How Computer Vision Works

Summary

The video script delves into the evolution of human vision and its remarkable complexity, starting from the development of light sensitivity in ancient organisms to the modern-day understanding of visual systems. It highlights the journey from the invention of the first photographic camera in 1816 to the current digital imaging technology that mimics the human eye's ability to capture light and color. The script then explores the challenges of image recognition for machines, contrasting human contextual understanding with the algorithmic view of images as mere data arrays. Machine learning, particularly through convolutional neural networks (CNNs), is presented as a solution to train algorithms in recognizing and understanding image content. The video also discusses the limitations of CNNs in handling temporal features in videos and introduces recurrent neural networks (RNNs) as a method to address this. The summary concludes with the challenges of data volume required for training models to mimic human vision and the role of services like Google Cloud Vision and Video in providing pre-trained models to assist developers and companies with limited resources.

Takeaways

• 👀 Human vision is a complex system that evolved from light-sensitive organisms billions of years ago, and today includes eyes, brain receptors, and a visual cortex for processing.
• 📸 The first photographic camera was invented in 1816, and since then, technology has advanced to digital cameras that mimic the human eye's ability to capture light and color.
• 🧠 Understanding the content of a photo is more challenging for machines than capturing it, as the human brain has evolutionary context that computers lack.
• 🌟 Machine learning algorithms can be trained to understand image content by using context from a dataset, similar to how the human brain operates.
• 🐕 For images that are difficult for humans to classify, machine learning models can achieve better accuracy by being fed enough data.
• 🤖 Convolutional Neural Networks (CNNs) are a type of neural network used in computer vision that break down images into filters and perform calculations to identify objects.
• 🔍 CNNs start with randomized filter values and use an error function to update these values over time, improving accuracy with each iteration.
• 🎥 Analyzing video involves considering the temporal nature of frames and using models like Recurrent Neural Networks (RNNs) that can retain information about previously processed frames.
• 📈 Training RNNs for video classification involves passing sequences of frame descriptions and adjusting weights based on a loss function until higher accuracy is achieved.
• 📈 Achieving human-like vision in algorithms requires incredibly large amounts of data, which can be a challenge for smaller companies or startups.
• 🌐 Technologies like Google Cloud Vision and Video APIs can assist by providing pre-trained models that have been trained on millions of images and videos.
• 🔧 Developers can easily add machine learning to their applications by using these APIs, as demonstrated by the simple cURL request example in the script.

Q & A

How did the evolution of human vision begin?

The evolution of human vision began billions of years ago when small organisms developed a mutation that made them sensitive to light.

What are the three main components of a visual system?

The three main components of a visual system are eyes for capturing light, receptors in the brain for accessing it, and a visual cortex for processing it.

When was the first photographic camera invented?

The first photographic camera was invented around 1816.

How does a computer perceive an image initially?

To a computer, an image initially appears as a massive array of integer values representing intensities across the color spectrum, without any context.

What is the role of machine learning in understanding image content?

Machine learning allows us to train the context for a dataset so that an algorithm can understand what the organized numbers in an image actually represent.

How can machine learning achieve better accuracy in classifying images that are difficult for humans?

Machine learning can achieve better accuracy by using a machine learning model to analyze a large number of images and, with enough data, learn to differentiate between objects that are hard for humans to classify.

What is a convolutional neural network (CNN) and how does it work?

A convolutional neural network (CNN) is a type of neural network that works by breaking an image down into smaller groups of pixels called filters. It performs a series of calculations on these pixels, comparing them against specific patterns it is looking for, to identify objects.

How does a CNN know what to look for and if its prediction is accurate?

A CNN knows what to look for and if its prediction is accurate through a large amount of labeled training data. It uses an error function to compare its prediction against the actual label and updates its filter values accordingly.

What is the limitation of CNNs when it comes to analyzing videos?

The limitation of CNNs in analyzing videos is that they can only take into account spatial features and cannot handle temporal or time-based features, which are important for understanding the context between video frames.

What type of model can handle the temporal nature of videos?

A recurrent neural network (RNN) can handle the temporal nature of videos as it can retain information about what it has already processed and use that in its decision making.

What is the challenge with training models to mimic human vision?

The challenge is the incredibly large amount of data needed to mimic human vision. It requires feeding the algorithm vast amounts of data, including millions of objects across thousands of angles, all annotated and properly defined.

