Today, when it comes to image data, ML algorithms can interpret images the same way our brains do. These are used almost everywhere, right from face recognition while capturing images on our smartphones, automating tedious manual work, self-driving cars, and everything in between.

With improvement in this area, machine learning algorithms can interpret images that our brains do. Concerning improvement in Image processing, we can see using image processing in many areas like face recognition while capturing images on our smartphones, automating tedious manual work, and self-driving cars.

There are two methods for image processing: analog and digital image processing. The analog image processing method is applied to hard copies like scanned photos and printouts, and the outputs here are usually images. In comparison, Digital image processing is used in manipulating digital images by using computers; the outputs here are usually information connected with that image, such as data on features, characteristics, bounding boxes, or masks.

These are many fields that image processing helps to have a better result such as:

• Self-Driving Technology: Assist in detecting objects and mimicking human visual cues & interactions.
• Medical Imaging: Help medical professionals interpret medical imaging and diagnose anomalies faster.
• Law Enforcement & Security: Aid in surveillance & biometric authentication.
• Gaming: Improving augmented reality and virtual reality gaming experiences.
• Image Restoration & Sharpening: Improve the quality of images or add popular filters etc.
• Pattern Recognition: Classify and recognize objects/patterns in images and understand contextual information. Image Retrieval: Recognize images for faster retrieval from large datasets.

Typically, machine learning algorithms have a specific pipeline or steps to learn from data. Let's take a generic example of the same and model a working algorithm for an Image Processing use case.

Firstly, ML algorithms need a considerable amount of high-quality data to learn and predict highly accurate results. Hence, we'll have to make sure the images are well processed, annotated, and generic for ML image processing. This is where Computer Vision (CV) comes into the picture; it's a field concerning machines being able to understand the image data. Using CV, we can process, load, transform and manipulate images for building an ideal dataset for the machine learning algorithm.

Frameworks and libraries for image processing

At present, there are more than 250 programming languages in existence, according to the TIOBE index. Out of these, Python is one of the most popular programming languages that's heavily used by developers/practitioners for Machine Learning. However, we can always switch to a language that suits the use case. Now, we'll look at some of the frameworks that we utilize for various applications.

OpenCV: OpenCV-Python is a library of Python bindings designed to solve computer vision problems. It’s simple and super easy to use.

Highlights:

• Huge library of image processing algorithms
• Open Source + Great Community
• Works on both images and videos
• Java API Extension
• Works with GPUs
• Cross-Platform

Tensorflow: Developed by Google, Tensorflow is one of the most popular end-to-end machine learning development frameworks.

Highlights:

• Wide range of ML, NN Algorithms
• Open Source + Great Community
• Work on multiple parallel processors
• GPU Configured
• Cross-Platform

PyTorch: PyTorch (by Facebook) is one of the most loved neural network frameworks for researchers. It’s more pythonic when compared with other ML libraries.

Highlights:

• Distribution Training
• Cloud Support
• Open Source + Great Community
• Works with GPUs
• Production Ready

Caffe: Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors.

Highlights:

• Open Source + Great Community
• C++ Based
• Expressive Architecture
• Easy and Faster Execution

EmguCV: Emgu CV is a cross-platform .Net wrapper to the OpenCV image processing library.

Highlights:

• Open Source and Cross-Platform
• Working with .NET compatible languages – C #, VB, VC ++, IronPython, etc.
• Compatible with Visual Studio, Xamarin Studio and Unity

MATLAB Image Processing Toolbox: Image Processing Toolbox apps let you automate common image processing workflows. You can interactively segment image data, compare image registration techniques, and batch-process large data sets.

Highlights:

• Wide range of Deep Learning Image Processing Techniques
• CUDA Enabled
• 3D Image Processing Workflows

WebGazer: WebGrazer is a JS-based library for eye tracking that uses standard webcams to infer the eye-gaze locations of web visitors on a page in real-time.

Highlights:

• Multiple gaze prediction models
• Continually supported and Open Source for 4+ years
• No special hardware; WebGazer.js uses your webcam

Apache Marvin-AI: Marvin-AI is an open-source AI platform that helps deliver complex solutions supported by a high-scale, low-latency, language-agnostic, and standardized architecture while simplifying exploitation and modeling.

Highlights:

• Open Source and Well documented
• Easy to use CLI
• Multi-threaded image processing
• Feature extraction from image components

MIScnn: An open-source deep-learning-based framework for Medical Image Segmentation.