Vision is easy for human brains. We can easily tell apart a tree, a bus, a dog, or an airplane. But its not the same with computers. It takes a lot of effort and skill to teach machines to ‘see’ like us. But humans have been successful in doing so. We have enabled machines with our natural skills to see, learn by example, and understand the world. With the help of computer vision and image recognition, we have taught machines to perform tasks involving vision. And when this capability is brought into businesses with a practical purpose, all this effort is worth it.
There is a vast amount of data being shared now in the form of digital images and videos. Mobile phones equipped with hi-tech cameras are leading to the creation of all this data through social networking sites, apps, and websites. This data is being used by businesses to provide better services to the people accessing it. Here we learn about image recognition, how it is being used on different kinds of data, and its use cases in various sectors.
What is Image recognition?
Image recognition is the method of identifying images and categorizing them into one of the predefined classes. It is the technology that identifies objects, buildings, places, logos, and people in images. Image recognition tools can define what is presented in a picture and separate one object from the other.
An example of how image recognition works is how Facebook can now perform facial recognition with 98% accuracy. This is very close to the human ability to recognize faces. With just a few tagged pictures, Facebook can identify the faces of your friends.
Progress in Machine learning technology is fueling the growth of image recognition in everyday tasks. The market for image recognition is estimated to grow by USD 38.92 Billion by 2021. Image recognition is being adopted by industries such as healthcare, retail, automotive, e-commerce, and many others. There is an increasing demand for security applications such as surveillance cameras with facial recognition.
Image recognition comes under computer vision, which is the field of study enabling machines to do this task. But there is a difference between the two, let us briefly understand what.
Difference between image recognition and computer vision
Computer vision is a broader field and image recognition is a part of it. Image recognition is the process of detecting an object or attribute within an image or a video.
Computer vision includes techniques of gathering and analyzing data that are high-dimensional and provides information in the form of decisions. Computer vision also includes object recognition, event detection, and image reconstruction.
Now since we have learned the basics of image recognition, let us discuss some of its real-life applications.
Image Recognition Use Cases
Image recognition is highly useful for the retail industry. The quality and price of consumer goods can be compared with the image recognition technique. Retail outlets can use image recognition to alert the staff when a shelf goes empty. Using the cameras in-store, store staff is sent instant alerts on their phones with the description of the missing item and location of the shelf.
Image recognition has the capability of making cars safer and more efficient. Companies like Ford, General Motors, Tesla, Apple, and many more are working towards manufacturing self-driving cars. Image recognition proves to be the ‘second pair of eyes’ for these autonomous vehicles. It helps in decreasing road accidents, keeps track of traffic regulations, and improves efficiency.
Image recognition technology is helping the healthcare sector in many ways. It is helping in standardized and effective clinical decision-making. Surgical procedures are being powered by image recognition techniques. Emotion detection techniques can be used to analyze how patients feel during and after they are hospitalized.
With image recognition, it is now possible to accurately track the medication consumption of a patient and help with main management.
All of the medical data consists of about 90% of medical images. With the huge amount of data available within X-rays, CAT scans, and MRIs, computed tomography (CT), and ultrasound scans, image recognition is helping improve the accuracy of radiologists and pathologists.
Image recognition has one of the most important roles in the security industry. Security devices including security cameras, home automation cameras, pet monitoring, uses facial recognition technology to identify threats.
Image recognition is being used against fraud detection, reducing the usage of traditional passwords, and to differentiate between a human face and photograph.
Consumer behavior can be detected by the photos shared by people on social media. If gone undetected marketers can miss on key trends related to products and services. Image recognition is now helping marketers understand visuals within social media, even if they do not have text descriptions. Also known as visual listening, brands monitor social media images to understand, evaluate, and interact with their consumers.
Modern image recognition software can identify a large number of objects such as humans, texts in images, places, buildings, logos, etc. This is just the start as it is going to advance much more. Businesses are now benefiting from this technology, using it to stand out from the competitors and make everyday operations more effective.
Skyl.ai’s image recognition solutions include shelf management in retail, getting insights from property listing images, medical imaging and detection, and many more. Check out all our image recognition solutions here.