It is a myth that machines can reproduce the human visual system. Modern mobile apps integrate modern systems that reflect how far we’ve come from the 1960s, when first university papers were published.
Computer vision is a booming subfield of machine learning and artificial Intelligence. This technology has enormous potential and has a broad range of applications. It aims to replicate the amazing capabilities of human eyesight.
What is computer Vision? What are its current applications in different industries and sectors? What are some examples? What are some common computer vision tasks you might encounter?
This guide will provide an overview of computer vision, as well as how it can be used in real-life. This guide provides a simplified explanation of complex problems. Anyone who has ever heard of computer vision will find it useful.
What’s Computer Vision?
We can understand the scene within an image and then describe it. It is more than simply identifying four people at the front, one street and several vehicles.
These are just some of the basic facts. However, it is evident that all those in front are walking. One person is naked. We even know their names. It is safe to assume that they aren’t at risk of being struck by cars and that their white Volkswagen is well parked. Humans would have no problem describing their clothes, and guessing the material or texture of each outfit.
These skills are what a computer vision program requires. This is the core problem that computer vision solves:
Computer Vision systems need to recognize a 2-dimensional image. It also includes their characteristics, such as their shapes, sizes, colors, textures, or spatial arrangement.
Differentiating computer vision from similar fields
Computer vision is more powerful than image processing and machine perception. Let’s look closer at the differences.
Image processing involves the processing of raw images to create a type of transformation. Image processing is often used to enhance photos, or prepare them to be used in a specific task. Computer vision can be used to describe and expound on images. Images can be processed in a variety of ways, including noise reduction, contrast, rotation and rotation. You don’t need to fully understand the image to perform these functions.
Machine vision systems can be used to produce products in the chemical industry. This is where computer vision can be used to perform certain actions, typically in production lines and production.
They can also check the order of the containers (are the containers empty, clean and free from any damage?). They can also check the properly seal of the product.
Computer Vision can solve more difficult problems like facial recognition (used by Snapchat for applying filters), image analysis, which allows visual searches similar in nature to those Google Images does, and biometric ID.
Humans understand the meaning of images and can interpret handwriting. Computer vision can be a complicated area with many practical applications.
Innovation that relies on artificial intelligence, machine learning and computer vision can be a boon for companies of all sizes.
Let us take a look at some of the most important industrial applications in recent years.
Computer vision is one the most prominent technological trends of recent times. Here are some everyday examples. You can find more information in our Guide to Retail Innovations with Machine Learning.
Brick-and mortar stores use computer vision algorithms with store cameras in order to learn about customers and their behaviour.
Algorithms can identify faces and determine individual characteristics like gender or age. Computer vision techniques can be used by retail stores to track customer movements in store, analyze navigation routes and detect walking patterns. They also measure storefront attention times.
The addition of direction gaze detection allows retailers to answer the key question: Where should the items go in the store? It will enhance customer experience and increase sales.
Computer vision is a powerful tool for developing anti-theft strategies. It can detect if someone is hiding an item in their backpack, or spot shoplifters by using face recognition algorithms.
To manage inventory, two major computer vision programs are available.
The use of security camera images to analyze security cameras can give a computer vision algorithm an accurate estimate of the stock available in a store. Store managers have access to this invaluable information that allows them to spot unusual demand spikes quickly and react efficiently.
Another common use of this algorithm is to analyze shelf space usage and find suboptimal configurations. This algorithm can help you position your items better and find lost space.
Manufacturing plants can experience two major problems: broken machines and defective parts. These issues can cause significant delays and loss of profits.
Computer vision algorithms allow for predictive maintenance. Algorithms can identify potential problems by looking at visual information attached to robots, such as cameras. It is an important contribution to create a system which predicts when a robot used for assembly or packaging will fail.
This is the same principle that applies to defect minimization. It can detect defects in every component of the production process. It allows manufacturers quickly to spot the problem and take corrective action. Sometimes, the problem isn’t so serious that it can stop the production process. A flagged product or route may be used to redirect the product. Sometimes, production must be stopped. It can also train it to classify defects according their severity and types.
Healthcare is flooded with computer vision applications.
The most widely-used medical imaging analysis tool is undoubtedly medical image analysis. It aids in medical diagnosis. Images can be analyzed using MRIs and CT scans. Xrays can also help to detect abnormalities like tumours and neurological signs.
