Artificial intelligence

1. ARTIFICIAL INTELIGENCE
(A.I)
COMPUTER VISION (C.V)
Presented by: Group 4

2. COMPUTER VISION
Computer vision is a field of science that studies
how computers can improve their understanding
of images and video.
It draws upon the engineering and scientific fields
to develop technologies that can improve
machines perception of objects as humans.

3. DEFINITION (CON’T)
Computer vision is an AI field that enables
systems to collect and interpret visual input for
purposes such as processing and classifying
images.

4. BRIEF HISTORY OF AI COMPUTER VISION
• Computer vision has been around for many years. Its
commercial use was first tested in the 1950s to
distinguish between handwritten text and typed words.
• Scientists and engineers have been working on
developing ways to make machines that can see and
interpret visual data for over 60 years.

5. C.V HISTORY (CON’T)
• In 1974, optical character recognition and intelligent
character recognition were introduced. These two technologies
could recognize text printed on any font or typeface.
• In 2000, the focus of study in object recognition was shifted
to face recognition. By 2001, the first real-world applications
of face recognition emerged. In 2010, the Images data set was
made available.

6. HOW COMPUTER VISION WORKS
• One way to teach a computer how to recognize patterns is to feed it
thousands of images, if not millions, of them. This method allows the
computer to study the various elements that relate to those patterns.

7. APPLICATION OF COMPUTER VISION
•Computer vision is one of the areas in Machine
Learning where core concepts are already being
integrated into major products that we use every
day.

8. COMPUTER VISION IN SELF DRIVING CARS
• Computer vision technology allows self-driving cars to see
clearly in their surroundings. It uses cameras that are
positioned around the vehicle to capture images of roads,
read traffic signs, detect other cars, objects and
pedestrians.

9. FACIAL RECOGNISION
•Computer vision also plays an important role in
facial recognition applications, the technology
that enables computers to match images of
people’s faces to their identities already stored
in a database.

10. COMPUTER VISION IN HEALTH SECTOR
•Computer vision has also been an important
part of advances in health-tech. Computer
vision algorithms can help automate tasks such
as detecting cancerous moles in skin images or
finding symptoms in x-ray and MRI scans.

11. EXAMPLES OF COMPUTER VISION
 Image classification: sees an image and can classify
it (a dog, an apple, a person’s face). More precisely, it
is able to accurately predict that a given image
belongs to a certain class. For example, a social media
company might want to use it to automatically
identify and segregate objectionable images uploaded
by users.

12. Object detection can use image classification
to identify a certain class of image and then
detect and tabulate their appearance in an
image or video. Examples include detecting
damages on an assembly line or identifying
machinery that requires maintenance.

13. •Object tracking follows or tracks an object once
it is detected. This task is often executed with
images captured in sequence or real-time video
feeds. Autonomous vehicles, for example, need
to not only classify and detect objects such as
pedestrians, other cars and road infrastructure,
they need to track them in motion to avoid
collisions and obey traffic laws.

14. Content-based image retrieval uses computer vision
to browse, search and retrieve images from large data
stores, based on the content of the images rather than
metadata tags associated with them. This task can
incorporate automatic image annotation that replaces
manual image tagging. These tasks can be used for
digital assets management systems and can increase the
accuracy of search and retrieval.

15. THE BAR/QR CODE READER

16. FLAT BED SCANNER

17. THE END