Computer vision analyzes visual data like images and videos to understand and interpret them similarly to humans. It works by training models on large datasets to recognize patterns and classify objects. Applications include face recognition for login, medical imaging analysis, and computer vision in autonomous vehicles. The future of computer vision may involve combining it with natural language processing for image captioning and visual assistance applications.

1. Computer Vision
Slides by Ankit Kamal
Roll Number: 17306
Branch: Information Technology

2. Contents ● Introduction
● How Computer Vision Works
● Brief History of Computer Vision
● Hardware used for Computer Vision
● Tasks performed by using Computer Vision
● Applications of Computer Vision
● Advantages and Disadvantages of Computer
Vision
● Present of Computer Vision
● Future Of Computer Vision
● Conclusion

3. Introduction
● Computer Vision is the field of computer science that focuses on creating
digital systems that can process, analyze, and make sense of visual data
(images or videos) in the same way that humans do
● The concept of computer vision is based on teaching computers to process
an image at a pixel level and understand it.
● Technically, machines attempt to retrieve visual information, handle it, and
interpret results through special software algorithms.

4. How Computer Vision Works

5. How Computer Vision Woks
★ Computer vision technology tends to mimic the way the human brain works. But how does our brain solve
visual object recognition? One of the popular hypothesis states that our brains rely on patterns to decode
individual objects. This concept is used to create computer vision systems.
★ Computer vision algorithms that we use today are based on pattern recognition. We train computers on a
massive amount of visual data—computers process images, label objects on them, and find patterns in
those objects. For example, if we send a million images of flowers, the computer will analyze them,
identify patterns that are similar to all flowers and, at the end of this process, will create a model “flower.”
As a result, the computer will be able to accurately detect whether a particular image is a flower every
time we send them pictures.
★ Golan Levin, in his article Image Processing and Computer Vision, provides technical details about the
process that machines follow in interpreting images.
○ In short, machines interpret images as a series of pixels, each with their own set of color values.
For example, below is a picture of Abraham Lincoln. Each pixel’s brightness in this image is
represented by a single 8-bit number, ranging from 0 (black) to 255 (white). These numbers are
what software sees when you input an image. This data is provided as an input to the computer
vision algorithm that will be responsible for further analysis and decision making.

6. How Computer Vision Woks
Color values of individual pixels are converted into a simple array of numbers used as input for a computer vision algorithm.

7. Brief history of Computer Vision
● 1959 – The first Digital Image Scanner was invented by transforming images into grids of numbers.
● 1966: Marvin Minksy instructed a graduate student to connect a camera to a computer and have it described what it
sees.
● 1990: Face recognition; statistical analysis came in picture
● 2000: Broader recognition, large annotated datasets available; video processing starts
● 2001 – Two researchers at MIT introduced the first face detection framework (Viola-Jones) that works in real-time.
● 2010 –
○ Google released Goggles, an image recognition app for searches based on pictures taken by mobile devices
○ To help tag photos, Facebook began using facial recognition.
● 2011 – Facial Recognition was used to help confirm the identity of Osama bin Laden after he is killed in a US raid.
● 2017 – Apple released the iPhone X in 2017, advertising Face Recognition as one of its primary new features.
● 2019 – The Indian government announced a Facial Recognition plan allowing police officers to search images
through mobile app.
● 2020 – Intel launched the Intel Xe graphics card pushing into the GPU market.
● 2030 – At least 60% of countries globally will be using AI surveillance technology (it is currently 43% ).

8. Hardware Used for
Computer Vision
● A power source
● At least one image acquisition device (camera, ccd, etc.),
● A processor
● Control and communication cables
Or
Some kind of wireless interconnection mechanism
● Software
● Display in order to monitor the system
Can also Include-
● Hardware with active illumination or something other
than visible light or both
○ Such as structured-light 3D scanners, thermographic
cameras, radar imaging, magnetic resonance images, etc.

