Artificial Intelligence In Image Processing

  What is image processing?

              

                  

Generally speaking, image processing is manipulating an image in order to enhance it or extract information from it. There are two methods of image processing:

·         Analog image processing is used for processing physical photographs, printouts, and other hard copies of images

·         Digital image processing is used for manipulating digital images with the help of computer algorithms.

In both cases, the input is an image. For analog image processing, the output is always an image. For digital image processing, however, the output may be an image or information associated with that image, such as data on features, characteristics, bounding boxes, or masks.

Today, image processing is widely used in medical visualization, biometrics, self-driving vehicles, gaming, surveillance, law enforcement, and other spheres. Here are some of the main purposes of image processing:

·         Visualization — Represent processed data in an understandable way, giving visual form to objects that aren’t visible, for instance

·         Image sharpening and restoration — Improve the quality of processed images

·         Image retrieval — Help with image search

·         Object measurement — Measure objects in an image

·         Pattern recognition — Distinguish and classify objects in an image, identify their positions, and understand the scene

Digital image processing includes eight key phases: 

1.Image acquisition is the process of capturing an image with a sensor (such as a  camera) and converting it into a manageable entity (for example, a digital image file).

One popular image acquisition method is scraping.

At Apriorit, we’ve created several custom image acquisition tools to help our   clients collect high-quality datasets for training neural network models.

2.Image enhancement improves the quality of an image in order to extract hidden information from it for further processing.

3.Image restoration also improves the quality of an image, mostly by removing  possible corruptions in order to get a cleaner version. This process is based   mostly  on probabilistic and mathematical models and can be used to get rid of  blur, noise, missing pixels, camera misfocus, watermarks, and other corruptions  that may negatively affect the training of a neural networ  

4.Color image processing includes the processing of colored images and different color spaces.Depending on the image type, we can talk about pseudocolor processing (when colors are assigned grayscale  values)or RGB processing (for images acquired with a full-color sensor) 

5.Image compression and decompression allow for changing the size and resolution of an image.Compression is responsible for reducing the size and resolution, while decompression is used for restoring an image to its original  size and resolution.

These techniques are often used during the image augmentation process. When you lack data, you can extend your dataset with slightly augmented images. In this way, you can improve the way your neural network model generalizes data and make sure it provides high-quality results.

6.Morphological processing describes the shapes and structures of the objects in an image. Morphological processing techniques can be used when creating datasets for training AI models. In particular, morphological analysis and processing can be applied at the annotation stage, when you describe what you want your AI model to detect or recognize.

7.Image recognition is the process of identifying specific features of particular objects in an image. Image recognition with AI often uses such techniques as object detection, object recognition, and segmentation.

This is where AI solutions truly shine. Once you complete all of these image processing phases, you’re ready to build, train, and test an actual AI solution. The process of deep learning development includes a full cycle of operations from data acquisition to incorporating the developed AI model into the end system.

8.Representation and description is the process of visualizing and describing processed data. AI systems are designed to work as efficiently as possible. The raw output of an AI system looks like an array of numbers and values that represent the information the AI model was trained to produce. Yet for the sake of system performance, a deep neural network usually doesn’t include any output data representations. Using special visualization tools, you can turn these arrays of numbers into readable images suitable for further analysis.

Implementation of image processing in artificial Intelligence

1. Filtering It is one of the most common techniques for AI image enhancements and modifications. Basically, through filtering, you can emphasize or remove some features from an image with the help of filters. Reduction of image noise is also possible through filtering. Most used filtering techniques are median filtering, linear filtering and Wiener filtering.

2. Edge detection that uses image segmentation as well as data extraction. This AI image processing technique helps in finding edges of objects by detecting discontinuities in brightness. Sobel edge detection, Canny edge detection and Roberts edge detection are a few common ways of edge detection used across the world.

Open-source libraries for AI-based image processing

Computer vision libraries contain common image processing functions and algorithms. There are several open-source libraries you can use when developing image processing and computer vision features:

•  OpenCV
•  Visualization Library
•  VGG Image Annotator

. OpenCV

The Open Source Computer Vision Library (OpenCV) is a popular computer vision library that provides hundreds of computer and machine learning algorithms and thousands of functions composing and supporting those algorithms. The library comes with C++, Java, and Python interfaces and supports all popular desktop and mobile operating systems. 
OpenCV includes various modules, such as an image processing module, object detection module, and machine learning module. Using this library, you can acquire, compress, enhance, restore, and extract data from image.

.Visualization Library

Visualization Library is C++ middleware for 2D and 3D applications based on the Open Graphics Library (OpenGL). This toolkit allows you to build portable and high-performance applications for Windows, Linux, and Mac OS X systems. As many of the Visualization Library classes have intuitive one-to-one mapping with functions and features of the OpenGL library, this middleware is easy and comfortable to work with.

. VGG Image Annotator

VGG Image Annotator (VIA) is a web application for object annotation.       It can be installed directly in a web browser and used for annotating detected objects in images, audio, and video records.
VIA is easy to work with, doesn’t require additional setup or installation, and can be used with any modern browser.

.Smart Cars  

Self-driving cars are the most common existing example of applications of artificial intelligence in real-world, becoming increasingly reliable and ready for dispatch every single day. From Google’s self-driving car project to Tesla’s “autopilot” feature, it is a matter of time before AI is a standard-issue technology in the automotive industry.
Advanced Deep Learning algorithms can accurately predict what objects in the vehicle’s vicinity are likely to do. The AI system collects data from the vehicle’s radar, cameras, GPS, and cloud services to produce control signals that operate the vehicle. Moreover, some high-end vehicles come with AI parking systems already. With the evolution of AI, soon enough, fully automated vehicles will be seen on most streets.