Image analysis techniques, which extract features from images to train detection of anomalies, are often the key. Some applications need more processing. Segmenting images is crucial for analysis of colonoscopies. This will help you detect polyps and prevent colorectal carcinoma.
Volume segmentation using 3D-rendered CT scanning to the thorax
This image shows results of image segmentation, which was used to visualize thoracic elements. Each vital component of the system has been colored by the pulmonary blood vessels (blue), pulmonary veils, the mediastinum(yellow) and the diaphragm.
This technology is used in many applications. These techniques include the ability to quantify calcium in the arteries and determine blood flow without an MRI.
Computer vision does not only apply to medical imaging. Some systems can assist visually impaired persons navigate indoor environments in safety. These systems allow the user to place objects and other objects in a floor plan at any time in real-time. This allows them to see what is happening around them. Eye area analysis and gaze track can help detect cognitive impairments early in life, such as dyslexia and autism. These can often be associated with abnormal gaze behaviour.
Have you ever wondered how autonomous cars are able to “see” what is happening around them? Autonomous vehicles need computer vision to be able to correctly perceive and interpret the environment.
The most difficult problem in computer vision is object recognition in images or videos. This involves classifying objects to determine whether they are vehicles, people, or traffic lights.
Object detection for self-driving cars
This technology uses data analysis from various sources like sensors or radars to enable a car to “see”.
The impact of computer vision on insurance has been significant, especially in claims processing.
Computer vision programs can help clients navigate the process. It can quickly analyze and send images to appropriate agents. It can estimate and adjust repair costs, detect fraud, and determine insurance coverage. This helps clients have a better experience by reducing the time it takes for claims to be processed.
Computer vision can greatly assist in avoiding accidents. Many applications, including those embedded into cars, trucks and industrial machinery, can help prevent collisions. This new era of risk management is likely to transform the insurance industry.
Precision agriculture is an important industry that computer vision has the potential to make a huge impact on.
There are many practical applications for grain production. It is a major economic activity around the world. There are many problems with grain production that humans cannot monitor. Computer vision algorithms can detect and predict pest and insect infestations. Farmers can identify potential problems quickly, which helps them reduce losses and improve product quality.
Another problem farmers face is weed control. They have become resistant to herbicides, which is a major threat to their livelihoods. Robots with integrated computer vision technology can be used to precisely spray herbicides on a farm. This robot reduces pesticide use and costs.
Soil quality is an important factor in agriculture. To identify soil problems and nutritional deficiencies, mobile phones can take images. These applications scan the photos and provide possible solutions.
Computer vision can also be used to sort. A few algorithms can be used to sort fruits, vegetables and flowers by identifying the key characteristics (e.g. The algorithms can identify the main characteristics of fruits, vegetables, or flowers, such as their size, quality and weight. These algorithms also detect and estimate the shipping costs to local markets. This allows for the longest shelf life possible and the fastest time to market.
Defence and Security
Computer vision software can be used to identify high-security customers in high-security organizations such as banks and casino by analysing images taken from security camera cameras.
Computer vision can be an effective ally in homeland security. It is useful for cargo inspection at ports, as well as surveillance of sensitive areas such as hospitals, power stations, embassies and railway stations. Computer vision is the process of analyzing and categorizing images and creating detailed descriptions. It gives decision-makers crucial information in real-time.
Computer Vision is widely used in defense missions, such as surveillance of enemy territory, identification in photographs of enemies, searching and rescue, vehicle and machine movement, and monitoring and recording the movements.
Computer Vision Tasks
How can we reproduce the human visual systems with high precision?
Computer vision involves a number of tasks that can be combined to create complex applications. The most common charges for computer vision are image recognition, and video recognition. These two can help identify the objects within an image.
The most critical task in computer vision is image classification. This allows you to classify an image using a predefined set categories. Let’s look at a binary example. We will categorize photos based upon whether they are tourist attractions. Let’s suppose that this classifier was built and that the following image is available.
The Eiffel Tower
The classifier will answer that the image is part the group of images containing tourist attractions. The classifier will not always recognize the Eiffel tower, but it will tell you that it has seen images and was told that they contained a tourist attraction.
Postcard about tourist attractions in Paris
You may be able to add more categories to the classifier if you are ambitious. You could have a class for each tourist attraction you are trying to identify, Sacre-Coeur, or Arc de Triomphe. Multiple answers could be given for each image, as in the case of the postcard.