9. Tasks performed by
using Computer Vision
● Recognition:The classical problem in computer
vision, image processing, and machine vision is that of
determining whether or not the image data contains
some specific object, feature, or activity.
● Motion Analysis:Several tasks relate to motion
estimation where an image sequence is processed to
produce an estimate of the velocity either at each
points in the image or in the 3D scene, or even of the
camera that produces the images.
● Scene Reconstruction:Given one or (typically) more
images of a scene, or a video, scene reconstruction
aims at computing a 3D model of the scene
● Image Restoration:The aim of image restoration is
the removal of noise (sensor noise, motion blur, etc.)
from images

10. Applications Of Computer Vision
● Face detection
● Login without a password.
● Special effects: Shape Capture and Motion Capture
● Smart cars
● Vision in space
● Medical Imaging

11. Face Detection
● Many new digital cameras
now detect faces
○ Eg. Canon, Sony, Fuji,

12. Login without a password.
Face recognition systems now
beginning to appear more
widely like Windows Hello
Fingerprint scanners on many
new laptops and other devices

13. Special effects: Shape Capture and Motion Capture
Shape Capture
Matrix Movie
Motion Capture: Pirates of The Caribbean Movie

14. Smart Cars
● Vision systems
currently in high-end
BMW, Tesla etc.
● By 2010: 70% of car
manufacturers.

15. Vision in space NASA'S Mars Exploration Rover Perseverance captured this

16. Medical Imaging
● Computer vision can process pictures of the
back of the eye (see left ) and rate them for
disease presence and severity.
● Computer vision algorithms can be used to
process retinal fundus photographs to screen for
diabetic retinopathy.
● The computer vision algorithm successfully identifies the tumor
region (bright green) and is not confused by the normal areas
that look like tumors.
● Applying computer vision technology during a lymph node
biopsy can help detect the tumor region.

17. Advantages and Disadvantages of Computer Vision
Advantages
● Faster and simpler process - Computer vision
systems can carry out repetitive and
monotonous tasks at a faster rate, which
simplifies the work for humans.
● Better products and services - Computer
vision systems that have been trained very
well will commit zero mistakes. This will
result in faster delivery of high-quality
products and services.
● Cost-reduction - Companies do not have to
spend money on fixing their flawed processes
because computer vision will leave no room
for faulty products and services.
Disadvantages
● Lack of specialists - Companies need to have
a team of highly trained professionals with
deep knowledge of the differences between
AI vs. Machine Learning vs. Deep Learning
technologies to train computer vision
systems. There is a need for more specialists
that can help shape this future of technology.
● Need for regular monitoring - If a computer
vision system faces a technical glitch or
breaks down, this can cause immense loss to
companies. Hence, companies need to have a
dedicated team on board to monitor and
evaluate these systems.

18. Present of Computer Vision
● Computer vision technology of today is powered by deep learning algorithms that use a special
kind of neural networks, called convolutional neural network (CNN), to make sense of images.
● These neural networks are trained using thousands of sample images which helps the
algorithm understand and break down everything that’s contained in an image.
● These neural networks scan images pixel by pixel, to identify patterns and “memorize” them.
● It also memorizes the ideal output that it should provide for each input image (in case of
supervised learning) or classifies components of images by scanning characteristics such as
contours and colors.
● This memory is then used by the systems as the reference while scanning more images.
● With every iteration, the AI system becomes better at providing the right output.

19. Future Of Computer Vision
● With further research on and refinement of the technology, the future of computer vision
will see it perform a broader range of functions.
● Not only computer vision technologies will be easier to train but also be able to discern
more from images than they do now.
● Image captioning applications can be combined with natural language generation (NLG)
to interpret the objects in the surroundings for visually challenged people.
● The future of computer vision will pave the way for artificial intelligence systems that are
as human as us.

20. Conclusion Computer vision is a popular topic in articles about new
technology.A different approach to using data is what makes
this technology different.
Tremendous amounts of data that we create daily, which
some people think as a curse of our generation, are actually
used for our benefit—the data can teach computers to see
and understand objects.
This technology also demonstrates an important step that
our civilization makes toward creating artificial intelligence
that will be as sophisticated as humans.