can make out what
tasks are to run on the GPU and on the CPU. The
tasks performed by the CPU are invoked directly by
the application, whereas in the case of the GPU
using MEX (NVIDIA Corporation, 2007) modules
allows us to both set the type of processing to be
performed, and image transfers.
In Figure 1 we can see a diagram that
summarizes the functional architecture of the
implemented system.
Our system uses GPU to speed up the image
processing since current GPUs has a multiprocessor
architecture suitable for pixel-wise processing.
Most GPUs, given its size and high power
consumption are not suitable for portable
applications. However, the GPU used in this system,
the NVIDIA ION2, has 16 processors integrated on
a platform with low power consumption; which has
its own battery with about 4 hours of usage.
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