Computer vision (CV) is the subcategory of artificial intelligence (AI) that targets creating and operating digital systems to process, interpret and analyze graphical data. Computer vision aims to facilitate computing devices to perfectly recognize an object or an individual in a digital image and take suitable action. Computer vision employs convolutional neural networks (CNNs) to process visual data at the pixel level and an in-depth understanding of recurrent neural networks (RNNs) to understand how one pixel connects to another.
Earlier experiments in computer vision were initiated in the 1950s and it was first put to use commercially to differentiate between typed and handwritten text by the 1970s. Today, the applications for computer vision have extended exponentially. By 2022, IBM and Data Science have predicted that the computer vision and hardware market will reach $48.6 billion.
One of the driving aspects behind the evolution of computer vision is the quantity of data we develop today that is used to prepare and make computer vision better.
Along with a remarkable amount of visual data (more than three billion images shared online every day), the computing power needed to explore the data is now obtainable. As the field of computer vision has grown with new hardware and algorithms so have the accuracy rates for object identification. In less than a decade, today’s systems have reached 99% accuracy from 50% making them more accurate than humans at quickly reacting to visual inputs.
How Does Computer Vision Work?
Computer Vision uses a technique known as semantic segmentation, which applies a class label to each pixel in the image to give the AI a more accurate view of the world around it.
Two essential technologies are used to accomplish this: a type of machine learning called deep learning and a convolutional neural network (CNN).
A CNN helps a machine learning or deep learning model “look” by breaking images down into pixels that are given tags or labels. It uses the labels to perform convolutions (a mathematical operation on two functions to produce a third function) and makes predictions about what it is “seeing.”
The Evolution of Computer Vision (CV):
Before the inception of deep learning, the tasks that computer vision could function were restricted and needed an excess of manual coding and effort by developers and human operators.
- Create a Database
- Annotate Images
- Capture New Images
What time does CV take to Decode an Image?
Today’s ultra-fast chips and related hardware, along with the fast reliable internet and cloud networks, make the process lightning fast.
One crucial factor has been the willingness of many of the big companies doing AI research to share their work with Facebook, Google, IBM, and Microsoft, notably by open sourcing some of their machine learning work. As a result, the AI industry is cooking ahead, and experiments that not long ago carried weeks to conduct might take 15 minutes today. And for multiple real-world applications of computer vision, this method all ensues constantly in microseconds.
Applications of Computer Vision:
Computer vision is one of the places in Machine Learning where core ideas are integrated into major products that we use every day. Add examples in each of the sub-categories that you mention below, please check comments against them:
Computer Vision in Self-Driving Cars:
Computer vision boosts self-driving cars to create insight into their surroundings. Cameras capture video from various angles around the car and provide it to computer vision software, which then processes the photos in real-time to discover the extremities of roads, and detect traffic signs, cars, objects, and pedestrians. For example; Manufacturers such as Tesla, BMW, Volvo, and Audi use multiple cameras, lidar, radar, and ultrasonic sensors to acquire images from the environment so that their self-driving cars can detect objects, and lanes markings, signs and traffic signals to safely drive.
Computer Vision in Facial Recognition:
Computer vision also plays a vital part in facial recognition applications, the technology that enables computers to resemble images of people’s faces to their self-identity. Computer vision algorithms notice facial features in images and resemble them with databases of duplicate profiles. Consumer devices use facial recognition to establish the uniqueness of their owners. Various social media apps use facial recognition to see and tag users, and law enforcement agencies rely on facial recognition technology to catch criminals in video feeds. For an example; China is definitely on the cutting edge of using facial recognition technology, and they use it for police work, payment portals, security checkpoints at the airport and even to dispense toilet paper and prevent theft of the paper at Tiantan Park in Beijing, among many other applications.
Computer Vision in Augmented Reality & Mixed Reality:
In augmented and mixed reality, Computer Vision technology enables computing devices such as smartphones, tablets, and smart glasses to overlay and implant virtual entities on real-world imagery. The computer vision technology enables AR gear to detect objects in the real world like tabletops, walls, and floors to select the locations on a device’s display to establish a virtual object. For instance, computer vision algorithms can help AR applications detect planes such as tabletops, walls and floors, a very important part of establishing depth and dimensions and placing virtual objects in the physical world.
Computer Vision in Healthcare:
Computer vision works best within health tech. Computer vision algorithms can assist in automating tasks like detecting cancerous moles in skin visions or uncovering symptoms in x-ray and MRI scans. Cancer detection is another notable example. Accuracy in diagnosing different forms of cancer is vital. According to Google, computer vision tools assist in detecting cancer metastasis with much higher precision than human doctors.
Computer Vision in Agriculture
The agricultural industry has detected various interpretations of computer vision - artificial intelligence (AI) models in regions such as planting, harvesting, advanced analysis of weather conditions, weeding, and plant health detection and tracking.
At CES 2019, John Deere featured a semi-autonomous combine harvester that uses artificial intelligence and computer vision to analyze grain quality as it gets harvested and to find the optimal route through the crops. There’s also great potential for computer vision to identify weeds so that herbicides can be sprayed directly on them instead of on the crops.
Challenges of Computer Vision:
Enabling computers to witness human is very tough. Inventing the machine that sees as we humans do is deceptively tough because we are never sure about the operation of human vision in the first place.
Studying biological vision needs an experience of the perception organs like the eyes and the brain. Progress has been made by charting the approach and learning the tricks and shortcuts used by the system, although like any study that involves the brain, there is a long way to go.
Future Road Map of Computer Vision:
More applications of Machine Learning in Computer Vision contain areas like Multilabel Classification and Object Recognition. In Multilabel Classification, we seek to create a model to identify accurately the number of objects present in an image and to what category they belong. In Object Recognition instead, we aspire to carry this idea a step ahead by determining the role of the various objects in the picture. Neuromorphic vision sensors will be the next direction after computer vision as they will be used to mimic the sensing and early visual-processing characteristics of living organisms.
CSM’s Computer Vision Capabilities:
Computer Vision (CV) is presently one of the primary applications of Artificial Intelligence. One of the main essentials needed to deploying a CV system is testing its robustness. CSM’s CV system is invariant to environmental changes (such as changes in illumination, orientation, and scaling) and able to perform its designed task repeatedly.
CSM has implemented a facial recognition application (E-Pass) in Secretariat Govt. of Odisha and its own City Office as well. This CSM’s Face Recognition Based Permission System is a web-enabled system that verifies a person who has registered and applied for the pass earlier. It permits when your face matches with the image captured earlier with the real-time identification captured in the camera within 2-3 seconds during entry. CSM has developed the GovTech space by harnessing emerging technologies to transfigure governance.
CSM has designed many solutions in mining, recognizing lumps and fines are tracked with the help of CSM’s computer vision app.
