Image recognition technology has great potential for wide adoption in various industries. This is a field with great potential to be exploited. Let’s find out in the following article together, shall we?
What is image recognition technology?
Computer image or visual recognition is a technique of finding ways to automate all the work that a human visual system can do like Google’s TensorFlow, Facebook’s DeepFace, Microsoft’s Oxford Project. They are all great examples of deep visual recognition systems. On the other hand, hosted APIs allow businesses to save large sums of money for development teams. For example, Google Cloud Vision, Clarifai, Imagga …
Image recognition technology is an ever-expanding field
Big companies like Tesla, Google, Uber, Adobe Systems … also use image recognition technology. To prove that this technology exists around the world, look at recent statistics. Researchers predict that the global market of image recognition will reach $ 38.92 billion by 2021. That’s a huge number. So it’s not surprising that more and more things called imageTech applications are taking advantage of image recognition for different purposes. Even in the business industries.
What helped expand today’s image recognition technology? These are open source tools that make programming easier. Meanwhile, the price is getting cheaper. Open source frameworks and libraries help companies benefit from exponential image recognition technology.
For example, large open databases like Pascal VOC and ImageNet provide access to millions of tagged images. They help image technology companies develop and improve their own machine learning algorithms. For a professional developer, real-time image recognition also uses an open source cross-platform library. It is called OpenCV. This is considered the first point of settlement. At the same time it is the most authoritative resource for image recognition professionals. Another great library for computer vision includes OpenNN, VXL … and many other.
The purpose of use of the image is very diverse
Before we go into more detail about image recognition and how to handle it, we should understand that images can be used in different ways. Serve a myriad of reasons, including:
- Identify the object
- Pattern recognition
- Locate duplicates (exact or partial)
- Search for images by segment
- Image processing (Retouch, …)
- Improve UX mobile apps …
Their applicability is unlimited. Advances in technology open up new opportunities for businesses. Therefore, image analysis is taken to a whole new level. This is thanks to open source solutions and deep learning tools.
The advantages of open source services are heavily mentioned. The image recognition calculations in the cloud have a great advantage. That is, it makes the business more efficient and much cheaper. In addition, internal developers of businesses can integrate their API into the application without any problem.
Image processing technique
In general, image processing has many stages. These include importing, analyzing, manipulating and creating images. There are two methods of image processing: digital (Digital) and analog. Digital image processing and its techniques are what this article refers to.
Computer algorithms play an important role in digital image processing. Developers use many algorithms to solve different tasks. These include digital image detection, analysis, rebuilding, restoration, and compression of image data. There is also image enhancement, image estimates and image spectrum estimates.
Key techniques for digital image processing
Image editing: basically means changing digital images with graphical software tools. For example, Photoshop, Lightroom, Gimp, …
Photoshop is one of the most powerful image editing tools available
- Independent component analysis: separating multivariate signals, calculating into additional components.
- Anisotropic diffraction: commonly known as Perona-Malik diffusion. This method helps reduce image noise without eliminating important parts of the image.
- Linear filtering: another digital image processing technique. It refers to the input signals that change over time. This generates output signals that depend on linear constraints.
- Neural networks are computational models widely used in machine learning. They are used to solve different tasks.
- Pixelation: converting printed images into digitized images (such as GIF).
- Main component analysis: a digital image processing technique. It is used to extract features.
- Partial differential equation, which eliminates image noise.
- Hidden Markov models: a technique used to analyze images in two dimensions (2D).
- Wavelets: stands for a mathematical function used in image compression.
- Self-organizing map: digital image processing technique to classify images into several layers (layers).
There are many different image processing techniques
Technology for image recognition and processing is forecast to thrive in the future. stay tuned for the next article to update on this interesting technology in the next article!
