Computer vision is a field of artificial intelligence that uses digital images and deep learning to teach machines to interpret and understand visual input. Early experiments in computer vision in the 1950s used neural networks to detect edges and classify simple shapes, while the 1970s saw the first commercial application in optical character recognition. Today, computer vision can perform tasks like facial recognition, object detection in images and video, and image segmentation, classification, and analysis that rival and exceed human visual abilities. Computer vision works by acquiring an image, processing it through machine learning models, and understanding what is depicted to take appropriate actions.

1. A R T I F I C I A L
I N T E L L I G E N C E
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2. K A S H A F N A
Z
B A T C H 1 8
R O L L # 0 2 5

3. C O M P U T E
R V I S I O N

4. COMPUTER VISIO
N
• Computer vision is a field
of artificial intelligence that
trains computers to interpret
and understand the visual
world. Using digital images
from cameras and videos
and deep learning models,
machines can accurately
identify and classify objects —
and then react to what they
“see.”
COMPUTER VISION

5. History of computer vision
• Early experiments in
computer vision took place in
the 1950s, using some of the
first neural networks to detect
the edges of an object and to
sort simple objects into
categories like circles and
squares.

6. History of computer vision
• In the 1970s, the first
commercial use of computer
vision interpreted typed or
handwritten text using optical
character recognition.

7. C O M P U T E
R V I S I O N
I N
T O D A Y ’ S
W O R L D

8. Computer
vision in
today’s world
• From recognizing faces to
processing the live action of
a football game, computer
vision rivals and surpasses
human visual abilities in
many areas.

9. How computer
vision works
• Computer vision works in three basic steps:
• Acquiring an image
• Images, even large sets, can be acquired in real-time
through video, photos or 3D technology for analysis
• Processing the image
• Ep learning models automate much of this process, but
the models are often trained by first being fed thousands
of labeled or pre-identified images.
• Understanding the image
• The final step is the interpretative step, where an object
is identified or classified.
• .

10. Today’s AI system
• Today’s AI systems can go a step further and
take actions based on an understanding of the
image. There are many types of computer
vision that are used in different ways:
• Image segmentation partitions an image into
multiple regions or pieces to be examined
separately.
• Object detection identifies a specific object in
an image. Advanced object detection
recognizes many objects in a single image: a
football field, an offensive player, a defensive
player, a ball and so on. These models use an
X,Y coordinate to create a bounding box and
identify everything inside the box.
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11. Today’s AI
• Facial recognition is an advanced type of object
detection that not only recognizes a human face
in an image, but identifies a specific individual.
• Edge detection is a technique used to identify
the outside edge of an object or landscape to
better identify what is in the image.
• Pattern detection is a process of recognizing
repeated shapes, colors and other visual
indicators in images.
• Image classification groups images into
different categories.
• Feature matching is a type of pattern detection
that matches similarities in images to help
classify them.
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12. T H A N K
Y O U