Knowledge&Technology

5 Applications of Computer Vision Using Machine Learning

In this article, you will find five applications of computer vision using machine learning. But let's start to discuss the differences first.
Alan Kilich
3 minutes

The possible uses of computer vision have recently gotten more attention. In fact, it's a technology that imitates human vision and can do sophisticated analyses of pictures. However, the distinction between the scope of computer vision and machine learning seems to be unclear to many. That makes sense, given how closely both technologies' applications overlap.

Artificial intelligence (AI), a catch-all phrase that includes a variety of other technologies as well, consists of both machine learning and computer vision. In this article, you will find five applications of computer vision using machine learning. But let's start todiscuss the differences first.

What Is Computer Vision?

 Briefly, ‘’Computer Vision’’ is used in order to provide computer systems with visual perception skills similar to those of humans. It is a collaborative discipline that makes it possible for computer systems to correctly process, examine, and understand our visual environment.

For instance, computer vision enables machines to recognize essential data from photos and video files in the same manner that people do. The goal is to give computers access to this "natural"visual quality so they can comprehend and evaluate complicated digital systems just as people can—possibly even better.

Modern computer vision uses machine learning,an aspect of artificial intelligence concerned with "training"computers to pick up new skills on their own over time. A machine learning system, however, will consider prior experiences and judgments to choose the most suitable response, in contrast to a system that constantly follows a set of predefined rules or instructions. Furthermore, this may be accomplished with little to no human involvement.

What is Machine Learning?

Simple artificial intelligence is a subset or component of machine learning. Machine learning-enabled devices can evaluate and interpret digital data without human intervention

Statistical concepts and algorithms are frequently used together in machine learning to create models that can generatejudgments from incoming data. As a result, Machine learning is used in various industries, from supercomputers to sophisticated software engineering.

So, a crucial question will be: What is computer vision in the context of machine learning? Machine learning ideas areapplied to computer vision because machine learning plays a significant role in computer vision nowadays.

What are the differences betweenMachine Learning and Computer Vision?

 Computer vision and machine learning are subfields of artificial intelligence that use advanced algorithms to quickly and accurately detect, interpret, and discover patterns in visual input, asmentioned above. As a result, because both of these technologies have many things in common, it would be best to distinguish between them based on their use cases rather than their differences.

 To give an idea, a typical system for both computer vision and machine learning will comprise many of the same elements and specifications, such as; a lens or other imaging sensor that may be used as an imaging device, appropriate lighting for the intended use, or computer software that can analyze photos while using computer vision or machine learning techniques.

Tasks of Computer Vision

 Even if it's increasingly simpler to get the resources needed to create computer vision applications, it's crucial to establish their purpose early on. It is simpler to get started if projects andapplications are focused, validated, and defined in terms of particular computer vision tasks.

 Here are some examples of Computer Vision applications;

 

●      A puppy, an apple, or a person'sface are examples of images that may be classified using image classification. More specifically, it can correctly guess which class a given picture belongs to. A social network corporation would wish to utilize it, for instance, to recognize and sort out offensive photographs shared by users automatically.

●      Object detection may employ image classification to identify a particular class ofpicture and then recognize and tabulate its existence in an image or video. Detecting damage on a manufacturing line or locating equipment that needs repair are a few examples.

●      Afte an item is found, it is followed or tracked. This operation is often carried out using real-time video streams or a series of sequentially taken pictures, called object tracking. For instance, autonomous vehicles must monitor moving things like people, other cars, and road infrastructure in addition to classifying and detecting them top revent crashes and follow traffic regulations.

●      Instead of focusing on the metadata tags attached to the photos, content-based image retrieval employs computer vision to browse, search, and retrieve images from massive data repositories. This activity may use automatic picture annotation instead of human image labeling. These activities can be used in digital asset management systems to improve search and retrieval precision.

Applications of Computer Vision using Machine Learning

 Here are some of the applications where Computer Vision uses Machine Learning.

1.Healthcare

One of the richest sources of information is medical imaging data. But here's the disclaimer:

Doctors must manually process patient data and do administrative tasks without the proper technologies.

Fortunately, as time passed and technology improved, the healthcare sector became one of the quickest to accept new automation technologies, such as computer vision.

Here are some applications where computer vision using machine learning is employed below;

- Cancer Detection

Comparing malignant and non-cancerous cells inphotos enables medical professionals to spot abnormalities and alterations.

Automated detection enables a quicker cancer diagnosis using information from magnetic resonance imaging (MRI) scans. Breast and skin cancer screening using computer vision has already been shown effective.

- X-Ray Analysis

Computer vision can be effectively used in the context of medical X-ray imaging for treatment and research, MRI reconstruction, or surgery planning.

Although most doctors still use manual X-ray image analysis to diagnose and treat illnesses, computer vision can successfully automate the procedure, boosting efficiency and accuracy.

- Diagnosis of Viral Infections

Computer Vision can be used for diagnosing viral infections like Covid-19. For X-ray-based COVID-19 diagnosis, several deep learning computer vision models are available.

