In recent years, computer vision has taken great leaps thanks to advances indeep learningand artificial neural networks.
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Theyre particularly efficient at classifying images and localizing objects in images.
Recent work done in the fieldshows the limits of computer vision algorithms and the need for new evaluation methods.
Nonetheless, the current applications of computer vision show how much can be accomplished with pattern matching alone.
In this post, well explore some of these applications, but we will also discuss their limits.
Few companies that match Googles vast store of user data.
you’re able to then search your images based on this information.
Google uses machine learning and computer vision to search the content of images even if you havent tagged them.
Googles image recognition isnt perfect, however.
Google also uses computer vision to extract text from images in your library, Drive, and Gmail attachments.
Another remarkable improvement that advances in computer vision have ushered in is smart zooming.
Traditional zooming features usually make images blurry because they fill the enlarged areas by interpolating between pixels.
Instead of enlarging pixels, computer vision-based zooming focuses on features such as edges, patterns.
This approach results in crisper images.
Many startups and longstanding graphics companies have turned to deep learning to make enhancements to images and videos.
AdobesEnhance Details technology, featured in Lightroom CC, uses machine learning to create sharper zoomed images.
Adobe uses deep learning to enhance the details of zoomed images.
Despite the advances, however, current facial recognition is not perfect.
AI and security researchers have found numerous ways to cause facial recognition systems to make mistakes.
you might now easily install security cameras and monitor your home online at any time.
Each camera sends a lot of data to the cloud.
Computer vision algorithms can enable home security camera to become more efficient in the usage of these resources.
Note, however, that computer vision is still not very good at understanding context.
AR owes much of its expansion to advances in computer vision algorithms.
Computer vision also enables you to extract information from the real world through the lens of your phones camera.
(AI researcher Jeremy Howard hasan interesting discussion on this.)
Deep learning algorithms provide end-to-end solutions that make the process very easier.
Some AI researchers have gone as far as saying deep learningwill soon replace radiologists.
But those who have experience in the fieldbeg to differ.
Theres much more to diagnosing and treating diseases than looking at slides and images.
Playing games
Teaching computers to play gameshas always been a hot area of AI research.
Most game-playing programs usereinforcement learning, an AI technique that develops its behavior through trial and error.
Computer vision algorithms play an important role in helping these programs parse the content of the games graphics.
Also, for the moment, AI algorithms need huge amounts of data to learn games.
For instance,OpenAIs Dota-playing AIhad to go through 45,000 years worth of gameplay to achieve champion level.
Go used various artificial intelligence systems to obviate the need for cashiers.
When they leave the store, their shopping cart is automatically charged to their Amazon account.
Three years after the announcement, Amazon has opened 18 Go stores and its still a work in progress.
Today, were still very far from havingself-driving cars that can navigate any roadon various lighting and weather conditions.
But we have made a lot of progress thanks to advances in deep neural networks.
One of the biggest challenges of creating self-driving carsenabling them to make sense of their surroundings.
Cameras installed around the vehicle monitor the cars environment.
Deep neural networks parse the footage and extract information about surrounding objects and people.
Creepy applications of computer vision
Like all other technologies, not everything about artificial intelligence is pleasant.
Advanced computer vision algorithms can scale up malicious uses.
Here are some of the applications of computer vision that have caused concern.
Surveillance
It is not only phone and computer makers who are interested in facial recognition technology.
But the question is, where do you draw the line between national security and citizen privacy?
Some states and cities in the U.S. havebanned the public use of facial recognition.
Autonomous weapons
Computer vision can also give eyes to weapons.
Military drones can use AI algorithms to identify objects and pick out targets.
For the moment, there are still no autonomous weapons.
Most military institutions are using AI and computer vision in systems that have a human in the loop.
Renowned computer scientist and AI researcher Stuart Russell has founded an organization dedicated tostopping the development of autonomous weapons.
