How Computer Vision Applications Work

Summary

The video script delves into the realm of computer vision, a technology that enables machines to interpret the visual world with remarkable accuracy, often surpassing human capabilities. It explains how artificial neural networks, inspired by the human brain's neural connections, are used to recognize patterns and features in images. The script covers various applications of computer vision, from aiding autonomous vehicles in navigation and safety to detecting damage from natural disasters using satellite imagery. It also touches on the transformative impact of computer vision in retail with Amazon Go's just walk out technology and the potential of improving road safety with self-driving cars. The video highlights the power of convolutional neural networks in identifying specific features within images and the importance of training these models with vast amounts of data. However, it also raises ethical concerns about privacy, misuse of technology, and the need for regulations to ensure that advancements align with ethical standards.

Takeaways

• 🐾 **Image Recognition Basics**: Just like humans, machines use neural networks to recognize patterns and differentiate between objects like cats and dogs.
• 🧠 **Artificial Neural Networks**: These advanced algorithms mimic the human brain's neural connections to process information without direct human input.
• 🚗 **Autonomous Vehicles**: Image recognition technology helps self-driving cars avoid collisions and interpret road signs, enhancing road safety.
• 🏥 **Medical Applications**: Computer models can locate tumors in MRI images with high accuracy, assisting in medical diagnostics.
• 🌐 **Satellite Imagery**: AI is used to analyze satellite images, such as assessing damage from natural disasters like the Woolsey fire.
• 🛒 **Retail Innovation**: Amazon Go stores utilize computer vision to track customer movements and purchases, creating a seamless shopping experience.
• 👥 **Facial Recognition**: This technology is used for identity verification and finding missing persons, but also raises privacy concerns.
• 🛍️ **Offline Shopping Transformation**: Computer vision is changing traditional shopping by processing visual data to save time and provide insights.
• 📈 **Convolutional Neural Networks (CNNs)**: Specifically designed for image data, CNNs are adept at identifying key features of objects within images.
• 🧑‍🤝‍🧑 **Training Neural Networks**: Machines learn to recognize patterns by being shown numerous labeled examples, similar to how humans learn.
• ✈️ **Aviation and Autonomous Tech**: Companies like Airbus are using computer vision to enable autonomous navigation in aircraft, improving efficiency and safety.

Q & A

How does image recognition technology help autonomous vehicles?

Image recognition technology enables autonomous vehicles to avoid pedestrians and other cars, as well as react to road signs, allowing them to navigate safely and efficiently.

What is the accuracy rate of computer models in locating tumors in MRI images?

Computer models can locate tumors in MRI images with up to 90 percent accuracy.

How do artificial neural networks differ from human neural connections?

Artificial neural networks are advanced machine learning algorithms that use mathematical computations to process data, whereas human neural connections form through electrical impulses and are shaped by experiences.

What is the role of weights in a neural network?

Weights in a neural network determine the importance of connections between nodes. They influence how much one node affects another, helping the network to focus on the most relevant data for a given task.

How does Amazon Go utilize computer vision technology?

Amazon Go uses computer vision to track customers and their actions within the store, along with monitoring the inventory, to create a 'just walk out' shopping experience without traditional checkouts.

What are convolutional neural networks (CNNs) and how are they used in image recognition?

Convolutional neural networks are a type of neural network specifically designed for two-dimensional image data. They are adept at identifying important features of objects within images by applying specific filters to highlight these features.

How do neural networks learn the appropriate weights for image recognition?

Neural networks learn the appropriate weights during a training process where they are fed a large number of labeled images. Through a feedback loop, the system adjusts the weights to minimize errors and improve accuracy.

How does Airbus use computer vision in their autonomous technology for aircraft?

Airbus uses computer vision in combination with cameras and sensors to enable aircraft to navigate the runway independently, take off at the appropriate time, and assist pilots during taxiing, takeoffs, and landings.

What is the potential impact of self-driving cars on road safety?

Self-driving cars, with their tireless and watchful autopilots, are projected to significantly improve road safety by reducing the number of accidents caused by human error.

