Big data is playing a prominent role in the area of image processing. Last year, Elsevier published a white paper on this topic, titled An optimal big data workflow for biomedical image analysis.
Nowadays, big data technology plays a significant role in the management, organization, and analysis of data, using machine learning and artificial intelligence techniques. It also allows quick access to data using the NoSQL database. Thus, big data technologies include new frameworks to process medical data in a manner similar to biomedical images. It becomes very important to develop methods and/or architectures based on big data technologies, for complete processing of biomedical image data, the authors write.
There are many new applications of big data in image processing. One factor is the increased applicability of artificial intelligence. AI image processing could offer new solutions to countless challenges across various industries.
The AI Image Processing Renaissance Makes its Mark
AI image processing is offering a solution to many repetitive tasks. This new technology is especially valuable in the manufacturing industry.
Ask any manufacturing manager about the tedious processes of grading, sorting, and processing materials. You will have a more thorough understanding of the benefits of automation. However, the main reason automatic systems haven’t been implemented is due to limitations of sensory.
Until recently, sensory was a gift that was only available to humans and other complex organisms. This situation is starting to change as computer technology evolves through the use of artificial intelligence advances and machine learning. Now, let’s look into the reasons why AI image processing is useful for developing and established businesses.
Automatic Visual Inspection
Quality assurance is crucial to the development of any business. If your company is already spending thousands on component inspection, you can welcome the idea of automatic replacements for human quality assurance inspectors.
Automatic optical inspection solutions based on image analysis are finding production environments where quality inspections are required. For instance, printed visual quality inspections in an area where AOI is located.
Optical Grading
For the last few decades, various industries have used cameras to monitor and sort produce such as fruit, vegetables optically, and fish. However, until recently, the systems required a certain level of human involvement. This is because previous image analysis algorithms were dependent on tuning that had to be performed by human operators.
With the use of AI image processing, optical grading and sorting can become exponentially faster, operate autonomously, and become more accurate.
These systems are good for a range of high-quality inspection process, allowing them to grade and sort products accurately. Here are some examples:
- Nuts and seeds
- Shellfish and fish
- Timber products
- Fruits and vegetables
- Recycled and virgin plastics
Robots With the Ability of Sight
The advances of AI image processing and objective tracking help robots break free from primitive guidance. Basically, they can see the world presented to them until they require human input.
Robotic vision is great for tasks that require navigation and mobility, like moving materials around a warehouse. The system that’s deployed within Amazon’s centers is a great example of AI image processing in real time use.
Robotic image processing technology has improved over the years. These robots use a more sophisticated form of machine learning software, which helps them accurately study the environment and make the correct reactions to the various events and features they encounter.
AI Image Processing Offers Invaluable Solutions in Manufacturing and Beyond
AI image processing has been used by professionals within the manufacturing, finance, health, and other industries. Through its ability to quickly analyze information faster than the human eye, it can complete repetitive tasks without exhaustion.
As technology continues to advance, you need to learn how to implement it within your own organization. It will boost productivity, reduce the number of mistakes, and can be managed by you and a team of developers. Thanks to AI image processing, we’ll start to see more solutions to problems in a precise, automated fashion.
