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Embedded Vision is ubiquitous across
a range of industries and applications,
from ADAS and Guided Robotics to
medical imaging and augmented
reality. The breadth of embedded
vision penetration across multiple
market segments is staggering.
In most of these applications the
downstream image processing pipeline
is very similar. This downstream
pipeline contains functions such as
Image Sensor / Camera interfacing
and reconstruction of the image in a
format suitable for further processing.
Commonly used algorithms within
downstream processing are colour
reconstruction (Bayer Filter), colour
space conversion and noise reduction
algorithms. It is the application specific
algorithms where the differences
between
applications
become
apparent. Implementing these is
where the embedded vision developer
expends significant time and effort.
These application algorithms are
reVISION Stack:
Accelerating your Embedded Vision development
Nick Ni and Adam Taylor
edge as increasingly Embedded Vision
applications are autonomous and
cannot depend upon a connection to
the cloud. One example of this would
be vision guided robotics, which
are required to process and act on
information gleaned from its sensors to
navigate within its environment. Many
applications also implement sensor
fusion, fusing several different sensor
modalities to provide an enhanced
understanding of the environment
and further aid the decision-making,
bringing with it increased processing
demands. Due to the rapid evolution
of both sensors and image processing
algorithms the system must also be
able to be upgraded to support the
latest requirements of the product
roadmap. The rise of autonomous and
remote applications also brings with
it the challenges of efficient power
dissipation and security to prevent
unauthorized modification attempts.
To address these challenges,
often complex to implement,
using techniques such as object
detection and classification, filtering
and
computational
operations.
Increasingly
these
application
algorithms are developed using open
source frameworks like OpenCV and
Caffe. The use of these open source
frameworks enables the Embedded
Vision developer to focus on
implementing the algorithm. Using the
provided pre-defined functions and IP
contained within, removes the need to
start from scratch which significantly
reduces the development time.
Depending upon the application, the
challenge faced by the designer is not
only how to implement the desired
algorithms. The Embedded Vision
developer must also address both
challenges faced by the application
and its environment while considering
future market trends.
These challenges and trends include
processing and decision making at the
48 l New-Tech Magazine Europe