How has facial recognition been used by Interpol since its launch in 2016?

Since the launch of their facial recognition system, Interpol has identified nearly 1500 terrorists, criminals, persons of interest, and missing people.

What ethical concerns are associated with the use of computer vision technology?

Ethical concerns with computer vision include privacy invasion, misuse of personal data, potential for misclassification of marginalized groups, and the possibility of deep fakes causing harm.

What is the proposed solution to balance the progress of computer vision technology with ethical considerations?

The proposed solution is to establish regulations and rules to ensure that technological progress is accompanied by ethical practices, protecting individual privacy and preventing misuse.

Outlines

00:00

😺 Understanding Machine Vision: From Cats to Complex Tasks

The first paragraph introduces the concept of machine vision by comparing it to how humans recognize objects like cats. It explains that just as children learn to differentiate between animals by exposure, machines use artificial neural networks to understand the visual world. This technology is crucial for applications like autonomous vehicles, medical imaging, and insurance claim assessments. The paragraph also touches on the use of AI in analyzing satellite imagery to assess damage from natural disasters and the role of neural networks in processing visual data through layers of interconnected nodes.

05:01

🛒 Computer Vision in Retail: Amazon Go and Beyond

The second paragraph delves into how computer vision is transforming the retail industry, particularly with the advent of Amazon Go stores. These stores utilize computer vision to track customer movements and product handling, creating a seamless shopping experience. The paragraph also discusses the broader implications for offline shopping, where visual data can be processed to save time, money, and provide a competitive edge. It further explains the use of convolutional neural networks (CNNs) for image recognition tasks, which are adept at identifying features of objects within images, and the importance of training these networks to recognize specific patterns.

10:01

🚗 Autonomous Vehicles and the Ethical Considerations of Computer Vision

The third paragraph discusses the application of computer vision in autonomous vehicles, highlighting how companies like Airbus and Tesla are using this technology to improve safety and efficiency on the road. It mentions the reduction in accidents with Tesla's autopilot feature and the potential for self-driving cars to significantly enhance road safety. The paragraph also addresses the ethical considerations of facial recognition technology, including its use in tracking criminals and missing persons, as well as the potential for misuse and the importance of balancing technological progress with privacy concerns.

Keywords

💡Image Recognition

Image recognition is a technology that enables machines to identify and categorize objects within an image. In the video, it is highlighted as a crucial component in various applications, such as autonomous vehicles avoiding pedestrians and cars, and in medical imaging for locating tumors with high accuracy. It is a fundamental aspect of computer vision, which is the focus of the video.

💡Artificial Neural Networks

Artificial neural networks (ANNs) are advanced machine learning algorithms inspired by the human brain's neural networks. They are used to process complex data patterns without explicit programming for each task. In the context of the video, ANNs are essential for machines to learn and interpret visual data, such as recognizing damage in satellite imagery post-disaster or identifying objects in images.

💡Convolutional Neural Networks (CNNs)

Convolutional neural networks are a type of ANN specifically designed for processing two-dimensional image data. They are adept at identifying and localizing features within images. The video explains that CNNs work by applying filters to pixel patches to detect patterns, which is vital for tasks like facial recognition and object detection in various applications, including retail and security.

💡Computer Vision

Computer vision is the field of study that enables computers to interpret and understand visual information from the world, similar to how human vision works. The video discusses its applications in various sectors, such as retail with Amazon Go stores, healthcare with MRI image analysis, and transportation with autonomous vehicles, emphasizing its transformative impact on technology and society.

💡Feature Extraction

Feature extraction is the process of identifying and extracting useful information or patterns from raw data. In the context of the video, it refers to how neural networks identify and focus on specific aspects of visual data that are important for the task at hand, such as detecting damage in satellite images or recognizing faces.

💡Deep Learning

Deep learning is a subset of machine learning that involves the use of neural networks with multiple layers to analyze various factors of data. The video illustrates how deep learning models are trained using vast amounts of labeled data, such as chest X-rays, to learn and make accurate predictions, like identifying pneumonia from medical images.