How can technologies like Google Cloud Vision and Video help companies with limited resources?

Google Cloud Vision and Video APIs can help by providing pre-trained models that have been trained on millions of images and videos. This allows companies to access powerful machine learning capabilities without the need for extensive resources to train their own models.

Outlines

00:00

👀 The Evolution and Advancement of Vision Systems

This paragraph discusses the evolutionary journey of vision systems, starting from the development of light sensitivity in ancient organisms to the current state of human and machine vision. It highlights the components of the human visual system, including eyes, brain receptors, and the visual cortex. The paragraph also touches on the invention of the photographic camera and the advancements in digital imaging. The focus then shifts to the challenges of image recognition by machines, contrasting human contextual understanding with the data-driven approach of algorithms. Machine learning is introduced as a method to train algorithms to understand image content, with examples given on how it can be used to differentiate between complex images. The paragraph concludes with an introduction to convolutional neural networks (CNNs), which are pivotal in computer vision for identifying objects within images.

05:00

🤖 Machine Learning and the Challenge of Image and Video Analysis

The second paragraph delves into the application of machine learning in image and video analysis. It explains how CNNs work by using filters to detect patterns and identify objects within an image. The paragraph also describes the training process of CNNs using labeled data and how they learn to make accurate predictions through iterative adjustments based on error functions. The limitations of CNNs in handling temporal features of videos are discussed, leading to the introduction of recurrent neural networks (RNNs), which can retain information about previously processed data. The challenges associated with the vast amount of data required to train these models to mimic human vision are also highlighted. The paragraph concludes with a mention of services like Google Cloud Vision and Video, which provide pre-trained models to help developers incorporate machine learning into their applications without the need for extensive data resources.

Keywords

💡Human vision

Human vision refers to the ability of the human eye to detect and respond to light, which is a complex and beautiful process that has evolved over billions of years. In the video, it is mentioned as the starting point for understanding how visual systems work and how they have been mimicked in technology. The script emphasizes the genetic engineering and balance of the human visual system, which allows us to appreciate simple things like a sunrise.

💡Genetic engineering

Genetic engineering is a scientific process that allows for direct manipulation of an organism's genes using biotechnology. In the context of the video, it is used to describe the natural evolution of the human visual system, which has been fine-tuned over millions of years to create a balanced and effective mechanism for capturing and processing light.

💡Photographic camera

A photographic camera is a device used to capture light and record images, which was first invented around 1816. The video script describes the early version of the camera that used a piece of paper coated with silver chloride, which would darken upon exposure to light. This invention laid the groundwork for the development of more advanced cameras capable of capturing digital photos, which mimic the human eye's ability to capture light and color.

💡Machine learning

Machine learning is a type of artificial intelligence that allows computers to learn from data and improve their performance over time without being explicitly programmed. In the video, machine learning is used to train algorithms to understand the context of images, much like the human brain does. It is a key technology in teaching computers to interpret and make sense of visual data.

💡Convolutional Neural Network (CNN)

A Convolutional Neural Network, or CNN, is a type of neural network used in computer vision tasks. It works by breaking down an image into smaller groups of pixels, called filters, and performing a series of calculations to identify patterns and objects. The video explains that CNNs can detect high-level patterns and specific objects, such as faces and animals, through a process of convolutions and by comparing against labeled training data.

💡Recurrent Neural Network (RNN)

A Recurrent Neural Network, or RNN, is a type of neural network that is designed to handle sequential data, such as video frames. Unlike CNNs, which only consider spatial features, RNNs can retain information about previously processed data and use that for decision making. In the context of the video, RNNs are used to analyze videos by considering the temporal context between frames, which is crucial for tasks like identifying actions or events over time.

💡Labeled training data

Labeled training data refers to a dataset that includes input data along with corresponding correct answers or labels. In the video, it is mentioned that CNNs use a large amount of labeled training data to learn what to look for in images and to improve the accuracy of their predictions. The initial predictions of a CNN are randomized, and it is through the comparison of these predictions with the actual labels that the CNN learns and updates its filter values.

💡Error function

An error function, also known as a loss function, is used in machine learning to measure the difference between the predicted output of a model and the actual output. In the video, the error function is crucial for training CNNs and RNNs. It helps the network to identify and correct its mistakes by comparing predictions against labeled data, leading to iterative improvements in accuracy.

