Image recognition and processing technology
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Image recognition and processing technology is an advanced application that is popularly applied in the present era. In particular, technology companies are currently using image technology to apply in the production of many devices. To understand more about this amazing technology, please follow the below article. Image recognition and processing technology 
The role of the use of imaging technology Imaging technology helps you do the following: 
digital image processing techniques Especially for image processing techniques, this work goes through many stages such as: import, analysis, manipulate and create images. There are 2 popular image processing methods, including: Digital and analog. For image processing techniques, computer algorithms play an important role. Common techniques used to process digital images include: 
Pixelation Hopefully, with the above sharing, you can see that image recognition and processing technology are really popular in social life today.
Image recognition and processing technologyWhat is image recognition technology?
In fact, there are many definitions of image recognition technology, they are also known as computer vision. We can understand simply that this is considered as a technique to perform a search for ways to automate the work that a human visual system can do. Image recognition is widely used in our lives. The following programs, for example, use image recognition systems: Google's TensorFlow, Microsoft's Oxford project, Facebook's DeepFace. Not only that, the application program interfaces - hosted APIs such as Google Cloud Vision, Clarifai, Imagga, ... are all allowed for businesses to use. They are considered as a big cost-effective solution for development teams. Image recognition services have many outstanding advantages. In addition to calculating that help to recognize image on cloud computing, the application also helps to make business efficient and cheaper. The APIs are also integrated within the company, ensuring no copyright problems. Currently, the API developers are also working on developing a new business in the field of image recognition.The role of the use of imaging technology
Image recognition technology has great potential, therefore it’s widely used in many industries. You can see that the leading companies in the world such as: Tesla, Uber, Adobe Systems, Google, .. all use this technology. According to studies, the image recognition industry is used all over the world. According to the estimates of researchers, the image recognition industry will reach 38.92 billion USD by 2021. Many imagetech applications utilize image recognition for many different purposes. So, this technology holds a particularly important place in society. The role of the use of imaging technology- Opening source tools make programming easier.
- Opening source frameworks and libraries help many companies use and benefit exponentially.
- This is an opening source cross-platform library for professional developers to recognize real-time images.
- Primary Image Recognition Technology as an excellent library for computer vision including Open, VXL and many others.
- With this technology you can use it in many different ways and unlimited applications. It can be seen that image analysis has been brought to a new level thanks to opening source solutions and deep learning tools.
Today's popular digital image processing techniques
Currently, all industries use identification technology. Images developed in mobile and web software serve many purposes such as: object recognition, locating copies, pattern recognition, searching segmented images, processing images, improving mobile application, ... digital image processing techniques- Image editing: this technique often uses graphics software such as Photoshop, Lightroom, Gimp, ...
- Image recovery: is used to get back lost information.
- Independent component analysis: this technique helps to separate the computed multivariate signal into additive components.
- Anisotropic diffraction (Perona-Malik diffusion): This technique is used to reduce image noise while preserving important parts of the image.
- Linear filtering: this technique refers to the time-varying input signals. In addition, they also generate output signals dependent on the constraint of linearity.
- Neural networks: These computational models are used to solve different tasks.
- Pixelation: used to convert printed images into digitized images.
- Analysis of main components: to extract features.
- Part of differential equations: used to eliminate image noise.
- Hidden Markov models: used to analyze images in two dimensions.
- Wavelets: to compress images.
- Self-organizing map: used for digital image processing, image classification into several layers.
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