The process of employing computers to understand and analyze images is known as computer vision (both photos and videos). While these algorithms have existed in various forms since the 1960s, recent advances in Machine Learning, as well as advances in data storage, computing capabilities, and affordable high-quality input devices, have resulted in significant improvements in how well our software can explore this type of content.

 

What is computer vision?

Computer vision is the process of understanding digital images and videos using computers. It aims to automate operations that can be accomplished with human eyesight. This includes techniques for capturing, processing, analyzing, and comprehending digital images, as well as data extraction from the actual world to generate information. It also features subdomains like object identification, video tracking, and motion estimation, making it useful in fields like medicine, navigation, and object modeling.

To put it another way, computer vision uses a device with a camera to take photographs or videos, then analyses them. Computer vision attempts to understand the information in digital photos and videos. Additionally, to solve a variety of problems, extract something helpful and relevant from these photographs and videos. How computer vision works

 

How does computer vision work?

 

 Computer vision is an interdisciplinary scientific discipline that studies how computers can understand the visual world as images and videos. It aims to duplicate and automate operations that the human visual system can perform from an engineering standpoint. Artificial Intelligence (AI) is used in computer vision to teach computers how to interpret and understand the visual environment. Machines can effectively recognize and classify objects using digital photos from cameras and movies, as well as deep learning models. For more than half a century, computer scientists have attempted to give computer vision, resulting in the field of computer vision. The goal is to enable computers to deduce a high-level understanding of the visual world from images. Images and videos in digital format If you've ever used a digital camera or smartphone, you already know that computers can capture images and videos with greater fidelity and better than humans.

Where is Computer Vision used?

 

1. Healthcare

In healthcare, computer vision is widely used. The analysis of pictures, scans, and photographs is important in medical diagnosis. Computer vision technologies promise not only to simplify but also to prevent incorrect diagnoses and lower treatment costs by analyzing ultrasound images, MRIs, and CT scans, which are all part of modern medicine's regular repertoire. Computer vision isn't meant to take the position of medical experts; rather, it's meant to make their jobs easier and to help them make better decisions. Image segmentation helps diagnosis by detecting key areas on 2D or 3D scans and colorizing them to make black-and-white images easier to examine.

 

The COVID-19 pandemic is using this technology. Doctors can use image segmentation to discover COVID-19 and analyze and quantify the infection and disease's progression. On CT scans of the lungs, the trained image recognition system detects suspicious spots. It assesses their size and volume in order to track the sickness of affected people. Computer vision not only makes it easier for doctors to diagnose and monitor a new disease during treatment, but it also creates vital data for researchers to study the disease and its progression. Researchers gain from the collected data and created images as well, as it allows them to devote more time to experiments and tests rather than data collecting. 

    

2. Automotive Industry

Self-driving cars are one of the artificial intelligence use cases that have gotten the most attention in recent years. Autonomous vehicles are equipped with powerful cameras that film their surroundings across a large area for this purpose. An image recognition algorithm monitors the resulting footage in real-time, which requires the algorithm's ability to search for and identify significant things not just in static photos but also in a continuous flow of images.

Top Tools used for Computer Vision

 

There are currently a number of online programs that give Computer Vision algorithms as well as a platform for executing or creating new ones. These tools also provide a platform for integrating computer vision with a variety of other software and technologies. So, let's have a look at some computer vision tools right now!

1. OpenCV

OpenCV (Open-Source Computer Vision Library) is an open-source computer vision library with a variety of computer vision and machine learning functionalities.

OpenCV, which was first released in 2000 by Intel, contains a number of computer vision algorithms that can perform a variety of tasks such as facial detection and recognition, object identification, monitoring moving objects, tracking camera movements, tracking eye movements, extracting 3D models of objects, creating an augmented reality overlay with scenery, recognizing similar images in an image database, and so on. OpenCV includes interfaces for C++, Python, Java, MATLAB, and other programming languages, and it runs on Windows, Android, Mac OS, Linux, and other platforms.

2. MATLAB

In 1984, MathWorks released MATLAB, a numerical computing environment. It includes the Computer Vision Toolbox, which comprises a number of computer vision algorithms and routines. Object detection, object tracking, feature detection, feature matching, 3-D camera calibration, 3D reconstruction, and so on are examples of these techniques. Machine learning methods like YOLO v2, ACF, Faster R-CNN, and others can be used to develop and train custom object detectors in MATLAB. These algorithms can also be executed on multicore CPUs and graphics processing units (GPUs) to make them significantly faster. Code generation in C and C++ is supported by MATLAB toolbox methods.

 3. GPU Image

GPU Image is a framework or rather, an iOS library that allows you to apply GPU-accelerated effects and filters to images, live-motion videos, and movies. It is built on OpenGL ES 2.0. Running custom filters on a GPU calls for a lot of code to set up and maintain. GPU Image cuts down on all of that boilerplate and gets the job done for you.

Computer Vision as a Service

1. Microsoft Azure

Microsoft API allows you to analyze images, read the text in them, and analyze video in near-real-time. You can also flag adult content, generate thumbnails of images and recognize handwriting.

2. Google Cloud and Mobile Vision APIs

 Google Cloud Vision API enables developers to perform image processing by encapsulating powerful machine learning models in a simple REST API that can be called in an application. Also, its Optical Character Recognition (OCR) functionality enables you to detect text in your images.The Mobile Vision API lets you get

References

https://home-webflow.glair.ai/post/the-uses-case-of-computer-vision

https://hub.packtpub.com/top-10-computer-vision-tools/

https://www.analyticsvidhya.com/blog/2021/06/everything-happening-in-computer-vision-that-you-should-know/

 https://fullscale.io/blog/machine-learning-computer-vision/

 Blog by,

Gajanan Jadhav