💡Autonomous Vehicles

Autonomous vehicles, also known as self-driving cars, are a key application of computer vision and image recognition. The video mentions how these vehicles use these technologies to avoid pedestrians and other cars, as well as to interpret road signs, showcasing the potential for increased road safety and efficiency in transportation.

💡Facial Recognition

Facial recognition is a technology that allows for the automated identification of individuals based on their facial features. The video discusses its use in various positive contexts, such as identifying criminals and missing persons, but also raises concerns about privacy and potential misuse, highlighting the need for ethical considerations in technology development.

💡Ethics in Technology

The video touches on the ethical implications of computer vision and AI technologies, emphasizing the importance of balancing technological progress with ethical standards. It suggests that while these technologies offer numerous benefits, there is a need for regulations to prevent misuse and protect individual privacy and rights.

💡Data Processing

Data processing refers to the manipulation and analysis of data, which is a fundamental part of how machines 'see' and interpret visual information. In the video, it is mentioned in the context of analyzing satellite imagery for insurance claims and using computer vision to track products in retail stores, illustrating the efficiency gains over manual methods.

💡Training Neural Networks

Training neural networks involves feeding them data and adjusting the network's weights and biases through a feedback loop until the desired accuracy is achieved. The video uses the example of training a model to recognize pneumonia from X-ray images, highlighting the iterative process of learning from data and improving over time.

Highlights

Image recognition technology enables autonomous vehicles to avoid pedestrians and other cars, as well as react to road signs.

Computer models can locate tumors in MRI images with up to 90 percent accuracy.

Artificial neural networks are used to answer questions and evaluate data, such as assessing damage in satellite imagery post-disaster.

Neural networks consist of layers, including input, output, and hidden layers where computations occur.

Each neuron in a neural network performs a computation, receiving data as numerical values representing features of the input data.

Amazon Go stores utilize computer vision to track items and customers, creating a checkout-free shopping experience.

Convolutional neural networks are specifically designed for image recognition tasks and excel at locating important features of objects.

Computer vision can process visual data to save time, money, and provide competitive advantages in various industries.

Neural networks learn weights during the training process by comparing the resulting output to the desired outcome and adjusting accordingly.

Airbus is using computer vision to develop autonomous technology for commercial flights, aiding in taxiing, takeoffs, and landings.

Self-driving Tesla cars with autopilot are projected to be four times safer than human-driven cars.

Facial recognition systems have been used to identify criminals, persons of interest, and missing persons with high accuracy.

The ethical dilemma of computer vision technology includes privacy concerns and the potential for misuse.

Regulations and rules are necessary to ensure that the progress of computer vision technology aligns with ethical standards.

Children's ability to differentiate between animals like cats and dogs improves with exposure to different breeds, colors, and sizes.

Machines use artificial neural networks to learn and recognize patterns, similar to the human brain's neural connections.

Tractable uses AI to study photos taken by insurance clients to evaluate reimbursement amounts for disaster damage.

NVIDIA's demonstration of using image recognition on satellite imagery to detect destruction from the Woolsey fire in 2018.

Transcripts

this is a cat

you know that

everyone watching this video knows that

even a toddler will point to this photo

and call it a cat

well not necessarily

sometimes children mix up cats and dogs

especially if they weren't exposed to

different breeds or colors or sizes of

animals before but as soon as they've

seen enough cats and dogs and other

furry friends they've learned the

difference

we apply similar logic when helping

machines understand the visual world

this technology helps autonomous

vehicles avoid pedestrians and other

cars as well as react to road signs and

that's how computer models can locate

tumors in mri images with up to 90

percent accuracy

the image recognition skill allows

computers to process more information

than the human eye often faster and more

accurately or simply when people are not

involved in looking

so how can machines see and interpret

the visual world

well let's talk about computer vision

and its applications

[Music]

