Artificial Intelligence (AI) has revolutionized the way we process and analyze images. With the help of AI, machines can now interpret images for a particular task based on the requirement. This has opened up immense opportunities to implement AI-based image processing in every industry.
In this article, we will discuss the basics of image processing in AI, its main purpose, and the various methods, techniques, and tools used for it.
What is Image Processing?
Image processing is the manipulation of an image to amplify the same to generate information out of it. It involves processing an image with the help of complex algorithms to extract meaningful information from it.
There are two methods of image processing: analog image processing and digital image processing. Analog image processing is used for processing photographs, printouts, and other image hard copies. Digital image processing is used for manipulating digital images.
Is Image Processing Part Of AI?
Image processing is a key component of artificial intelligence (AI). It is the process of analyzing and manipulating digital images to extract useful information or to make them more suitable for a specific purpose. AI algorithms are used to automate the process of image processing, allowing computers to recognize objects, identify patterns, and make decisions based on the data.
AI-based image processing can be used for a variety of applications, such as facial recognition, medical imaging, and autonomous vehicles. AI-based image processing is also used in robotics, where robots use visual data to navigate and interact with their environment. Image processing is a powerful tool that can be used to improve the accuracy and efficiency of AI systems.
Main Purpose of Image Processing
Image processing is used for a variety of purposes, including:
- Representing processed data in a visual way that can be understood, such as giving a visual form to invisible objects.
- Improving the processed image quality, such as image sharpening and restoration.
- Image convalescence, which helps in searching images.
- Measuring objects in the image.
- Using pattern recognition to classify objects in the image, locate their position, and get an overall understanding of the scene.
Image Processing Phases
Image processing involves eight phases:
- Capturing the image with a sensor and converting it into a manageable entity.
- Improving the input image quality and extracting details hidden in it.
- Removing any possible corruption like blur, noise, or camera misfocus to get a clearer vision on a probabilistic and mathematical model basis.
- Processing colored images and varied color spaces with pseudocolor or RGB processing. This allows for changes in image resolution and size, be it for the reduction or restoration of images depending on the need.
- Defining the object structure and shape in the image.
- Identifying specific features of a particular object in the image and using techniques like object detection.
- Visualizing the processed data.
- Using Artificial Intelligence and machine learning algorithms to boost the data processing speed and generate quality image results.
Image Processing Methods, Techniques, and Tools
Images captured with regular sensors need preprocessing as some could contain too much noise or be misfocused. There are two detection techniques used for processing digital images as well as for preprocessing:
- Feature detection, which is used to modify and enhance the input image. With various filters available, certain features in the image can be emphasized or removed, and can also reduce the image noise.
- Edge detection is used for data extraction and image segmentation, to find meaningful object edges in the images that are preprocessed.
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To make things easier, there are specific libraries and frameworks that can be used to implement image processing functionalities. Some of the most popular open-source libraries for AI-based image processing are OpenCV, VXL, AForge.NET, and LTI-Lib.
OpenCV is a popular computer vision library that provides ample algorithms and functions that support the algorithms. It includes various modules like image processing module, object detection module, and machine learning module to name some.
VXL is a set of libraries for computer vision and implements a number of popular computer vision technology algorithms and related functionalities.
AForge.NET is a computer vision library with multiple libraries that can be used for image processing and computer vision to neural networks and fuzzy computations.
LTI-Lib is a library that makes it easier to share and maintain code while still providing fast algorithms for real-world applications. It provides a wide range of features that can be used for solving mathematical problems, a set of classification tools, and multiple image processing and computer vision algorithms.
Conclusion
AI-based image processing has revolutionized the way we process and analyze images. With the help of AI algorithms, machines can be taught to interpret images for a particular task based on the requirement. There are immense opportunities to implement AI-based image processing in every industry. It depends on how you want to make the most out of it.
Ace Infoway is curious about AI and machine learning and is aware of the latest improvements in AI-powered image processing. We learn and implement them in our AI projects effortlessly. We’d be happy to assist you in implementing image processing functionality in your current web application or building a custom AI-based solution from scratch.
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