Seminae

1. Computer vision
Research topic
Student : Nabiyev Nabi
Course : Lumos School of Data
Mentor : Saber MalekzadeH

2. DEFINITION OF RESEARCH topic
2
▪ Computer vision is a field of artificial
intelligence that trains computers to
interpret and understand the visual world.

3. 3
Thanks to advances in
artificial intelligence and
innovations in deep learning
and neural networks, the
field has been able to take
great leaps in recent years
and has been able to
surpass humans in some
tasks related to detecting
and labeling objects.

4. ITISprovedthatcomputervisionis themost
powerfulmanifestationof AI
▪ You probably use computer vision every day and don’t even
think about it. Enjoy checking out the latest Snapchat
filters? That’s computer vision. Unlock your iPhone with your
face? That’s computer vision, too. Use your phone to deposit
your latest paycheck and get some cash in your bank account?
Well, that’s also computer vision.
▪ Computer vision as we know it is at a tipping point. Thanks to
industry-wide development efforts and advances in deep
learning algorithms and graphics processors, we’re doing things
that were unimaginable just a decade ago.
4

5. ▪ The ambitious computer vision projects we have seen in 2018 signify that the technology is
finally catching up with the applications that developers have long yearned to create. It also
means that it will soon get cheaper to develop tailored computer vision applications.
▪ ModiFace, for instance, lets users try on makeup using only their
smartphones. Topology does the same for eyewear. MTailor makes custom-tailored jeans
and shirts using a similar process. Outside of fashion, Pottery Barn lets users see what new
furniture might look like in their homes, and Hover turns users’ pictures of their homes into
fully measured 3D models.
▪ None of these projects is as complicated as self-driving cars and cashierless grocery
stores, but that’s what qualifies the current generation of computer vision products as a
harbinger for massive deployment in the next few years: Once it becomes possible for small
companies to develop functioning computer vision products for a mass audience, the
technology will begin infiltrating almost every part of our lives.
5

6. Hey guys , but
there is a
problem.
6

7. Digital equipment can capture images at
resolutions and with detail that far surpasses
the human vision system. Computers can also
detect and measure the difference between
colors with very high accuracy. But making
sense of the content of those images is a
problem that computers have been struggling
with for decades. To a computer, the above
picture is an array of pixels, or numerical values
that represent colors.
7

8. History of research
8
▪ Computer vision began in earnest
during the 1960s at universities that
viewed the project as a stepping
stone to artificial intelligence. Early
researchers were extremely
optimistic about the future of these
related fields and promoted artificial
intelligence as a technology that
could transform the world.

9. timeline
9
1959 – The first digital image scanner was invented by transforming images into grids of numbers.
1963 – Larry Roberts, the father of CV, described the process of deriving 3D info about solid objects
from 2D photographs.
1966 – Marvin Minksy instructed a graduate student to connect a camera to a computer and have it
described what it sees.
1980 – Kunihiko Fukushima built the ‘neocognitron’, the precursor of modern Convolutional Neural
Networks.
1991-93 – Multiplex recording devices were introduced, together with cover video surveillance for ATM
machines.
2001 – Two researchers at MIT introduced the first face detection framework (Viola-Jones) that works
in real-time.
2009 – Google started testing robot cars on roads.
2010 – Google released Goggles, an image recognition app for searches based on pictures taken by
mobile devices.
2010 – 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.
2012 – Google Brain’s neural network recognized pictures of cats using a deep learning algorithm.
2015 – Google launched open-source Machine learning-system TensorFlow.
2016 – Google DeepMind’s AlphaGo algorithm beat the world Go champion.
2017 – Waymo sued Uber for allegedly stealing trade secrets.

10. 10
Larry Roberts – The father of Computer Vision
Roberts, who was known as Larry, was born and
raised in Westport, Connecticut.He was the son of
Elizabeth (Gilman) and Elliott John Roberts, both of
whom had doctorates in chemistry. During his
youth, he built a Tesla coil, assembled a television,
and designed a telephone network built from
transistors for his parents' Girl Scout camp.
Roberts attended the Massachusetts Institute of
Technology (MIT), where he received
his bachelor’s degree (1959), master's
degree (1960), and Ph.D.(1963),all in electrical
engineering.His Ph.D. thesis "Machine Perception
of Three-Dimensional Solids" is considered as one
of the foundational works of the field of Computer
Vision.

11. Applications of computer vision
11
▪ Video Surveillance
▪ Vision-Based Biometric
Authentication
▪ Digital Documentation
▪ Human Pose Estimation
▪ Image Transformation Using GANs
▪ Computer Vision for Developing
Social Distancing Tools
▪ Creating a 3D Model From 2D
Images
▪ Medical Image Analysis

12. Future
works
▪ 2025 – By this time, regulation in
FR will significantly diverge
between China and US/Europe.
▪ 2030 – At least 60% of countries
globally will be using AI
surveillance technology (it is
currently 43% according to CEIP).
12

13. Future applications of
computer vision
▪ Creating city guides
▪ Powering self-driving cars
▪ Boosting augmented reality
applications and gaming
▪ Organizing one’s visual memory
▪ Brand Monitoring and Expanded
Tagging
▪ Visual Question Answering
▪ Virtual Work Experience
13

14. ▪ What makes computer vision different
▪ Computer vision isn’t like other AI technology. First, computer vision is an entirely new
capability for most organizations, not an incremental improvement to something that others
have tried before, like predictive analytics.
▪ Also, there is no intrinsic barrier for computer vision improving toward human-level
perception. When these algorithms infer information from images, they’re not trying to
predict an intrinsically uncertain future, like lots of other AI does; they’re just identifying a
categorical truth about the present contents of an image or set of images. This means
computer vision will be able to get more accurate over time until it matches — or exceeds —
the abilities of human image recognition.
▪ Finally, computer vision can collect training data much more quickly than other AI tools. Big
data sets require massive investments in training data, but computer vision just needs people
to label pictures and videos accurately — easy stuff. And that’s why computer vision’s
adoption rate has accelerated so much in the recent past.
14

15. Wheretofindself-drivingcars
15
Find more maps at slidescarnival.com/extra-free-resources-icons-and-maps

16. references
▪ https://siliconcanals.com/news/europe-
autonomous-car-market-top-10-countries-
leading-the-race-in-2019/
▪ https://www.popsci.com/self-driving-cars-
cities-usa/
▪ https://artificialintelligence.oodles.io/blogs/f
uture-of-computer-vision/
▪ https://imagga.com/blog/7-image-
recognition-uses-of-the-future/
▪ https://www.verdict.co.uk/computer-vision-
timeline/
▪ https://bdtechtalks.com/2019/01/14/what-
is-computer-vision/ 16

17. Any questions?
You can find me at:
▪ LinkedIn
▪ Telegram
17