Let’s assume that we want to locate tourist attractions on an image. Localization refers the process to locate an object within an image.
The best way to locate an object within an image is to create bounding boxes that surround it.
It is an important task. It can automatically crop objects out of a set. It can combine it and the classification task to quickly create a set of (cropped) photos of tourist attractions.
Object detection is a combination of classification and location. This applies to all objects in an image. Object detection is performed when an image contains any object. The object detection process allows you to classify an image and locate a variable amount of objects.
Object Detection Results
This dense photo shows how computer vision programs can identify many objects such as cars, people, and bicycles.
Complex problems can even be encountered by humans. Because some objects are not in the frame or overlap, they may only partially be visible. It is possible for similar items to have very different sizes.
Counting is an application of object detection. It can count different fruits or measure people during football matches or public demonstrations. There are many ways object detection can be used in real life.
Although object identification and object detection are different, they can be accomplished with similar techniques. Object identification aims to identify instances of an object in images. It’s not about classifying objects, as we have already seen. It’s about locating instances of an object in photographs and if it does, where. One example would be looking for images that have the logo of a business. Another example is to look at real-time pictures from security cameras in order to identify a person.
Following object detection, instance segmentation will be the next step. It doesn’t just involve finding objects in an image, but also creating a mask to match each object.
Instance segmentation results
The image shows the intial segmentation algorithm finding masks to fit cars and Beatles (although it’s incomplete for Lennon).
If they were to be achieved manually, these results would prove costly. But technology allows them to be achieved. France’s law bans children from being exposed by media without their consent. When children are being interviewed or taken outside, segmentation techniques may blur their faces. This is often the case in student strike situations.
Object tracking uses consecutive frames of video to track an object in motion. It is vital for robots assigned with anything such as following goals or stopping a soccer ball (in case of goalkeeper robots). This is crucial for autonomous vehicles that can use high-level spatial reasoning to plan their routes. It’s also useful for human tracking systems. These systems can be used to monitor and analyze customer behaviours, such as during retail or football games.
It’s simple to locate objects by applying object detection in each image of the video sequence. The next step is to compare all instances of each object, and then determine their movement. This method has one drawback: object detection can be expensive for every image. Another alternative is to capture a thing being traced only once (as a rule of thumb, the first time it appears), and then detect its movements using subsequent images without explicitly recognizing them. It doesn’t always have to detect objects. It can be based on motion criteria alone without the object being observed.
Business Use Cases
Computer vision applications are increasingly being used by companies to solve business problems and improve their products. These applications are an integral part of your day. Here are some examples.
Visual Search Engines
Google Images was created in 2001. It made visual search technology open to the public. Visual search engines are able to find images that match certain content criteria. Although keyword searching is common, sometimes we need to present an image to search engines and ask for similar photos. In some cases it is possible to use more specific search criteria such as images of beaches in summer that contain at least ten people.
A variety of visual search engines can be found on websites via APIs and mobile applications.
Google Images is the most popular visual search site. Search inputs for the first two sites can include keywords and images. This is also known reverse image search. Yahoo allows users to search keywords.
Yahoo image search
Some other visual search websites are also worth mentioning. TinEye focuses solely on reverse images and Picsearch allows for text search, but offers extensive coverage.
A wide variety of mobile apps are available. The integration of visual search technology into standard features is gradually becoming a part of the mobile application.
Google Lens replaces Google Goggles, an application that extracts detailed information from photos. To learn more about your cat’s breed, you can snap a photo. Museums can provide additional information on particular works of artwork.
Pinterest Lens is a brand new feature on the ecommerce marketplace. Pinterest Lens will help you discover outfit ideas based around your wardrobe. Just take a picture and Pinterest Lens can show you outfit ideas. Visual search for online shopping has been one of the fastest growing trends.
Face recognition at Facebook
Since the mid-2000s, photographic cameras capable of recognizing facial expressions as well as performing autofocus have been available. However, facial recognition has improved in recent years. The most common (and controversial!) application for recognising a person in a photo or video is to identify them. This is especially true when it comes to security systems. It can also be used in social networks to add filters to people’s faces, and in photo management software to search per person. It can prevent you from voting multiple time in an electoral process. To recognize emotions or facial expressions, facial recognition can be used.
