Artificial vision in the common world:
Artificial vision, also known as computer vision, is a notion that most people
don’t get immediately. It’s a branch of artificial intelligence (AI) and it is
about copying the operating process of the human vision system and adapting it
to machines. You surely have already seen examples in science fiction movies
e.g. the robots in Terminator or Sony NS-5 robots from the movie I Robot. Those
machines use artificial vision to operate in their environment. However, you
should b e aware that computer vision isn’t science fiction, it exists in the
real world to a lesser extent. You’re often using it in your daily life without
even knowing it. How do you think your smartphone unlocked itself when you
bring it in front of your face? How does your front camera capture your face to
add a filter to it?

Figure
1: NS-5 Robot from the movie I robot
Before describing and illustrating artificial vision, let’s start
with the human vision system. . First of all, light emanates from a source.
When it reaches an object, the light is partially absorbed and the rest is
reflected. Those reflected light rays reach the observers’ eyes. In the eye,
the cornea converges the light through aqueous humor to the eye lens. The eye
lens converges light across the vitreous humor on the retina. The eye is a
dioptric assembly which conveys light to the sensor. The human visual system’s
sensor is called the retina. It’s made of several cell layers, cones ( used for
diurnal and colored vision) and rod cells ( used for night and grayscale
vision). These sensors’ cells capture a lot of information. The information is
carried through the optical nerve to different areas of the brain cortex. Those
areas use the signals to extract useful information and analyze the scene.

Figure
2:Drawing
of an eye’s side view and the light rays’ path
In order t o see, you need organs that : - Focus/guide light to a
sensor: the cornea and eye lens - Work as sensor on which the image of the
observed scene will drop: the retina - Work as cables to spread the information
coming from the sensor: the optical nerve - Are able to analyse images in order
to extract information and make a decision on what to do next: the visual
cortex and the encephalon.
Figure
3: Drawing of an eye’s side view
and its composition
Eye fulfills several functions as explained
above . The optical nerve carries signals captured by the eye to the brain. The
visual cortex analyses the received signals and extracts the useful
information, then it classifies those elements for us to assess our environment
(object recognition, shapes, faces, colors etc..). All the analysis is done un
consciously by the brain. This analysis is also the result of a lifelong
learning Now let’s move to computer vision: Computer vision uses several
devices to capture, transmit, treat and analyze information like the human
vision system. In the machine realm, we are able to control
specific criteria. common vision systems
are actually not as adaptable as human’s eyes and brain.
Firstly, we have to pick a sensors / camera
based on several criteria:
-
Depending the application, you
will have: linear or matrixial sensors, size, number of pixels
-
The smallest element you want
to see.
-
The type of power supply you
want to use.
-
Etc…

Figure
4: photo of a vision came
Secondly, you can choose the light source.
It helps to enhance the element you want to detect. The type of source isn’t
all you have to pick., I t is also very important to select the position of
this light e.g. angles, distance, number. It will help you to maximize contrast
between background and your subject. The most common positions are s:
the low angle light which
enhances surface defects.

Figure 5: diagram of the low angle configuration
the front light which shows
aspects and shapes
Figure
6: diagram of the front light configuration
The back light used to
highlight the objects’ edges. (length measurement)

Figure 7 : diagram of the back light configuration

Figure 8 : diagram of the back light configuration
You can choose a lens which fulfil some criteria:
-
Sensor size
-
Circle of full light of the
lens
-
Working distance (distance
between inspected objects and sensor)
-
Coating on lenses
-
Wave length of light source
-
Etc ..

The
optical lens focuses the light, that is directed on the camera sensors

Figure 10: Image of a digital sensor
Images are
transferred from sensors through electrical cables, optical fibers, or wireless
mode, to the processing unit. This processing unit might be a computer or an
electrical card like FPGA, a server or any other kind of image processing card.
This processing unit performs image processing, it does the same job as the
visual cortex. This image processing unit performs a pre- treatment and
enhances the visibility of the useful t. Then it moves on the detection and
processing phases to extract the features of the different elements in the
image. Finally extracted features are analyzed to make a decision.

Figure 11 Screen shot of
Terminator, with identification extracted from « the real augmented reality
terminator vision »
On the image above,
we see that the edges of the face are underlined which can be the result of
pre- treatment on images. It detects the edges of objects to observe.

Figure 12 : Extracted
underlined face d from previous image
The analysis and
extraction steps can give those kinds of results:
-
Distance between eyes = 5cm -
Mouth surface = 3cm²
-
Nose length = 4 cm
-
Nose position = 15cm
After the data
collection (from extracted features) and the processing steps, a decision is
made. (classification or specific action). In this image the decision is shown
as:
-
Gender recognition : male /
female
-
Facial expression recognition:
aggressive / happy / sad / angry / scared

Figure 13: Extraction from figure 11

Figure 14 : Diagram of acquisition and processing line of artificial
vision
In, order to
efficiently use computer vision, you need to know what you want to detect. Then
you choose the best fitting components of your vision system, and the position
and angles between the elements to increase the visibility of features you want
to see. After acquiring the image, the system preprocessed (filtered) and
analyzed it (segmented, extraction of features). The elements in the image are
then classified in order to enable a decision. For us, human beings, this
process happens as followed: “I see something, I acquire information: two eyes,
four legs, height 70 to 80cm at shoulder. Mouth opens and closes quickly, hairs
etc...” This is the feature extraction step. The b rain classifies the elements
and recognizes a dog. “Do I need to run or should I come closer?” This is the decisional
step. . Now, you have an overview of what computer vision is. It is used in way
more fields than the entertainment apps only. In fact, a lot of industries use
it to perform quality controls, to develop self- driving cars, to monitor
public transports and so on. It is also used in the military to build weapons
like the as Samsung automatic machine -gun SGR-A1. The field of computer vision
still has a lot of secrets . We will dig into that later.
Source:
1. http://intelligence-artificielle-tpe.e-monsite.com/album-photos/robots-de-fiction/ns5sonny.html
2. https://www.youtube.com/watch?v=6uPUhqR6zCo
3. https://fr.wikipedia.org/wiki/%C5%92il_humain
4. http://www.mesures.com/pdf/old/828-dossier-Keyence-critere-choix.pdf
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