in the first three years of our lives we

create one million neural connections

per second whenever we learn something

new a neuron in our brain lights up with

an electrical impulse and sends the

message about a new experience to other

neurons forming connections everything

we know is shaped by these neural

connections networks of them whenever we

see a new type of cat the same

connection strengthens making it easier

for us to recognize an animal as a cat

next time

with machines we use similar neural

networks but those networks are

artificial

just a disclaimer we will be using

simplified explanations and formulas to

make concepts more digestible

an artificial neural network is an

advanced machine learning algorithm that

can answer questions without any hints

from humans questions like how much will

it cost to fix a car or a roof after the

disaster tractable uses ai to study the

photos taken by insurance clients to

evaluate how much should be reimbursed

and in this demo nvidia applies image

recognition to satellite imagery to

detect destruction of homes in the

woolsely fire in 2018.

the neural network looks for specific

features that it knows must represent

damage and it does so by doing math

a neural net consists of many layers an

input layer that receives the data

the output layer that makes the final

forecast

and the hidden layers where the magic

happens

layers consist of nodes neurons that are

all interconnected each neuron is

performing a computation

just like biological neurons receive

signals about the world artificial ones

receive data

the difference it's represented in a

format understood by computers numbers

for images it's the color values of the

pixels composing the image

neurons in the input layer are not that

important because they simply receive

data and pass it on so let's focus on

this node in one of the hidden layers

connected to many nodes in the previous

layer it accepts one number from each

node but it doesn't treat all these

connections equally they have different

importance or weights also represented

as numbers the higher the weight the

more influence one node has on another

so a node multiplies inputs by their

corresponding weights and then adds them

all up to create a single number this

number will become an input for the

nodes in the next layer

so the weights basically help the net

look for the data that carries the

greatest importance for the task and

layer upon layer recognize more of the

details until the last node puts it all

together and says here are all the areas

hit by hail

yes an experienced evaluator can also do

this kind of job manually but they will

have to travel to the affected zone and

inspect large areas immensely extending

the claim processing time but a computer

can analyze data from photos without

workers moving around and wasting

precious time

of course it doesn't have to be just

static images amazon incorporated neural

networks to develop one of its most

groundbreaking products amazon go stores

every part of their unique just walk out

experience is run by computer vision

cameras and sensors keep track of every

person entering the store and what

they're doing whether picking up an item

or returning it to the shelf at the same

time every product sold at the store is

tracked by cameras too is it on the

shelf or in someone's hand

and finally those two instances are

combined to answer the ultimate question

which person took what product

with almost 30 locations in the u.s and

now a shop in london the automated

stores are no longer a concept but the

retail reality as amazon starts

providing the technology to whole foods

tesco and starbucks

this way computer vision is transforming

the way offline shopping works any

visual data that can provide useful

information can be processed by machines

saving time money and providing

competitive advantage

this was the basic principle that all

neural nets work by but they can be

different too

for image recognition tasks

convolutional neural networks are used

the most they were specifically created

to work with two-dimensional image data

and are extremely good at locating

important features of objects

here's how convolutional neural networks

work

computers see images as grids of pixels

as you may know each pixel is

represented and encoded as a combination

of red green and blue of different

intensity here's what a computer sees

when you input this picture

let's switch it to grayscale and reduce

the resolution for simplicity

from this color intensity map alone the

computer must determine how many cars

are parked in the parking lot this

information can be used to optimize

price or redirect traffic to less

populated areas

this will get a bit math heavy so be

prepared

in a convolutional neural network we do

almost the same thing the normal neural

network does but instead of multiplying

one pixel value by its weight we

multiply a patch of pixels by a set of

weights called a filter or a kernel

this is done because in images the

difference between neighboring pixel

values is important pixels with low

values are darker while high values are

lighter if the contrasting pixels are

located close together it's a good

indication that we're looking at an

object's outline

so we multiply this patch of pixels by

the filter and receive one pixel

the filter moves across the image patch

by patch until it transforms the whole

picture so the convolutional neural net

not only extracts value from the image

but also reduces its dimension making it

