Image processing in intelligent systems is a multi-year project that many people have worked on since the advent of artificial intelligence (AI).
Image processing in its infancy required a large number of manual inputs to provide computer instructions for accessing the output. These machines, known as Expert Systems, were trained to detect images.
According to the Gartner Institute (Technology Research Institute), the number of industries that have used artificial intelligence has increased by 270% in four years.
We need a machine that does more than just detect images. The development of artificial intelligence has helped engineers design software that enhances the human capacity to accurately view, understand, recognize and describe photo and video content.
What is image processing?
In general, the set of processes that are performed on an image to obtain specific information and output from the same image is called image processing. There are two ways to process an image.
Analog image processing; This method is used to process printed and printed photos.
Digital image processing; Image processing in this method is done with the help of sophisticated algorithms and image manipulation.
What is the main purpose of image processing?
With the help of image processing, the processed information can be displayed visually. Like shaping invisible objects. Image processing can be used to improve the quality of the processed image, sharpen the image and restore the images. Image processing helps to measure objects inside the image. Image processing facilitates the classification of objects in the image, recognizes their position, and provides an overall understanding of the image by recognizing patterns.
How many steps does image processing involve?
There are 8 steps for image processing, which we will explain step by step.
1. Image acquisition
Take a photo with the help of a sensor and turn it into a controllable file.
2. Increase image quality (Image enhancement)
You can increase the quality of the input image and extract the hidden details.
3. Image restoration
Any possible errors such as image blur, noise, or lack of focus of the camera are eliminated to get a better view of the possible model and the basis of the mathematical model.
4. Color image processing
Color images and various color spaces are processed by pseudocolor or RGB method.
5. Image compression
This allows you to change the size and resolution of the image depending on your needs.
6. Morphological processing
In this step, the structure and shape of the object in the image are defined.
7. Image recognition
The unique properties of a particular object are detected using techniques such as object detection.
8. Representation and description
This step is about visualization and processed information.
Managing large amounts of data manually is not an easy task. This is where AI and Machine Learning algorithms come into play. The use of artificial intelligence and machine learning increases the speed of information processing and produces quality output. Undoubtedly, to get quality results, you must choose the right tools and methods.
Methods, techniques and tools of image processing
Photos taken with conventional sensors are accompanied by camera noise and lack of focus. That is why they need initial processing. There are two identification methods for digital image processing as well as initial processing:
This method is used to change and improve the input image. With the help of various filters available, some features in the image can be removed or emphasized. Image noise can also be reduced.
2. Edge detection
This method is used for data extraction and image segmentation in order to find object edges in processed images.
There are special libraries and frameworks that you can use to run image processing.
Open source libraries for image processing based on artificial intelligence
Computer vision libraries include general image processing functions and algorithms. Several open-source libraries are available that can be used to develop computer image and vision processing capabilities.
Open Source Computer Vision Library or OpenCV
OpenCV is a popular computer vision library that provides and supports a large number of algorithms and functions. The library also includes various modules such as the image processing module, object recognition module, and Machine Learning module. OpenCV can be used for image processing tasks such as image preparation, compression, enhancement, restoration, and finally data mining.
This library is a collection of libraries for computer vision and implements a number of popular algorithms in this field and related functions.
AForge.NET is a computer vision library with several libraries that can be used for computer image and vision processing, neural networks, and fuzzy computing. AForge.NET also provides a set of utility files and application programs for using these frameworks.
The LTI-Lib library makes it easy to share and maintain real-time applications by providing fast algorithms. This library provides a wide range of capabilities that can be used to solve mathematical problems and provides the audience with a set of classification tools, image processing algorithms, and vision algorithms.
Using artificial intelligence algorithms, machines can interpret images based on a specific need. In each industry, there are unique opportunities for image processing based on artificial intelligence. It’s entirely up to you how you want to get the most out of it.