A look at computer vision and its real world applications in the future
Have you ever wondered what would happen if a computer could see like a human?
Yes, it’s finally a reality. Nowadays, the latest computers/mobile phones can detect an image and gives you results with similar products and objects.
Google lens is one of the best examples of image recognition. It uses AI, image processing, and computer vision for detecting an image.
Another example is the working of face id – iPhone’s use facial recognition to create 3D templates of your face and sends it to the processor’s neural engine which is used to detect the facial map and give access when the user tries to unlock.
According to the above examples, it looks like Computer vision is used only in image processing and recognition. But, there is a vast use of computer vision. Recognition and image building platforms are just a tiny percentage of what computer vision can do.
Learn more about what is computer vision, How is works and recognize objects, how is it different from image processing, real-world applications of computer vision, AI and machine learning in computer vision, and it’s benefits.
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What is Computer Vision?
Computer vision is one of the fields in machine learning and artificial intelligence that deals with processing and understanding of visual objects (images or videos).
In simple terms:
“If AI is an artificial brain for a system, Computer vision would be an artificial eye for a computer.”
Mathematical models and designs are generated by scanning images or videos, which is then analyzed with the help of deep learning and image processing.
Computers use algorithms to detect similar objects out of the crowd. Potentially millions of images with faster recognition. Social Networks like Facebook use CV to find the details of a person and recognize the face in an image.
Aim of computer vision is to emulate human vision through three major processes i.e. image processing, image acquisition, and image understanding.
Evolution of CV
The field of computer vision emerged in 1950 When Crusen blight invented perception, an early artificial neural network and it could solve different categories like differentiating triangles and squares.
Due to the rapid growth in artificial intelligence and machine learning, computer vision technology has advanced to a greater height.
Almost a decade ago, computers used to take about 20 seconds to detect and provide information about an image. But now it takes around 20 milliseconds to process an image. Neuroscience plays a major role in the evolution of CV.
When the computer is provided with real-world data, it can point the difference between many objects within a fraction of seconds like the difference in dogs and cats, person and also learn about their expressions.
Check out this video to know how the computer learns to detect and recognize objects instantly. source: youtube/ted
Just imagine that you are depressed for some reason and your phone automatically suggests you with a motivational video or plays an encouraging song…
How Computer recognize objects | How CV works?
Human’s visual system includes an eye for capturing an image, receptors for accessing it, and visual cortex for processing it in the brain. Similarly, we have attempted to create a visual system for computers that can do the same task faster.
The modern camera has evolved so far that it can capture images that are almost the same as what the human eye can see. Although capturing images has become an easy job, understanding them was hard.
For a computer, the image would look like an array of integers that represents intensities across the color spectrum. To recognize the image through those sets of numbers, machine learning is used. Machine learning allows us to train the context for a data set by which a computer algorithm can understand the set of numbers in a group and what it represents.
It basically takes an image input, detects the pattern, understanding with a group of integers from that image, and predicts the output. Note that this is only based on the prediction output, it is not 100% correct.
CNN (convolution neural network) works by breaking an image into pixels taking a group/series of pixels and comparing it with pixels from other groups to detect the similarities.
CNN produces many predictions with a different number of groups. As the number of processes increases, the precision of those predictions also increases which results in a more accurate output.
Now let’s talk about detecting objects from a video- Similar concepts can be applied here because video is just a set of images. The set of data should be collected from every image per frame and through CNN, recognition can be done.
Unlike image detection, there is a catch here. Due to different frames over time (movement of the object) in a video, the group of numbers will also change. Hence, detecting the context during frames and labeling them should be accurate.
Ex: If you are detecting video of a moving car, it can move either forward or backward. This is where CNN is lacking because it can only understand visual data not time-dependent information.
To solve this issue, a Recurrent neural network is used (RNN). Here the processed data from CNN is retained and then RNN takes the other data from before, present, after, time label, and then processes it many times to reach high accuracy.
Although these techniques can be used to recognize similar objects, if the object is set at a different angle, color, the algorithm would fail to detect. Hence, for a computer to understand the object truly like a human eye, we need to feed it with millions of data for a single object with a different perspective.
You can check out the working of Computer vision in detail from the video below.
Read more in detail on Computer vision 101
Practical applications of Computer vision
Apart from small applications like detecting an object using google lens and apple’s face id, computer vision can also be used in various large industries.
A vision towards self-driving cars
One of the major implementations of CV is in the self-driving cars. This technology is used in the sensors and camera of self-driving cars to detect nearby vehicles, distance, traffic conditions, and other significant data. It can give a new vision to our future vehicles by making it completely automatic and free from drivers which might reduce accidents in the future.
Many companies like tesla, google, apple have also started developing self-driving electric vehicles which can improve safety, reduce accidents and it also reduces emissions.
Check out this article to know more about tesla autopilot cars and waymo: google’s driverless car.
Customer service and service bots
In recent years, computer vision technology has become so mature that it can now, not only recognize objects but can perform actions accordingly. Deep learning systems are used to analyze large amounts of data. Hence, it can be used in the service center to assist customers and it can also be used as a service bot that can perform specific tasks repeatedly with different people by recognizing them.
Computer vision powered bots have grown a lot and even surpasses human vision in some cases. Hence, many organizations have also started using service bots in their office works and for visual support tools.
Government usage for detecting criminals
As the computer vision is a powerful technology, the government started using it in surveillance platforms. Countries like Europe, China, America started implementing this technology to recognize criminals.
In 2018, Google employees address a letter to the CEO, Sundar Pichai about the concerns and stop involvement in project maven (Warfare drones that are powered by computer vision and AI) for the US military. It was canceled by the company because it could damage the brand and can give a negative impact on people’s trust.
More precisely, Artificial intelligence should not be used for destruction or battles because it can change the processor AI evolution in the wrong direction.
We believe that “Google was right to do so” because being such a huge and trusted brand, they should not involve in weapon development and warfare activities.
Must read: What would happen if AI surpasses human intelligence?
Designing and image recognition tools
A huge amount of data is being provided to analyze an image. Using the algorithm, designing tools are created that understands color correction, textures, and other details. There is a vast use of CV in designing tools to create 3d models with color accuracy.
Learn more on top 10 tools for image recognition and designing which uses computer vision.
Image recognition was the first application that made significant growth and development of computer vision technology. Many apps are developed that has features of recognizing object, person, or products from a crowd of images. Check out the top 11 image recognition apps that you can use right now.
There other applications like medical diagnosis image processing, tracking data, human action, and expression recognition. However, this is not a comprehensive list of applications. There are tons of uses with this technology that can be implemented in coming years.
Future of computer vision technology
If you are wondering how this technology would look like in the future, will it cause any trouble? Don’t worry, AI and computer vision have a long way to go. Current machine learning techniques used by computer vision requires a significant amount of data and computational power. It is extremely hard to implement it in real-time scenarios for moving objects.
Reports show that many companies like waymo, Ola, tesla have hired thousands of employees to teach the pedestrians, self-driving cars about the obstacles. This is held in the outsourcing stores of countries like India or china. The primary goal is to collect a huge amount of data, video footage, and analyzing them per frame. This process is done because self-driving cars lack the computational power and accurate understanding of moving objects simultaneously.
It might take a few years or more for computer vision to be a full-fledged technology that can be used in advanced applications and it could give a new perception to our future. It will majorly impact the field of self-driving cars and automated vehicles.