easier to process

depending on what features we want the

machine to find we apply a specific

filter

they're like templates helping bring out

needed features

this one can pinpoint the outlines other

kernels can filter out horizontal or

vertical lines there are kernels for

finding faces and separate face features

okay but how does a computer know which

weights to multiply by we're glad you're

catching on because this is kind of a

crucial part neural networks learn those

weights during the training process

want to know how to train a deep

learning model

let's start with something familiar how

do humans see

well to be honest we don't know for sure

how our brains apply meaning to the

visuals around us the most popular

theory is pattern recognition it states

that we rely on patterns or features to

figure out what objects we're seeing

a cat has its set of characteristics a

long tail

fur and big pointy ears to start with

but we don't teach our children about

cats by listing all those

characteristics to them we show them

pictures because it's easier and allows

for much more flexibility

this and this are both cats even though

they're different in many ways but a

child is capable of catching all the

things features that are common in them

so we do the same thing with machines we

show them pictures tell them what's in

them and hope they figure out all the

important features by themselves

say you want to build an app that will

calculate the probability of pneumonia

from x-ray images similar to the one we

prototype for decision making in the

hospital setting

as we already established you can't

simply tell the computer what disease

looks like on a radiograph moreover you

want to distinguish between different

conditions

so the best approach is to give the

computer a vast amount of labeled chest

x-rays to extract valuable features on

its own

this is one of over 112 000 training

images we use for our pneumonia scenario

its pixel map is fed to the input layer

which performs convolution except this

time all filter values are random

completely incidental the results are

passed to outgoing layers where they're

multiplied and added many times before

arriving at the output layer there the

resulting output is compared to the

desired one in case of an error the

system starts tweaking weights over and

over until it gives a correct result the

process of training is basically finding

appropriate weights from the constant

feedback loop

this video might not look like much but

here an aerospace manufacturer airbus is

making aviation history in this

demonstration they present a new

autonomous technology that will soon

assist pilots of commercial flights in

taxiing takeoffs and landings

notice the pilot nervously hovering a

hand over the stick while the aircraft

soars into the sky on its own accord

airbus uses cameras sensors and software

powered by computer vision to let the

jet navigate the runway independently

and take off at the appropriate time in

harsh weather conditions pilots will

have to manually correct the plane but

in ideal circumstances like this they

can delegate such operations to the

computer and get busy with navigational

tasks and communication

on the ground the transformation

promises to be even more drastic

currently over 6 000 pedestrians die in

traffic accidents every year in the u.s

a number that increased by almost 50

percent since 2010. over 90 percent of

collisions happen due to drivers getting

distracted

but if we remove human error from the

equation we can significantly improve

road safety

in the first quarter of 2021 tesla

vehicles operating with autopilot became

engaged in a single road accident for

every 4.19 million miles driven in the

same period tesla cars not using

autopilot experienced one accident for

every 978 000 miles traveled so it seems

like self-driving tesla cars can be four

times safer than the ones operated by a

regular human driver even though people

are wary of autonomous cars stats speak

for themselves tireless and watchful

autopilots are projected to make roads

safer and more walkable as we inevitably

move towards the autonomous future

one of the most popular uses of computer

vision is facial recognition the unique

architecture of our faces allows

machines to perform fantastically

accurate face matching to validate our

identity or find missing persons

since the launch of their facial

recognition system in 2016 interpol

identified almost 1500 terrorists

criminals persons of interest and

missing people

these applications are our triumph in a

decades-long effort to make computers

see the same way we do

that said it's not always that positive

it's one thing when technology tracks

humans on the road but a completely

different one when it recognizes their

identities logs details about their

personal lives or classifies

marginalized groups to mistreat them

deep fakes can be entertaining in one

context while harmful and destructive in

others

shockingly accurate face search engines

are available both to those who want to

stop misuse of their pictures and to

those who want to stalk other people

this is a common dilemma that almost

every technology becomes subject to

should we stall the progress to protect

our privacy it seems that computer

vision has advanced too far and promises

too many benefits to have us ever choose

to stop progress in its tracks our job

here is to provide regulations and rules

to make sure that progress and ethics go

hand in hand

[Music]

you