💡Google Cloud Vision and Video

Google Cloud Vision and Video are APIs provided by Google that allow developers to integrate image and video analysis into their applications. The video script highlights that these technologies use pre-trained models to analyze visual content, which can be a significant advantage for companies with limited resources to train their own models. The APIs can process millions of objects across various angles, providing a powerful tool for applications that require advanced computer vision capabilities.

💡cURL

cURL is a command-line tool used for transferring data using various internet protocols, including HTTP and HTTPS. In the video, cURL is demonstrated as a way to make a request to the Cloud Vision API, showcasing how easy it is to integrate Google's pre-trained model into an application. The script provides an example of sending an image to the API and receiving a response with analyzed data.

💡Evolution

Evolution is the process by which species of organisms change over time through genetic variation and natural selection. The video script uses the term 'evolution' to describe the billions of years of development that led to the human sense of sight. It draws a parallel between the evolutionary process and the advancements in technology that are now enabling computers to approach and potentially match human vision capabilities.

Highlights

Human vision is incredibly complex and beautiful, evolving from a simple light sensitivity mutation in ancient organisms to the sophisticated visual systems found in many species today.

The human visual system consists of eyes to capture light, brain receptors to access it, and a visual cortex to process it.

In the past 30 years, we've made significant strides in extending our visual capabilities to machines through advancements in camera technology and machine learning.

The first photographic camera, invented in 1816, used silver chloride-coated paper that darkened upon light exposure, paving the way for modern digital photography.

Capturing light and color with a camera is relatively easy compared to understanding the content of a photo, which requires context that machines lack.

Machine learning allows algorithms to be trained on datasets to understand the context and meaning behind numerical image data.

Convolutional neural networks (CNNs) break down images into smaller pixel groups called filters to detect patterns and identify objects.

CNNs use labeled training data and an error function to iteratively improve their predictions and filter values.

Recurrent neural networks (RNNs) can handle the temporal nature of videos by retaining information about previously processed frames.

Analyzing videos requires integrating CNNs for image analysis with RNNs that can account for changes over time.

To truly mimic human vision, algorithms require vast amounts of labeled data covering a multitude of angles and variations for millions of objects.

The sheer scale of data needed presents a significant challenge for smaller companies or startups with limited resources.

Google Cloud Vision and Video APIs offer a solution by providing pre-trained models that have been trained on millions of images and videos.

These APIs allow developers to easily integrate powerful machine learning models into their applications without the need for extensive data training.

Google's pre-trained models can extract a wide range of data from images and videos, simplifying the process for application development.

The Cloud Vision API can be easily accessed and utilized through a simple REST API request, as demonstrated in the transcript.

The evolution of computer vision has brought us to a point where machines are increasingly capable of matching human visual recognition abilities.

The potential applications of these technologies are vast, from enhancing accessibility to revolutionizing industries through automated analysis and understanding of visual data.

Transcripts

[MUSIC PLAYING]

SARA ROBINSON: Human vision is amazingly

beautiful and complex.

It all started billions of years ago

when small organisms developed a mutation that

made them sensitive to light.

Fast forward to today, and there is an abundance

of life on the planet which all have

very similar visual systems.

They include eyes for capturing light,

receptors in the brain for accessing it,

and a visual cortex for processing it.

Genetically engineered and balanced pieces

of a system which help us do things

as simple as appreciating a sunrise.

But this is really just the beginning.

In the past 30 years, we've made even more strides

to extending this amazing visual ability, not just to ourselves,

but to machines as well.

The first type of photographic camera

was invented around 1816 where a small box held a piece of paper

coated with silver chloride.

When the shutter was open, the silver chloride

would darken where it was exposed to light.

Now, 200 years later, we have much more advanced versions

of the system that can capture photos right into digital form.

So we've been able to closely mimic how the human eye can

capture light and color.

But it's turning out that that was the easy part.

Understanding what's in the photo is much more difficult.

Consider this picture.

My human brain can look at it and immediately know

that it's a flower.

Our brains are cheating since we've

got a couple million years worth of evolutionary context

to help immediately understand what this is.

But a computer doesn't have that same advantage.

To an algorithm, the image looks like this--

just a massive array of integer values which represent

intensities across the color spectrum.

There's no context here, just a massive pile of data.

It turns out that the context is the crux of getting algorithms

to understand image content in the same way

that the human brain does.

