Computer vision is the field of artificial intelligence that teaches machines to understand the visual world similarly to humans. It has progressed significantly in recent years due to advances in deep learning techniques. Computer vision algorithms are trained on large datasets to recognize patterns and identify objects. It is used in applications like smartphone cameras, web search, self-driving cars, medical imaging, and more. However, computer vision still faces challenges in matching human-level visual recognition abilities.

1. Computer Vision
Yusif Aghalarli 672.7E

2. What is Computer Vision?
Computer vision has been around for more than 50 years, but recently, we see a
major resurgence of interest in how machines ‘see’ and how computer vision can be
used to build products for consumers and businesses.

3. Artificial Intelligence
The key driving factor behind all these is Computer Vision. In the simplest terms,
Computer Vision is the discipline under a broad area of Artificial Intelligence which
teaches machines to see. Its goal is to extract meaning from pixels.

4. A brief history
In the summer of the year 1966, Seymour Papert and Marvin Minsky at MIT Artificial
Intelligence group started a project titled Summer Vision Project. The aim of the
project was to build a system that can analyze a scene and identify objects in the
scene.
In the 70s, taking ideas from studies of the cerebellum, hippocampus and cortex for
human perception, David Marr, a neuroscientist at MIT, set up the building blocks for
the modern Computer Vision and thus is known as the father of the modern
Computer Vision.

5. Deep Vision
Deep Learning has taken off since 2012. Deep learning is a subset of machine learning
where artificial neural networks, algorithms inspired by the human brain, learn from
large amounts of data. Powering recommender systems, identify and tags friends in
photos, translate your voice to text, translate text into different languages, Deep
Learning has transformed Computer vision leading towards superior performance.

6. How does computer vision work?
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.”

7. How does computer vision work?
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

8. Deep learning revolution
To understand the recent process of computer vision technology, we need to dive into
algorithms this technique relies on. Modern computer vision relies on deep learning,
a specific subset of machine learning, which uses algorithms to glean insights from
data.

9. Deep Learning
Deep learning represents a more effective way to do computer vision—it uses a
specific algorithm called a neural network. The neural networks are used to extract
patterns from provided data samples. The algorithms are inspired by the human
understanding of how brains function, in particular, the interconnections between the
neurons in the cerebral cortex.

10. Applications
Smartphones: QR codes, computational photography (Android Lens Blur, iPhone
Portrait Mode), panorama construction (Google Photo Spheres), face detection,
expression detection (smile), Snapchat filters (face tracking)

11. Applications
Web: Image search, Google photos (face recognition,
object recognition, scene recognition, geolocalization
from vision)
VR/AR: Outside-in tracking (HTC VIVE), inside out
tracking (simultaneous localization and mapping,
HoloLens), object occlusion (dense depth estimation)

12. Applications
Insurance: Claims automation, Damage
analysis, Property inspection
Medical imaging: CAT / MRI reconstruction, assisted diagnosis,
automatic pathology, connectomics, AI-guided surgery

13. Self-driving cars
Computer vision enables cars to make sense of
their surroundings. A smart vehicle has a few
cameras that capture videos from different
angles and send videos as an input signal to the
computer vision software. The system
processes the video in real-time and detects
objects like road marking, objects near the car
(such as pedestrians or other cars), traffic
lights, etc

14. Challenges
Even after a huge amount of work published, Computer vision is not solved. It works
only under few constraints. One main reason for this difficulty is that the human
visual system is simply too good for many tasks e.g.- face recognition .A human can
recognize faces under all kinds of variations in illumination, viewpoint, expression, etc.
which a computer suffers in such situations.

15. Conclusion
Computer vision is a popular topic. 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.