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In this video, we’ll look at how vision works in humans and, in comparison, learn how it works in computers.
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[MUSIC]
0:00
Before learning how computer vision works,
0:03
it's helpful to have an understanding
of how vision works in humans.
0:06
[MUSIC]
0:09
Human vision might seem simple,
but it's a highly complex and
0:12
dynamic system involving a coordinated
process between the eyes and the brain.
0:15
This process integrates information
from both eyes for depth perception,
0:20
adjusts for varying light conditions, and
recognizes patterns, all in real time.
0:25
To understand the mechanics
behind human vision,
0:31
imagine you are in a park and
you see a dog playing fetch.
0:34
As you focus your attention on the dog,
0:38
your eyes transform into
remarkable visual sensors.
0:40
[MUSIC]
0:43
Think of your eye as a camera.
0:47
What happens when you look
at the dog playing fetch?
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Light rays bounce off the dog,
and they enter your eye.
0:53
In the eye, the cornea, which is like a
clear window, helps focus the light rays.
0:58
The light rays pass through the pupil,
1:05
which is the dark dot in
the center of your eye.
1:07
The size of the pupil changes based on how
much light is around, and the iris, which
1:10
is the colored part of your eye, regulates
the amount of light that enters the eye.
1:15
Behind the pupil is the lens,
which adjusts its shape to further focus
1:20
the light onto the back of the eye,
a region called the retina.
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The retina converts light into
electrical signals that travel through
1:29
the optic nerve to the brain.
1:34
The brain then processes
the electrical signals,
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extracting crucial information and
weaving it together,
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letting you recognize and understand
that what you're looking at is a dog.
1:42
Your brain integrates the visual
details with your prior knowledge and
1:47
experiences forming a meaningful
perception of the dog in your mind.
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In this analogy,
your eyes act as a camera,
1:55
the dog steals the spotlight
as the subject,
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and how you view and recognize it reflects
the teamwork between the eyes and brain.
2:01
[MUSIC]
2:05
Now that you understand how human vision
works, let's look at how computer
2:09
vision enables computers to interpret
information from visual data.
2:14
Both human vision and computer vision
rely on the principle of receiving and
2:19
processing visual information.
2:24
In human vision, our eyes capture
light rays reflected from objects and
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convert them into electrical signals.
2:30
In computer vision, cameras act as eyes,
2:32
capturing light to
generate digital images.
2:35
[MUSIC]
2:38
In human vision,
the brain processes electrical signals,
2:40
allowing us to recognize objects,
gauge depth, and perceive motion.
2:44
In computer vision, algorithms
process digital images to
2:49
detect objects, patterns,
and other relevant features.
2:54
In human vision, as we grow, we learn
to recognize countless objects and
2:59
scenes based on our experiences.
3:03
And over time,
this learning allows us to quickly and
3:06
accurately interpret new visual scenarios.
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In computer vision,
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machine learning models are trained
on vast amounts of labeled data.
3:14
The more data the models are exposed to,
the better they become at making quick and
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accurate recognitions and predictions.
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In human vision, when we interpret
a scene such as seeing a friend,
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we decide on an action like waving hello.
3:32
Similarly, in computer vision,
upon interpreting an image,
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such as recognizing a pedestrian,
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the system decides on an action, like
instructing an autonomous vehicle to stop.
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These analogies might simplify the
intricacies of human and computer vision,
3:49
but they underline
the key similarities and
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show how computers aim to
replicate human visual skills.
3:56
Computer vision focuses on enabling
computers to capture visual data and
4:02
extract meaningful information from it,
just like we do with our eyes and brains.
4:07
But how exactly does computer
vision achieve this?
4:12
A series of steps collectively describing
the typical process flow in computer
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vision is often referred to as
the computer vision pipeline.
4:24
Image acquisition is the initial
step where an image or
4:28
video is captured by a camera, scanner,
sensor, or other imaging device.
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The pre-processing step is next.
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Once acquired,
the image might need to be enhanced or
4:40
modified to improve its quality.
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Techniques such as filtering
gray-scale conversion and
4:45
contrast adjustment can be applied.
4:49
In the feature extraction step,
the system identifies and
4:52
extracts important details from the image,
such as edges, textures, and shapes.
4:56
During detection and recognition,
the system uses the details
5:01
identified during the feature extraction
step to pinpoint specific objects,
5:05
patterns, or features within the image.
5:10
For instance, after extracting edges,
corners, or textures from an image,
5:13
the system can then piece these
features together to recognize more
5:18
complex structures like buildings or
faces.
5:22
The last step is post-processing and
interpretation.
5:24
After detection, the system interprets the
recognized patterns to make decisions or
5:29
further analysis.
5:34
For example, after recognizing a face,
5:36
the system might associate it with
a specific individual in a database.
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Together, the steps seem to mimic
the human process of seeing, recognizing,
5:44
and understanding visual data.
5:49
[MUSIC]
5:51
As you now understand,
computer vision mimics the way our
5:54
eyes capture visual data and
our brains make sense of it all.
5:58
Through a series of computational steps,
computer vision extracts meaningful
6:02
information for analysis and
decision-making,
6:06
allowing computers to understand and
interpret the visual world around us.
6:09
[MUSIC]
6:14
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