The process of identifying objects and understanding the world through images taken with digital cameras is sometimes called “computer vision” or “machine vision.”
This area of artificial intelligence (AI) remains one of the most difficult and challenging. This is partly due to the complexity of many scenes taken from the real world.
This area is created using a combination of statistics, geometry, optics, and machine learning to create a digital representation of the area as seen by the camera. Many algorithms are designed to focus on a narrow goal such as reading and identifying license plates.
Key areas of computer vision
AI scientists are often focused on specific goals. These challenges have become important subdisciplines. This focus often leads to higher performance, as the algorithms are more focused on a specific task. Although machine vision’s ultimate goal may seem impossible, it is possible to answer simple questions such as reading each license plate that passes by a toll booth.
- Face recognition: Identifying people by locating faces in photos. It is possible to organize videos and photos by using the ratios of facial features. It can be used to identify the person in certain cases and provide security.
- Object recognition: The ability to identify the boundaries between objects allows you to segment images, inventory the world, and guide automated processes. Sometimes algorithms can accurately identify animals, plants, or objects. This talent is essential for applications in industries plants, farms, and other areas.
- Structured recognition: The algorithms are more accurate when the setting is predictable and easy to understand, which is often the case in an assembly line or industrial plant. Computer vision algorithms are a great way to improve safety and quality control, particularly for repetitive tasks.
- Structured lighting: Certain algorithms employ special patterns of light, which are often generated by lasers to simplify the work and give more precise answers than can often be generated from scenes with diffuse lighting from many sources, often unpredicted.
- Statistical analysis: In some cases, Sometimes statistics can be used to track people and objects. Tracking the speed and distance of steps can help identify people.
- Color analysis: Questions can be answered by careful analysis of colors in images. You can measure a person’s heartbeat by observing the slight redder waves that move across their skin with each beat. The distribution of colors can help identify many bird species. Some algorithms use sensors that detect light frequencies beyond the human vision range.
Best applications for computer vision
Although the task of teaching computers how to see the world is still daunting, there are some applications that can be applied. Although they may not be perfect, they can provide enough information to be of use. They are trustworthy enough to be trusted by users.
- Facial recognition: Many software programs and websites that organize photos provide some way to sort images by the people who are using them. They may be able to locate all images that have a specific face. These algorithms are good enough to complete the task in part. Because users don’t expect perfect accuracy, misclassified photos are of little consequence.
- These algorithms have some applications in law enforcement and security. Many people worry that their accuracy may not be sufficient to support a criminal prosecution.
- 3D object reconstruction: Manufacturers often scan objects to create 3-D models is a common practice for manufacturers, Game designers, and artists The lighting can be controlled using a laser or other means. This allows for the accurate reproduction of many smooth objects. You can also feed the model to a 3D printer. Sometimes, you can create a three-dimensional reproduction by editing. Reconstructions that are not controlled by lighting can have wildly different results.
- Mapping and modeling: Many people are now using images from drones, planes, and cars to build accurate models of roads, buildings, and other areas of the globe. It all depends on the quality of the camera sensors as well as the lighting conditions at the time it was taken. While digital maps can be used to plan travel, they are accurate enough and are constantly refined. However, complex scenes may require human editing. Many building models are accurate enough to be used in construction and remodeling. For example, roofers often bid on jobs based upon measurements taken from digitally constructed models.
- Autonomous vehicles: It is common for cars to follow lanes and keep a good distance from other vehicles. Structured lighting is expensive and larger, but it allows for accurate tracking of all objects in unpredictable and shifting street lighting which is more expensive, bigger, and more elaborate.
- Automated retail: Machine vision algorithms are used by mall owners and store managers to track customer shopping habits. Some companies are testing automatic charging customers for picking up an item they don’t want to return. Mounted scanners can also be used to track inventory and measure loss.
How established players are tackling computer vision
All large technology companies offer products that use machine vision algorithms. These tasks are narrowly focused and can be applied to specific tasks such as sorting photo collections or moderating social posts. Microsoft is one example of a company that has a large research team who are constantly exploring new topics.
Google, Microsoft, and Apple all offer photography websites that allow users to store and catalog their photos. A valuable feature is the ability to use facial recognition software to organize collections. This makes it easier for users to find particular photos.
These features can be purchased as APIs that other companies can use to implement. Microsoft also provides a database of facial features for celebrities that can be used to organize images gathered by news media over time. You can also search for your “celebrity sibling” in the database.
Some tools provide more detailed information. Microsoft’s API offers a “describe picture” feature which will search multiple databases to find recognizable details such as the appearance of a landmark. The algorithm will return descriptions of objects and a confidence score that measures how accurate the description may be.
Google’s Cloud Platform gives users the choice of training their own models or using a large number of pre-trained models. A prebuilt system is also available that enables visual product search to assist companies in organizing their catalogs.
AWS’ Rekognition service focuses on the classification of images using facial metrics and trained object models. It offers content moderation and celebrity tagging for social media apps. One prebuilt application can be used to enforce safety regulations in the workplace by monitoring video footage to make sure that all employees are wearing personal protective equipment (PPE).
A large number of computing companies are involved in autonomous travel research. This challenge relies on several AI algorithms and machine vision algorithms. Google and Apple are widely believed to be working on cars that combine multiple cameras to map a route and avoid obstacles. They use both traditional cameras and those that use structured lighting, such as lasers.
Machine vision startup scene
Many machine vision startups focus on the topic of building autonomous vehicles. Pony AI, Pony AI, and Wayve are just a few examples of startups that have received significant funding. They are developing the software and sensors systems that will enable cars and other platforms to navigate on the streets.
These algorithms are being used by some manufacturers to improve their production lines. They can be used to guide robotic assembly or inspect parts for any errors. SaccadeVision creates three-dimensional scans to check for defects. Veo Robotics developed a visual system to monitor “work cells” and watch out for dangerous interactions between robots and humans.
It is possible to track people as they travel around the world. This can be done for safety, security, or compliance. VergeSense is a company that has created a “workplace analysis” solution to help companies optimize the use of hot desks and shared offices. Kairos creates privacy-savvy facial recognition tools to help companies understand their customers and improve the customer experience through options such as more aware kiosks. AiCure uses facial recognition to identify patients and dispense the right drugs. It also monitors them to ensure they are taking the medication. Trueface monitors customers and employees to detect high temperatures and enforces mask requirements.
Other machine vision companies focus on smaller tasks. Remini offers an “AI Photo Enhancer” online service, which adds detail and enhances images by increasing their apparent resolution.
What machine vision cannot do?
Machine vision algorithms are more difficult than other areas such as voice recognition because of the gap between AI and human abilities. When algorithms are asked to recognize objects that are mostly static, they succeed. For example, people’s faces are relatively fixed. The distances between the major features such as the nose and the corners of the eyes are rarely very different. Image recognition algorithms are skilled at finding faces in large collections of photos that have the same ratios.
The variety of chairs can make it difficult to understand basic concepts such as what a chair is. There are many different types of chairs, some even millions. While databases are being built to look for exact copies of known objects, it can be difficult for machines and humans to classify new objects correctly.
The quality of the sensors is a particular problem. Digital cameras are unable to match the performance of human eyes in low light conditions. Some sensors can detect colors beyond the range of the human eye’s rods and cones. This ability allows machine vision algorithms to detect objects that are invisible to the human eye. It is a major area of research.