2. What Is Computer Vision?
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Computer vision is a branch of AI that studies
how computers can see and understand the
content of digital images and videos.
While humans use retinas, optic nerves, and
dedicated parts of their brains to collect and
process visual information, this process is
completely different in machines.
3. Technological Components,
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Sensors. Cameras and other devices equipped with
specialized sensors are critical to capturing visual data
surrounding us.
Data. Most people are already familiar with image and
video data and their traditional associated formats, such
as .jpg and .png for images and .mov and .avi for videos.
4. Technological Components,
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Algorithms. There are a myriad of techniques and
algorithms computer vision researchers have developed for
cleaning and preparing image data, including filtering,
resizing, or image normalization. Once visual data is
prepared, it’s time for the fun part. Following the rise of
deep learning, we can train powerful deep learning models
that quickly surpass human capabilities in a wide range of
tasks.
5. Applications of Computer Vision
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Object detection
Many popular computer vision applications involve
recognizing things in images. A great example is self-driving
cars. Manufacturers of autonomous cars use multiple
cameras to acquire images from the environment so that
their self-driving cars can detect objects, lane markings,
and traffic signs to safely drive.
6. Applications of Computer Vision
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Facial recognition
Used for security and surveillance, facial recognition
analyzes key features to identify people. This is done by
training neural networks on vast biometrics databases that
allow models to identify unique facial features in humans.
Automatic translation
Tools like Google Translate allow users to point a
smartphone camera at a sign in another language and
almost immediately obtain a translation of the sign in their
preferred language.
7. Applications of Computer Vision
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Image generation
Computer vision applications understand can create
realistic images using generative AI. This is the case of
DALL-E, a genAI model that creates images from text
descriptions, or Sora, which does the same but with videos.
Another example is deep fakes. A deep fake is software that
is used to depict people in fake videos they did not actually
appear in. By understanding what makes up a human face,
deep fakes can generate new faces.
8. Computer Vision in AI
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The unique applications of computer vision we have today
wouldn’t be possible without AI, in particular, deep learning
models. To understand why, we first need to understand
what a digital image is –the most basic unit of information in
computer vision.
A digital image is made up of hundreds, if not thousands of
pixels, which contain information about color and intensity.
In grayscale images, each pixel's intensity can be
represented by a number between 0 and 255.
10. Computer Vision in AI
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By contrast, colored images are generally stored in the
RGB system. RGB stands for Red, Green, and Blue. Each
image can be thought of as being represented by three
rasters, one for each color channel. This means that you
need three times the amount of data to store a color image
compared to a grayscale one.
12. Machine Vision and Computer Vision
Aspect Machine Vision Computer Vision
Definition
Use of cameras, sensors, and
algorithms to analyze images
and make decisions, often in
industrial settings.
A field of AI focused on
enabling computers to
interpret and understand
digital images and videos.
Primary Use
Cases
Quality control, defect
detection, assembly line
monitoring, and robot guidance.
Object detection, facial
recognition, image generation,
autonomous vehicles, and
medical imaging.
