ML based Image Processing

 What is Image Processing?

Image processing is that the process of remodeling a picture into a digital form and performing certain operations to induce some useful information from it. The image processing system usually treats all images as 2D signals when applying certain predetermined signal processing methods.

There are five main sorts of image processing:

• Visualization - Find objects that aren't visible within the images
• Recognition - Distinguish or detect objects within the image
• Sharpening and restoration - Create an enhanced image from the first image
• Pattern recognition - Measure the varied patterns round the objects within the image
• Retrieval - Browse and search images from an oversized database of digital images that are kind of like the initial image

Benefits if Image Processing.

The implementation of image processing techniques has had a large impact on many tech organizations. Here are a number of the foremost useful benefits of image processing, no matter the sector of operation:

1. The digital image may be made available in any desired format (improved image, X-Ray, photo negative, etc)
2. It helps to enhance images for human interpretation
3. Information is processed and extracted from images for machine interpretation
4. The pixels within the image are often manipulated to any desired density and contrast
5. Images will be stored and retrieved easily
6. It allows for simple electronic transmission of images to third-party provider
   
   
   
   
   
   Fig.1 Different phases in Image Processing.

What is Machine Learning?

Machine Learning is that the field of study that provides computers the potential to be told without being explicitly programmed. ML is one amongst the foremost exciting technologies that one would have ever bump into. because it is obvious from the name, it gives the pc that produces it more just like humans: the power to be told. Machine learning is actively getting used today, perhaps in more places than one would expect.

Why is it important?

Machine learning is very important because it gives enterprises a view of trends in customer behavior and business operational patterns, likewise as supports the event of recent products. Many of today's leading companies, like Facebook, Google and Uber, make machine learning a central a part of their operations. Machine learning has become a big competitive differentiator for several companies.

Working of ML Image Processing.

Typically, machine learning algorithms have a selected pipeline or steps to be told from data. Let's take a generic example of the identical and model a working algorithm for a picture Processing use case.

Firstly, ML algorithms need a substantial amount of high-quality data to find out and predict highly accurate results. Hence, we'll should confirm the photographs are well processed, annotated, and generic for ML image processing. this is often where Computer Vision (CV) comes into the picture; it is a field concerning machines having the ability to know the image data. Using CV, we are able to process, load, transform and manipulate images for building a perfect dataset for the machine learning algorithm.

For example, say we wish to create an algorithm which will predict if a given image incorporates a dog or a cat. For this, we'll have to collect images of dogs and cats and preprocess them using CV. The preprocessing steps include:Converting all the pictures into the identical format.
Cropping the unnecessary regions on images.
Transforming them into numbers for algorithms to be told from them(array of numbers).

Computers see an input image as an array of pixels, and it depends on the image resolution. supported the image resolution, it'll see height * width * dimension. E.g., a picture of a 6 x 6 x 3 array of a matrix of RGB (3 refers to RGB values) and a picture of a 4 x 4 x 1 array of a matrix of the grayscale image.

These features (data that's processed) are then employed in the following phase: to settle on and build a machine-learning algorithm to classify unknown feature vectors given an in depth database of feature vectors whose classifications are known. For this, we'll have to choose a perfect algorithm; a number of the foremost popular ones include Bayesian Nets, Decision Trees, Genetic Algorithms, Nearest Neighbors and Neural Nets etc.

Below could be a screenshot of classic machine learning image processing workflow for image data:

The algorithms learn from the patterns supported the training data with particular parameters. However, we are able to always fine-tune the trained model supported the performance metrics. Lastly, we will use the trained model to create new predictions on unseen data.

In the next section, we’ll review a number of the technologies and frameworks we will utilise for building a Machine Learning image processing model.

Applications of Image Processing using ML.

1.Medical Technology :

In the medical field, Image Processing is employed for various tasks like PET scan, X-Ray Imaging, Medical CT, UV imaging, neoplastic cell Image processing, and far more. The introduction of Image Processing to the medical technology field has greatly improved the diagnostics process

The image on the left is that the original image. The image on the correct is that the processed image. we will see that the processed image is much better and may be used for better diagnostics.

 2.Computer / Machine Vision :

One of the foremost interesting and useful applications of Image Processing is in Computer Vision. Computer Vision is employed to form the pc see, identify things, and process the entire environment as an entire. a vital use of Computer Vision is Self Driving cars, Drones etc. CV helps in obstacle detection, path recognition, and understanding the environment.

This is how typical Computer Vision works for Car Autopilots. the pc takes in live footage and analyses other cars, the road, and other obstacles.

3.Pattern recognition:

Pattern recognition may be a a part of Image Processing that involves AI and Machine Learning. Image processing is employed to search out out various patterns and aspects in images. Pattern Recognition is employed for Handwriting analysis, Image recognition, Computer-aided diagnosing, and far more.

 Conclusion :   

Machine Learning algorithms allow you to do image processing at scale, and with great detail. I hope you learned a thing or two about how Image Processing can be used in Machine Learning.