Facebook’s facial recognition tech has drawn both attention and concern. The team was tasked with two main goals: to protect strangers using photos taken by users (see below) and to inform those with visual impairments appearing in photos or videos.
Face recognition at Facebook
Despite its limitations, this technology is still useful in many cases, including the fight against cyber-harassment.
Are you tired of standing in long lines at supermarkets or grocery stores? Amazon Go stores provide a unique experience. Amazon Go stores are transparent and do not offer shipping options. This is possible due to computer vision.
Customers can enter the store to browse the products and then go home without stopping at self-check stations or check stands.
It’s possible. It’s possible because of Walk Out technology from Amazon. Install a mobile application to identify yourself with Amazon. This will generate a QR code that allows you to enter Amazon Go shops. To gain access to the stores’ entrances, customers will need to pass through turnstiles.
These turnstiles scan QR codes of customers as they enter the store. Customers may also be accompanied by others, without the need to download the application.
Demonstration at Amazon Go
Customers have the freedom to move around in the store. This is where computer vision comes in. A variety of sensors are installed in the store. These sensors include motion sensors, cameras, weight sensors and weight sensors that are embedded into the products.
These sensors record information about the activities of each person. These devices monitor customers’ activities in real-time.
One can pick up an item and return it if they choose to. The system will ultimately charge the item to the person who picked it up. If the item is handed to another person, they will be financially responsible. The system creates a virtual shopping basket that holds the items and keeps it updated in real-time. This makes it easy for you to shop.
Customers are allowed to leave the shop after they have finished shopping. Customers can exit the store after they are done shopping.
It’s very convenient to have Tesla’s Autopilot feature. Autopilot can be activated to allow you ride along with your vehicle while it is driving. This is not an impossible dream. This isn’t an autonomous system like Ubers autonomous cars. It is a sophisticated assistant that can drive your car along specific routes. Uber insists on a crucial point: it is the driver who must drive the vehicle in any conditions.
Demonstration Autopilot technology
This video shows Autopilot’s capabilities. Three views from the outside cameras are shown to the right of Autopilot’s main screen. These view show computer vision implementations. Autonomous driving can be achieved using techniques like object detection and tracking.
Tesla cars need to be equipped properly for Autopilot to function. There are eight panoramic cameras with 360o view and a range up to 250m. Ultrasonic sensors are used to detect objects. Radar is used in order to extract information from the environment. All of this allows cars adjust their speed according the traffic conditions.
Tesla’s Autopilot tech is yet another example of computer visualisation’s extraordinary impact on daily activities.
Google is not my business. What can you do right now?
It doesn’t matter whether large companies employ computer vision. However, it doesn’t necessarily mean that they are the best. Any business can benefit from computer vision to improve its efficiency and effectiveness.
How to create a computer-vision program?
It is important to select a strategic strategy for implementing computer vision projects.
Computer vision innovation is only possible if you have a solid business strategy.
Ask these questions to help create a roadmap for a computer vision program.
Will the computer vision system reduce or increase revenue?
Computer vision projects that have a high success rate either lower costs or increase revenues (or both). Your goal must be clearly defined. This will help you and your company grow.
Each computer vision project has its own unique characteristics. A success metric should be a measurable and logical metric. Once you have determined a success metric it is crucial to get it accepted by data scientists and businesspeople.
How can I get more information?
Data scientists should have easy access data to start computer vision projects. They will be required to collaborate with other people in different departments, including the IT department. They will need to have business knowledge to support the project. This would be the main constraint.
Is your organization able to provide the correct data?
Computer vision algorithms don’t work like magic. Computer vision algorithms work only if they have data. They only function as well as the data that they receive. There are many ways to get the data you need, depending on what your goals are.
Your computer vision model is more likely to succeed if you have more information. If you are uncertain about the quality and quantity of the data, you can consult data scientists. They can help you assess the data and decide the best route to access it.
Does the organization have the right data format?
Imagine you have thousands upon thousands of photos from your smartphone. You are trying to train an algorithm to recognize them in the images.
Then you discover that it won’t work because the actual use case was noticing people holding smartphones in various lighting/contrasts/backgrounds, not the smartphones themselves. As your data collection efforts are almost useless, you will need to start all over again.
Not only should you have the correct data type and amount, but also the correct format. It is important to understand whether there are any biases in the data you collect, since algorithms can detect them.
We hope this introduction was enjoyable and helped you understand computer vision, its workings and how they can be used in real life.