Computer Vision is a field that deals with the creation of intelligent systems or computers to understand the data from digital images or videos. It's primary goal is to providing a human like vision to the systems and makes them smarter to take decisions on its own based on the input data that comes from single or different types of cameras.
As a Computer Vision enthusiast, I am writing this blog to provide the key aspects of Computer Vision in today's world.
In this digital era, the emerging AI technology is becoming particularly ubiquitous by making computers to think and act like humans. Researchers and enthusiasts alike, work on numerous aspects of the field to achieve incredible results. One of many such areas is the domain of Computer Vision, which enables the artificial vision to machines to make them more robust in perceiving surroundings and to make intelligent decisions based on data.
Nowadays, most of us experience computer vision applications without knowing it, just like face unlocking in our mobiles. The outstanding results of CNN and the existence of high computational power ignited the Automotive companies toward fully autonomous vehicles.
For example, the recent DALLE 2.0 network has showcased new inventions in Computer Vision by making high-resolution images based on text captions.
The fast-growing Computer vision technology is mainly divided into traditional and Convolutional neural networks. Before explaining about the CV, it's very important to understand, what is an image and how is it formed ? everyone knows, that images will be generated with our cameras but as a CV researcher, the fundamental concepts in the image formation are very crucial. Actually, the image is a matrix with pixel intensity values which is a mixup of three colour spaces Red, Green, and Blue to form the desired colour as shown in the below figure.
The transparent and well-established traditional algorithms were widely used to perform small tasks on images using HOG (Histogram Oriented Gradient), SIFT (Scale-Invariant Feature Transform), and Edge detection algorithms. One of the state-of-the-art open-source library OpenCV has become more popular by providing the majority of the algorithms needed to start working with images like face detection, Blob detection and many more.
In today's world, The Convolutional Neural Network (CNN) plays a prominent role in the computer vision world by reaching human-level intelligence due to greater accuracy. The CNN replaced classic CV algorithms by performing automatic feature extraction on images using a small-sized kernel in a sliding window mechanism throughout the image.
The below methods can be used to identify the desired objects :
Object detection: It is the process of identifying and localizing objects by providing bounding boxes around the objects with class labels.
Segmentation: In this method, each pixel of the input image carries class information and which is widely used in Autonomous vehicles to perceive the surrounding objects with proper geometrical information.
Generative Adversarial Networks: In short GANs uses unsupervised learning algorithms and opened up many directions for research by generating synthetic instances of videos, images and voice data with high resolution. As the name suggests, it's a combination of two neural networks, competing with each other to generate new synthetic data based on input images.
There are several more research fields of Computer Vision for example Pose estimation, 3D Reconstruction, Style transfer and many others.
The majority of industrial automation relies on machine vision for robots to perform desired repetitive tasks for example quality inspection, Automated smart recycling etc. Most of the security surveillance applications have implemented Computer Vision algorithms to identify suspicious behaviour of people in public.
In conclusion, it is a basic overview of computer vision and in the next article, we will break down the complete mechanics of Convolution Neural Networks operation step by step by providing a strong visual hierarchy.
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