And to make this work, we use an algorithm

very similar to how the human brain operates

using machine learning.

Machine learning allows us to effectively train

the context for a data set so that an algorithm can

understand what all those numbers

in a specific organization actually represent.

And what if we have images that are difficult for a human

to classify?

Can machine learning achieve better accuracy?

For example, let's take a look at these images of sheep dogs

and mops where it's pretty hard, even for us,

to differentiate between the two.

With the machine learning model, we

can take a bunch of images of sheep dogs and mops,

and as long as we feed it enough data,

it will eventually be able to properly tell

the difference between the two.

Computer vision is taking on increasingly complex challenges

and is seeing accuracy that rivals

humans performing the same image recognition tasks.

But like humans, these models aren't perfect.

They do sometimes make mistakes.

The specific type of neural network that accomplishes this

is called a convolutional neural network or CNN.

CNNs work by breaking an image down

into smaller groups of pixels called a filter.

Each filter is a matrix of pixels,

and the network does a series of calculations

on these pixels comparing them against pixels

in a specific pattern the network is looking for.

In the first layer of a CNN, it is

able to detect high-level patterns like rough edges

and curves.

As the network performs more convolutions,

it can begin to identify specific objects

like faces and animals.

How does a CNN know what to look for

and if its prediction is accurate?

This is done through a large amount of labeled training

data.

When the CNN starts, all of the filter values are randomized.

As a result, its initial predictions make little sense.

Each time the CNN makes a prediction

against labeled data, it uses an error function

to compare how close its prediction was

to the image's actual label.

Based on this error or loss function,

the CNN updates its filter values

and starts the process again.

Ideally, each iteration performs with slightly more accuracy.

What if instead of analyzing a single image,

we want to analyze a video using machine learning?

At its core, a video is just a series of image frames.

To analyze a video we can build on our CNN for image analysis.

In still images, we can use CNNs to identify features.

But when we move to video, things

get more difficult since the items we're identifying

might change over time.

Or, more likely, there's context between the video frames that's

highly important to labeling.

For example, if there's a picture

of a half full cardboard box, we might

want to label it packing a box or unpacking a box depending

on the frames before and after it.

This is where CNNs come up lacking.

They can only take into account spatial features,

the visual data in an image, but can't handle temporal or time

features-- how a frame is related to the one before it.

To address this issue, we have to take the output of our CNN

and feed it into another model which

can handle the temporal nature of our videos.

This type of model is called a recurrent neural network

or RNN.

While a CNN treats groups of pixels independently,

an RNN can retain information about what

it's already processed and use that in its decision making.

RNNs can handle many types of input and output data.

In this example of classifying videos,

we train the RNN by passing it a sequence of frame

descriptions-- empty box, open box, closing box--

and finally, a label--

packing.

As the RNN processes each sequence,

it uses a loss or error function to compare its predicted output

with the correct label.

Then it adjusts the weights and processes the sequence again

until it achieves a higher accuracy.

The challenge of these approaches to image and video

models, however, is that the amount

of data we need to truly mimic human vision

is incredibly large.

If we train our model to recognize

this picture of a duck, as long as we're given this one picture

with this lighting, color, angle, and shape,

we can see that it's a duck.

But if you change any of that or even just rotate the duck,

the algorithm might not understand what it is anymore.

Now, this is the big picture problem.

To get an algorithm to truly understand and recognize

image content the way the human brain does,

you need to feed it incredibly large amounts

of data of millions of objects across thousands of angles

all annotated and properly defined.

The problem is so big, that if you're

a small startup or a company lean on funding,

there's just no resources available for you to do that.

This is why technologies like Google Cloud Vision and Video

can help.

Google digests and filters millions of images and videos

to train these APIs.

We've trained a network to extract all kinds of data

from images and video so that your application doesn't

have to.

With just one REST API request, we're

able to access a powerful pre-trained model that

gives us all sorts of data.

Here's how easy it is to call the Cloud Vision API with cURL.

I'll send this image to the API, and here's

the response we get back.

Billions of years since the evolution of our sense of sight

we found that computers are on their way

to matching human vision, and it's all available as an API.

If you'd like to know more about the Cloud Vision and Video

APIs, check out their product pages at the links here

to see how you can easily add machine

learning to your application.

Thanks for watching.

[MUSIC PLAYING]