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Computer Vision Introduction

Computer vision is a subfield of artificial intelligence that deals with acquiring, processing, analyzing, and making sense of visual data such as digital images and videos. It is one of the most compelling types of artificial intelligence that we regularly implement in our daily routines.

Computer vision helps to understand the complexity of the human vision system and trains computer systems to interpret and gain a high-level understanding of digital images or videos. In the early days, developing a machine system having human-like intelligence was just a dream, but with the advancement of artificial intelligence and machine learning, it also became possible. Similarly, such intelligent systems have been developed that can “see” and interpret the world around them, similar to human eyes. The fiction of yesterday has become the fact of today. In this tutorial, “Computer Vision Introduction”, we will discuss a few important concepts of computer vision, such as:

• What is Computer Vision?
• How does Computer Vision Work?
• The evolution of computer vision
• Applications of computer vision
• Challenges of computer vision

What is Computer Vision?

Computer vision is one of the most important fields of artificial intelligence (AI) and computer science engineering that makes computer systems capable of extracting meaningful information from visual data like videos and images. Further, it also helps to take appropriate actions and make recommendations based on the extracted information.

Further, Artificial intelligence is the branch of computer science that primarily deals with creating a smart and intelligent system that can behave and think like the human brain. So, we can say if artificial intelligence enables computer systems to think intelligently, computer vision makes them capable of seeing, analyzing, and understanding.

History of Computer Vision

Computer vision is not a new technology because scientists and experts have been trying to develop machines that can see and understand visual data for almost six decades. The evolution of computer vision is classified as follows:

• 1959: The first experiment with computer vision was initiated in 1959, where they showed a cat as an array of images. Initially, they found that the system reacts first to hard edges or lines, and scientifically, this means that image processing begins with simple shapes such as straight edges.
• 1960: In 1960, artificial intelligence was added as a field of academic study to solve human vision problems.
• 1963: This was another great achievement for scientists when they developed computers that could transform 2D images into 3-D images.
• 1974: This year, optical character recognition (OCR) and intelligent character recognition (ICR) technologies were successfully discovered. The OCR has solved the problem of recognizing text printed in any font or typeface, whereas ICR can decrypt handwritten text. These inventions are one of the greatest achievements in document and invoice processing, vehicle number plate recognition, mobile payments, machine translation, etc.
• 1982: In this year, the algorithm was developed to detect edges, corners, curves, and other shapes. Further, scientists also developed a network of cells that could recognize patterns.
• 2000: In this year, scientists worked on a study of object recognition.
• 2001: The first real-time face recognition application was developed.
• 2010: The ImageNet data set became available to use with millions of tagged images, which can be considered the foundation for recent Convolutional Neural Network (CNN) and deep learning models.
• 2012: CNN has been used as an image recognition technology with a reduced error rate.
• 2014: COCO has also been developed to offer a dataset for object detection and support future research.

How does Computer Vision Work?

Computer vision is a technique that extracts information from visual data, such as images and videos. Although computer vision works similarly to human eyes with brain work, this is probably one of the biggest open questions for IT professionals: How does the human brain operate and solve visual object recognition?

On a certain level, computer vision is all about pattern recognition which includes the training process of machine systems for understanding the visual data such as images and videos, etc.

Firstly, a vast amount of visual labeled data is provided to machines to train it. This labeled data enables the machine to analyze different patterns in all the data points and can relate to those labels. E.g., suppose we provide visual data of millions of dog images. In that case, the computer learns from this data, analyzes each photo, shape, the distance between each shape, color, etc., and hence identifies patterns similar to dogs and generates a model. As a result, this computer vision model can now accurately detect whether the image contains a dog or not for each input image.

Task Associated with Computer Vision

Although computer vision has been utilized in so many fields, there are a few common tasks for computer vision systems. These tasks are given below:

• Object classification: Object classification is a computer vision technique/task used to classify an image, such as whether an image contains a dog, a person’s face, or a banana. It analyzes the visual content (videos & images) and classifies the object into the defined category. It means that we can accurately predict the class of an object present in an image with image classification.
• Object Identification/detection: Object identification or detection uses image classification to identify and locate the objects in an image or video. With such detection and identification technique, the system can count objects in a given image or scene and determine their accurate location and labeling. For example, in a given image, one dog, one cat, and one duck can be easily detected and classified using the object detection technique.
• Object Verification: The system processes videos, finds the objects based on search criteria, and tracks their movement.
• Object Landmark Detection: The system defines the key points for the given object in the image data.
• Image Segmentation: Image segmentation not only detects the classes in an image as image classification; instead, it classifies each pixel of an image to specify what objects it has. It tries to determine the role of each pixel in the image.
• Object Recognition: In this, the system recognizes the object’s location with respect to the image.

