Applications and websites that offer recognition of pictures search are growing to be an essential part of businesses today. So, we have noted down the uses of this so-called “recognition of pictures” trend across industries. Scroll down below to learn more!
Image Recognition Applications
In real-world applications, you can use image recognition for security and surveillance. Also, for face, gesture, and object recognition.
Meanwhile, it can also be for visual geolocation and medical image analysis. Or, for driver help.
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Moreover, image recognition is now available in the mainstream.
For example, Facebook, Google, and Youtube use face, photo, and video frame recognition. Yes, in their production. Not only them but many other high-profile consumer applications.
Here are some industries that use image recognition.
Gaming Industry
In the gaming industry, image recognition can transpose a digital layer on top of images from the real world.
AR, Augmented Reality, adds details to the existing environment.
For example, you may have heard of Pokemon Go. This popular game relies on image recognition technology.
Automotive Industry
Image recognition is crucial to ease autonomous driving.
In the US, autonomous vehicles are in testing phases. Yet, in many European cities, these vehicles are now used for public transport.
With image recognition, vehicle systems can identify objects on the road.
These includes:
- Vehicles
- traffic lights
- moving objects
- People
- road signs
- Pathways
Manufacturing
Moreover, image recognition can also be found in the manufacturing cycle.
For example, you can store images of components with related metadata. Then you can use it to detect defects. As a result, you’ll lessen the chances of defects in the process.
Education
Of course, there are students with learning difficulties and disabilities. In such situations, image recognition can really help.
For example, applications with computer vision can produce image-to-speech and text-to-speech functions. As a result, students with impaired vision can read out materials.
E-commerce Industry
Well, image recognition is highly beneficial for vendors as well as consumers.
Through image recognition, you can automatically process, categorize, and tag product images. Yes, it enables a strong image search.
For example, customers can seek a chair with a particular armrest and obtain relevant results.
As additional info, we have listed below some practical challenges you may encounter when working on CNN projects. CNN stands for Convolutional Neural Networks.
For industries that want to create an image recognition picture, there are also hurdles while thinking about its practical uses.
CNN is an integral part of the image recognition process. So, there are few challenges below when making one.
CNN Challenges
Tracking Experiments
It’s arduous to trace experiment source code, hyperparameters, and configuration.
Also, you want to run many experiments to find hyperparameters that give the best execution.
Scaling Experiments On-premise Or In The Cloud
Provisioning machines is time-consuming. Why?
Because you need to satisfy deep learning projects and analyze experiments between them.
Manage Training Data
Yes, working on computer vision projects suggests rich media such as images or video. As a result, it may require large training sets weighing Gigabytes to Petabytes.
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