- Disease Level

Computer vision recognizes severely unwell patients (critical patient screening) to focus on medical care. It has been used for covid patients and observed that patients breathe more quickly thanks to disease level diagnosis.

It is possible to accurately and discretely screen many ill people for aberrant respiratory patterns using deep learning and depth cameras.

2. Retail

Retailers can get massive amounts of visual data from cameras deployed in their facilities that will help them create a better customer and staff experience. The digital transformation of the actual industry is now much more feasible thanks to the development of computer vision systems to analyze this data.

Here are a few of the retail industry's most widely used computer vision applications below;

-  Counting People in the Shop

Data examples train computer vision algorithms to detect and count people as they are noticed. Such people measuring equipment may be used in instances with COVID-19, where only a certain number of customers are let in a shop at once to gather data on the business's success.

-  Self Check-out

Developing computer vision-based systems that comprehend consumer interactions and track goods movement has made autonomous checkout conceivable. Modern retail stores are using cashier-less checkouts toaddress problems like lengthy lines.

-  Customer Behavior Analysis

Using camera-based technologies can upcycle the video stream from popular, affordable security surveillance cameras. Machine learning algorithms identify individuals invisibly and contactless to study time spent in various locations, wait periods, and line up times andevaluate the service's quality.

Customer behavior analytics can promote customer happiness, optimize retail shop layouts, and objectively measure key performance indicators across many locations.

3. Banking

Banking is one of the main areas where Computer Vision and Machine Learning are employed, and it helps bank officers do their jobs efficiently.

-  Money Counting

For example, while counting money, scanners use computer vision and calculate the money quickly and accurately. This also prevents counterfeiting by enabling the distinction of counterfeit money.

-  Portfolio Management

Portfolio management is one of Computer Vision's most significant benefits for the banking industry. Aside from that, cutting-edge technology can put everything at your fingertips and silhouette and do away with in-person banking.

- Fraud Security

Banks and other financial organizations are particularly vulnerable to fraud. Therefore, having a reliable and secure system is crucial to safe guarding their clients' interests. Financial institutions must thus actively watch out for possible fraud schemes, including stock fraud, phone or SMS fraud, illicit remittance through internet banking,etc.

4. Security and Surveillance

Computer vision employs various technologies to analyze and comprehend visual data using computers. The main objective of computer vision in applications for the security and surveillance industries is to automate human oversight. Real-world scenarios may now be recorded and digitally preserved, opening up new possibilities for threat detection that is more accurate and early, risk quantification, and real-time security evaluations.

-  Weapons’ or Dangerous Objects’Detection

Deep learning is used in real-time object identification to locate particular items in video scenes. Applications for object identification in security often include the detection of weapons (such as knives or guns) or protective gear. Employing software like Cameralyze helps to detect any weapons or dangerous objects thanks to their advanced AI-based technologies.

- Virtual Fencing

A typical function of AI vision surveillance systems is the virtual fencing of sensitive areas. To identify incursionoccurrences, particular locations of interest indicate virtual barriers.

- Anomaly Detection

Anomaly detection in traffic, the subway, on campuses, trains, boats, buildings, and public spaces is done using AI videoanalysis. A few examples of anomaly detection in visual AI have stopped vehicle identification, fear detection, and the recognition of unusual pedestrian behavior.

Software like Cameralyze prepares you for all abnormal situations. Thus, by analyzing live broadcasts, you will prevent unwanted situations.

5. Automotive

To assure the quality of the product and the assembly process, automotive manufacturing assembly activities involve visual inspections such as scratch detection on machined surfaces, item identification, and selection. Computer vision and machine learning are helpful in this situation.

-  Autonomous Driving

Computer vision is a component of autonomous vehicles like self-driving cars. The automobile has cameras that provide a 360-degree field of view and a wide range. The cameras help with lane locating, estimating road curvature, detecting obstacles, detecting traffic signs, andmany other tasks. Object detection and classification must be implemented using computer vision.

- Inspection in the Assembly Lines

Deep learning applications for automotive AIvision have huge promise for part inspection and defect location. Before anycar is put together, it is crucial to find any faulty produced components, such as brake parts, and the manual examination is challenging to carry out on your own.

Deep learning techniques are more reliable at spotting many flaws than traditional image processing techniques.

Conclusion

Despite all the hype surrounding artificial intelligence, machine learning, and computer vision, it was obvious that computer vision still falls behind our biological vision. This is the situation that both business owners and developers are in. In addition to the high costs, resource scarcity, and restrictions on general learning algorithms that come with this sort of business.

However, at Cameralyze, we think that innovation and technology will help us develop continuously. Our committed team of computer vision and machine learning professionals provides ongoing assistance to reach the technologies such as object identification, face recognition, demographicanalysis, etc., and solutions you need to scale up your company.

Contact us right away to learn more and get a start free now, or get in touch with our team.

 

 

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