Image Processing in AI — A Beginner’s Guide
“Artificial intelligence will digitally disrupt all industries. Don’t be left behind.”
– Dave Waters
From the inception of AI, image processing to be incorporated in smart systems is a perennial project for people working on it. In its initial phase, it required a lot of manual input, providing instructions to computers, to get some output. These machines known as Expert Systems were trained to recognize images.
According to Gartner, the total number of AI adopters across different industries has increased by 270% over the last 4 years.
We want the machines to do more than just recognize images. With the advancement in Artificial Intelligence, it has helped engineers to design software that can impersonate the human capacity to observe, understand, recognize and describe the photo and video content with great accuracy.
What is Image processing?
In general terms, manipulating an image to amplify the same to generate information out of it is called image processing. There are 2 methods of image processing:
– Analog image processing is used for processing photographs, printouts, and other image hard copies.
– Digital image processing, which is used for manipulating digital images with the help of complex algorithms
Main Purpose of Image Processing
– Representing processed data in a visual way one can understand, for instance, giving a visual form to invisible objects.
– To improve the processed image quality, image sharpening and restoration work well.
– Image convalescence helps in searching images.
– Helps to measure objects in the image.
– With pattern recognition, it becomes easy to classify objects in the image, locate their position and get an overall understanding of the scene.
Image Processing Phases
There are 8 phases for image processing which goes step-wise:
Image acquisition:
Captures the image with a sensor and converts it into a manageable entity
Image enhancement
The input image quality is improved and also extracts details hidden in it.
Image restoration
Any possible corruption like blur, noise, or camera misfocus is removed to get a clearer vision on a probabilistic and mathematical model basis.
Color image processing
The colored images and varied color spaces are processed with pseudocolor or RGB processing way.
Image compression and decompression
This allows for changes in image resolution and size, be it for reduction or restoring images depending on the need.
Morphological processing
Defines the object structure and shape in the image.
Image recognition
For a particular object, the specific features are identified in the image, and techniques like object detection are used for the same.
Representation and description are all about visualizing the processed data.
It is not easy to accomplish processing massive amounts of data manually. Here’s where Artificial Intelligence and machine learning algorithms become very helpful. The use of ML and AI to boost the data processing speed and generate quality image results. But of course, in order to get high-quality results, you need to pick the right tools and methods.
Image processing methods, techniques, and tools
The images captured with regular sensors need preprocessing as some could contain too much noise or are misfocused. There are two detection techniques to be used for processing digital images as well as for preprocessing.
Filtering
Used to modify and enhance the input image. With various filters available, certain features in the image can be emphasized or removed, can also reduce the image noise, and so on.
Edge detection
Used for data extraction and image segmentation, to find meaningful object edges in the images that are preprocessed.
To make things easier, there are specific libraries and frameworks that can be used to implement image processing functionalities.
Read more: https://www.aceinfoway.com/blog/ai-image-processing
