Generating a Digital Brain using Deep Learning!

Images and videos have become a very crucial part of our day-to-day tasks. The human brain is capable of processing digital media and recognizes everything instantly. But what if we can develop a similar brain for our machines as well? Sounds galvanizing, right? Today, computer vision is regarded as one of the most successful applications of deep learning. We can now enable our computers to recognize images and videos using deep learning algorithms and even gain useful insights from them. This world has witnessed a significant leap in technology that has put computer vision on the crash projects of several industries. Convolution Neural Networks (CNNs) have made it possible to train our image processing algorithms faster, contributing to the growing popularity of computer vision. A huge database of images and videos is now available on the internet. In this blog, we are going to discuss some of the most popular real-world applications of computer vision and how industries are getting benefited from it. So what are you waiting for? Let’s start.

 

Computer Vision, what this fancy term is all about?

Before moving onto the applications, let’s first understand what computer vision is all about.

“Computer Vision is an application of Deep Learning that empowers computers to gain a high-level understanding of digital media, such as images and videos. It includes extraction of high-dimensional data from the real world and then processing it to produce numerical information.”

Following is a curated list of top 10 real-world applications of computer vision:

Facial Recognition

Face Recognition algorithms capture more than just features. It is programmed in a manner that enables it to capture all the unique factors of a face and that too from multiple angels. For example, it also measures and memorizes the distance between an individual’s eyes and mouth. Siamese Network in computer vision is used to carry out facial recognition.

Augmented Reality

Augmented Reality (AR) first takes a real-world environment and then adds a computer-generated input to it. Several parts from both of the environments, real and augmented, can interact together and even digitally manipulated. Therefore, AR can simply be defined as an amalgamation of both worlds. It renders a 3D registration of real and virtual objects.

Social Distancing

Every single human life is at the risk of getting infected by the virus and social distancing can be regarded as the need of the hour. We can effectively monitor public spaces through computer vision cameras and sensors to track social distancing and impose strict rules and regulations for those who violate its norms.

Unmanned Aerial Vehicles (UAVs)

Amidst COVID-19, Computer Vision-enabled drones or UAVs can be used for a variety of purposes:

  • Delivering emergency food supplies and testing kits.
  • Spraying disinfectant for sanitization of public places.
  • Detecting unmasked citizens through cameras.
  • Communicating advisory through speakers.
  • Analyzing the movement of people in quarantine shelters.

Machine Vision

Machine Vision is defined as a set of methods to enable image-based automation for business operations like process control, automated inspection, robot guidance, etc. It is a bifurcation of systems engineering that integrated existing technologies in new ways and uses them to solve real-world problems.

Self-driving cars

Self-driving cars have been at the core of the automobile industry over the past few years

and computer vision brought a promise to transform this vision into reality. YOLO (You Look Only Once) is an immensely popular computer vision algorithm used for autonomous driving which can efficiently detect objects in the path.

Optical Character Recognition

OCR is simply the electronic conversion of images containing handwritten text into machine-encoded text. It includes text processing in different forms, such as a photo of a document, a scanned document, subtitles superimposed on an image, or a scene-photo. Several computer vision algorithms are used for OCR technology, such as matrix matching.

Visual Search

Visual search uses images as keywords as opposed to texts and searches for related images, websites, blogs, or any other posts. Visual Search Engine is programmed in a manner that bridges the time gap between your search. For example, Google Lens allows the users to look for objects through the lens and get similar results as per their image search. 

Gesture Recognition

It is no surprise that multiple algorithms exist in the computer vision field to detect human gestures and postures. They can interpret human gestures originated from any motion or state of the human body. For example, a store supervisor can carry out emotion recognition to determine if customers visiting the store are happy with the services or not.

Computer-aided Diagnosis

Computer Vision also finds a wide range of applications in the healthcare sector. It can assist medical professionals in training. Doctors can interpret medical images used in techniques like X-Ray and MRI using computer vision efficiently. 

Key Takeaways

Computer Vision is a thriving discipline and several industries are leveraging it to process data in the form of images and videos. When Apple unveiled its facial recognition software, it instantly became the talk of the town. After all, a computer can never get tired even after processing trillions of GBs of data. It is the right time to push our limits and unleashes the true potential of computer vision!