Computer vision is one of the most powerful and compelling types of AI. This is a field of computer science that replicates the human vision system. It also enables the computers to convert objects in image and video form. It allows the machines to process video and images just like humans. Initially, computer vision worked in limited areas only.
After the recent massive development of AI, it got a massive boost. Due to deep learning and neural networks, computer vision has taken great leaps. Let’s start.
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What is Computer Vision?
Let’s first start with the definition. Computer vision is all about developing techniques to help computers “see” digital images. It focuses on the analysis of visual inputs by computer. It is a multidisciplinary field that is a combination of AI and ML.
As we have mentioned earlier, it replicates the complexity of human vision. But in some cases, CV interprets images even better than human vision. Now let’s look at image processing.
Computer Vision and Image Processing
Computer vision is slightly different from image processing. However, they are closely connected. In image processing, the computer creates a new image from the existing one. Generally, it simplifies and enhances the content. On the other side, the computer vision system first recognizes the image.
Image processing is a part of computer vision, but it works more complex way. This process falls into the category of digital signal processing.
For example, we can take the removal of digital noise from an image. Cropping the bounds of the image or normalizing photometric properties also fall into this. So, it identifies and analyses the image before processing. Now let’s discuss how it replicates the complex procedures of human vision.
How Does Computer Vision Work?
It is all about the recognition of patterns like humans do. With the help of machine learning, we can teach the computer to analyze images. We train computers on a huge amount of visual data. At that time, computers process images and label objects on them.
By this, the computer finds patterns in those objects. The computer can identify a particular image from millions of the same images. According to experts, machines interpret images as a series of pixels. Each of the pixels has a specific colour value.
With the help of machine learning, the computer analyses and stores the image details. Artificial intelligence helps the computer to identify and use that information later when required. This is how computer visions work.
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Where can We Apply Computer Vision Technology?
It is an integrated part of our life. Here, we will explain those in detail:
1. Content Organization
We are using a computer vision system widely to organize contents. Apple Photos is a perfect example of this category. This app accesses the photos of the gallery and automatically adds tags.
It allows the users to browse the more structured collection of photographs. Also, it creates a curated view of your best moments. This feature makes Apple Photos cool for the users.
2. Facial Recognition
This technology is useful to match photos of people’s faces to their identities. Facial technology is one of the crucial technologies that we use every day. Facial recognition is an integrated part of biometric authentication.
Recently, it is becoming popular in mobile devices due to face-lock security. Most smartphones are offering the option of unlocking by showing face. To do this, users must authorize the face in that device.
Once registered, then they can unlock their phones with the front camera. Mobile devices process the image and determine whether the owner is holding the phone or not. The whole process works fast with the help of the said technology.
3. Self-driving Cars
Smart vehicles with auto-pilot mode are getting much popularity nowadays. These cars have so many cameras to take pictures and videos of their surroundings. These cameras send videos and images to the computer vision software.
Then the system processes the inputs to identify objects around the car. It also determines road marking, traffic lights etc. All of these happen within a second.
4. Health-sector
The image plays an important role in medical diagnosis as it deals with 90% of accuracy. Many diagnoses are based on image processing like X-rays, MRI, and mammography. Also, image segmentation has proved its efficiency during medical scans analysis.
For example, algorithms can detect diabetic retinopathy very fast. Another notable example is cancer detection. Computer vision software can detect cancer metastasis faster than human doctors.
5. Agriculture
Problems like weeds emergence or nutrient deficiency are very common in the field of agriculture. Computer vision plays a vital role here to solve these issues. We can process images of drones, satellites, etc.
It helps us to detect the problem in the early stage. It helps to avoid unnecessary financial losses.
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Limitations of Computer Vision
Though computer vision has lots of efficient applications, it has some weaknesses too. The human visual system identifies objects based on a 3D model. Also, we can transfer knowledge from one domain to another.
But in the case of Computer vision, there is no such system. It works with the neural network, which memorizes the images by pixels. So, the system needs to see many examples to recognize every object successfully.
Moreover, it is still struggling to understand the context of images. Also, it can determine the relationship between the objects where humans can easily do. You have to train the neural network with lots of examples to solve these issues.
Conclusion
Computer vision is still a newborn, and it has a long way to go. Already it has achieved a huge acceptance in many sectors. The massive development of artificial intelligence has made this possible. However, it has certain limitations to overcome. Experts are working on the training process to make it easier. Seeing the recent AI development, we can expect a high time for computer vision.
