What Is Computer Vision: Applications, Benefits and How to

Data Science is the science most needed to solve various current problems. Data Science is an interdisciplinary field involving multiple other areas, such as mathematics, statistics, programming, and business.

In its development, it is common for Data Science to be combined with other sciences so that the results obtained are more effective and according to needs. One of them is connected with Artificial Intelligence (AI).

Data in Data Science can be quite diverse, from structured and semi-structured to unstructured. AI can help overcome problems with this data, including unstructured data.

Computer Vision is one part of Artificial Intelligence. Computer Vision is a technology that allows computers to see and recognize objects around them like humans.

In an increasingly rapid technological development, Computer Vision is one of the new technologies utilized to be combined with Data Science with the help of Deep Learning.

So that the facial recognition process becomes easier to do. So, how about Computer Vision? Come on, see the article!

What is Computer Vision

Computer vision is a science in informatics engineering that allows a computer to see objects or objects around it. So that by being able to see things around it, the computer can analyze objects or images in front of it so that the information can be received and can produce certain commands.

Computer vision combines cameras, edge or cloud-based computing, software, and artificial intelligence (AI) so that systems can “see” and identify objects.

Intel has a broad portfolio of AI-enabled technologies, including CPUs for general-purpose processing, computer vision, and vision processing units (VPUs) for acceleration.

Computer vision uses deep learning to form neural networks that guide systems in processing and analysis. A fully trained computer vision model can recognize objects, detect and recognize people, and even track movement.

How Does Computer Vision Work?

Computer vision will break the existing image into many different parts. The neutron network will identify each of the existing features, then combine them into one unified piece.

The computer will automatically label the image. That is why the more data that is used as learning material by computers, the ability to predict the image correctly will also increase.

The laptop will utilize algorithms that can analyze colour, shape, distance between objects, and so on until the computer can label all objects as certain objects.

Even though the entered object is three-dimensional, the computer will still define it as a two-dimensional object due to its limited ability to remember linear lines in one plane.

This two-dimensional image will then be translated as a number. Once added up, it will form a certain value representing the colour code.

Each pixel will contain three main colours, which are red, green, and blue. Colour identification is important enough to provide a detailed explanation.

Computer Vision Techniques

In general, the techniques used by Computer Vision in processing one or a group of images can be divided into five types, namely:

Image Classification

The computer will classify the image into which class.

Object Detection

It is used to detect the presence of an image. It can also be combined with image classification.

Object Tracking

It will usually be used to track the position of objects in data in the form of video, both in real-time and in recordings.

Semantic Segmentation

It differentiates images by differentiating the pixels in an image to be included in a group. Later, after being in the group, it can be classified and labelled.

Instance Segmentation

It is almost similar to Semantic Segmentation, except that this technique immediately distinguishes each unit of the object in the image into different pixel groups and classes.

Computer Vision Function

Following are the functions of computer vision

1. Obtain Clearer Information Thus Reducing Errors

By utilizing Computer Vision, we can see objects more clearly, as with a camera that can capture objects or images more clearly.

2. Improving Image Quality

Using computer vision technology, pictures or photos were taken with the camera become clearer. This is called image processing, so it is possible to reduce noise in the image with image processing.

3. Able to Analyze

Computer Vision allows the computer to analyze the next process. An example is the use of a scanner. Besides being able to capture images, the computer system is also capable of processing them for the next step.

4. Computers Can Understand Images or Objects

In the industrial world, such as manufacturing, computer vision will greatly assist the production process of an item. So that with computer vision allows the computer to understand what to do without having to go through human intervention.

Computer Vision Application

Computer Vision is widely used in business, transportation, health, and daily life. The following is an example of using Computer Vision:

1. Face Recognition.

This technology is one of the technologies quite close to our daily lives. Facebook, for example, can recognize the user’s face and add a label. In addition, we can find face recognition on several smartphones where the key uses face ID.

2. Self Driving Cars

The self-driving car is a technology that allows cars to drive without a driver. Some of the uses of Computer Vision in self-driving cars are detecting lane lines, detecting obstacles and traffic signs, including red lights, and calculating steering angles.

3. X-Ray & CT Scan

In the medical world, X-Ray and CT scans are important. By utilizing Computer Vision, doctors can create three-dimensional interactive models so that the process of interpreting medical images becomes easier.

4. Retail Without Cashier

Overseas, another use of Computer Vision is the presence of retail outlets that do not have cashiers as Amazon Go does. They will detect items purchased by customers using a camera and will automatically bill them via Amazon Prime.

Computer Vision Challenges

Achieving human-level vision in machines is challenging due to both technical and fundamental difficulties. Understanding the intricacies of human vision involves more than just comprehending the functions of receptors such as the eye.

It also requires a deep understanding of how the brain processes visual information.

Although progress has been made in mapping out this process and discovering the tricks and shortcuts used, much still remains to be uncovered, as studying the brain is a vast and ongoing endeavor.

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