If you were told to name certain things that you’d find in a park, you’d casually mention things like grass, bench, trees, etc. This is a very easy task that any person can accomplish in the blink of an eye. However, there is a very complicated process that takes place in the back of our minds. Human vision involves our eyes, but it also involves all of our abstract understanding of concepts and personal experiences through millions of interactions we have had with the outside world. Up until recently, computers had very limited abilities to think on their own. Computer vision is a recent branch of technology that focuses on replicating this human vision to help computers identify and process things the same way humans do.
What is Computer Vision?
Computer vision is one of the fields of artificial intelligence that trains and enables computers to understand the visual world. Computers can use digital images and deep learning models to accurately identify and classify objects and react to them.
The amount of data that we generate today is tremendous - 2.5 quintillion bytes of data every single day. This growth in data has proven to be one of the driving factors behind the growth of computer vision.
How Does Computer Vision Work?
Computer vision is similar to solving a jigsaw puzzle in the real world. Imagine that you have all these jigsaw pieces together and you need to assemble them in order to form a real image. That is exactly how the neural networks inside a computer vision work. Through a series of filtering and actions, computers can put all the parts of the image together and then think on their own. However, the computer is not just given a puzzle of an image - rather, it is often fed with thousands of images that train it to recognize certain objects.
For example, instead of training a computer to look for pointy ears, long tails, paws and whiskers that make up a cat, software programmers upload and feed millions of images of cats to the computer. This enables the computer to understand the different features that make up a cat and recognize it instantly.
Computer Vision Applications
Computer vision is being used in more areas than you might expect. From detecting early signs of cancer to enabling automatic checkouts in retail places, computer vision has made its way into our lives. Here are some more computer vision applications:
- Face recognition - Snapchat, Instagram, Facebook and many other social media apps use face-detection algorithms to recognize you in pictures and apply filters on your face.
- Surveillance - Surveillance cameras use computer vision to help detect suspicious behavior in public and private locations.
- Image retrieval - Google Images help you find relevant images when you upload an image. There are different algorithms that analyze the content in the image uploaded and return results based on the best-matched content.
- Biometrics - Fingerprint and iris recognition are some common methods in biometric identification that uses computer vision.
- Smart cars - Smart cars use computer vision to detect traffic signs and lights and other visual features when the cars go on auto mode.
Computer Vision Algorithms
Computer vision algorithms include the different methods used to understand the objects in digital images and extract high-dimensional data from the real world to produce numerical or symbolic information. There are many other computer vision algorithms involved in recognizing things in photographs. Some common ones are:
- Object Classification - What is the main category of the object present in this photograph?
- Object Identification - What is the type of object present in this photograph?
- Object Detection - Where is the object in the photograph?
- Object Segmentation - What pixels belong to the object in the image?
- Object Verification - Is the object in the photograph?
- Object Recognition - What are the objects present in this photograph and where are they located?
- Object Landmark Detection - What are the key points for the object in this photograph?
Fig: Computer vision detecting cats in a picture (Source)
Many other advanced computer vision algorithms such as style transfer, colorization, human pose estimation, action recognition, and more can be learned alongside deep learning algorithms.
Computer Vision Benefits
Computer vision can automate several tasks without the need for human intervention. As a result, it provides organizations with a number of benefits:
- Faster and simpler process - Computer vision systems can carry out repetitive and monotonous tasks at a faster rate, which simplifies the work for humans.
- Better products and services - Computer vision systems that have been trained very well will commit zero mistakes. This will result in faster delivery of high-quality products and services.
- Cost-reduction - Companies do not have to spend money on fixing their flawed processes because computer vision will leave no room for faulty products and services.
Computer Vision Disadvantages
There is no technology that is free from flaws, which is true for computer vision systems. Here are a few limitations of computer vision:
- Lack of specialists - Companies need to have a team of highly trained professionals with deep knowledge of the differences between AI vs. Machine Learning vs. Deep Learning technologies to train computer vision systems. There is a need for more specialists that can help shape this future of technology.
- Need for regular monitoring - If a computer vision system faces a technical glitch or breaks down, this can cause immense loss to companies. Hence, companies need to have a dedicated team on board to monitor and evaluate these systems.
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Where Can You Get Started With Computer Vision?
Despite the recent progress in this new field of technology, we are still not even close to unlocking the power of computer vision. However, there are already multiple organizations that have found ways to leverage this technology to solve real-world problems. This only proves that computer vision is growing at an immense rate and is not likely to stop anytime soon. If you want to learn more about computer vision and other deep learning benefits, Simplilearn’s Deep Learning Course (with Keras & TensorFlow) Certification Training is the perfect way to set you on the right path. You will master all the deep learning concepts and models using Keras and TensorFlow frameworks and implement deep learning algorithms. Get started with this course today and prepare yourself for a successful career in deep learning.