How to learn computer Vision?

Although, computer vision requires all basic concepts of machine learning, deep learning, and artificial intelligence. But if you are eager to learn computer vision, then you must follow below things, which are as follows:

1. Build your foundation:
   • Before entering this field, you must have strong knowledge of advanced mathematical concepts such as Probability, statistics, linear algebra, calculus, etc.
   • The knowledge of programming languages like Python would be an extra advantage to getting started with this domain.
2. Digital Image Processing:
   It would be best if you understood image editing tools and their functions, such as histogram equalization, median filtering, etc. Further, you should also know about compressing images and videos using JPEG and MPEG files. Once you know the basics of image processing and restoration, you can kick-start your journey into this domain.
3. Machine learning understanding
   To enter this domain, you must deeply understand basic machine learning concepts such as CNN, neural networks, SVM, recurrent neural networks, generative adversarial neural networks, etc.
4. Basic computer vision: This is the step where you need to decrypt the mathematical models used in visual data formulation.

These are a few important prerequisites that are essentially required to start your career in computer vision technology. Once you are prepared with the above prerequisites, you can easily start learning and make a career in Computer vision.

Applications of computer vision

Computer vision is one of the most advanced innovations of artificial intelligence and machine learning. As per the increasing demand for AI and Machine Learning technologies, computer vision has also become a center of attraction among different sectors. It greatly impacts different industries, including retail, security, healthcare, automotive, agriculture, etc.

Below are some most popular applications of computer vision:

• Facial recognition: Computer vision has enabled machines to detect face images of people to verify their identity. Initially, the machines are given input data images in which computer vision algorithms detect facial features and compare them with databases of fake profiles. Popular social media platforms like Facebook also use facial recognition to detect and tag users. Further, various government spy agencies are employing this feature to identify criminals in video feeds.
• Healthcare and Medicine: Computer vision has played an important role in the healthcare and medicine industry. Traditional approaches for evaluating cancerous tumors are time-consuming and have less accurate predictions, whereas computer vision technology provides faster and more accurate chemotherapy response assessments; doctors can identify cancer patients who need faster surgery with life-saving precision.
• Self-driving vehicles: Computer vision technology has also contributed to its role in self-driving vehicles to make sense of their surroundings by capturing video from different angles around the car and then introducing it into the software. This helps to detect other cars and objects, read traffic signals, pedestrian paths, etc., and safely drive its passengers to their destination.
• Optical character recognition (OCR)
  Optical character recognition helps us extract printed or handwritten text from visual data such as images. Further, it also enables us to extract text from documents like invoices, bills, articles, etc.
• Machine inspection: Computer vision is vital in providing an image-based automatic inspection. It detects a machine’s defects, features, and functional flaws, determines inspection goals, chooses lighting and material-handling techniques, and other irregularities in manufactured products.
• Retail (e.g., automated checkouts): Computer vision is also being implemented in the retail industries to track products, shelves, wages, record product movements into the store, etc. This AI-based computer vision technique automatically charges the customer for the marked products upon checkout from the retail stores.
• 3D model building: 3D model building or 3D modeling is a technique to generate a 3D digital representation of any object or surface using the software. In this field also, computer vision plays its role in constructing 3D computer models from existing objects. Furthermore, 3D modeling has a variety of applications in various places, such as Robotics, Autonomous driving, 3D tracking, 3D scene reconstruction, and AR/VR.
• Medical imaging: Computer vision helps medical professionals make better decisions regarding treating patients by developing visualization of specific body parts such as organs and tissues. It helps them get more accurate diagnoses and a better patient care system. E.g., Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) scanner to diagnose pathologies or guide medical interventions such as surgical planning or for research purposes.
• Automotive safety: Computer vision has added an important safety feature in automotive industries. E.g., if a vehicle is taught to detect objects and dangers, it could prevent an accident and save thousands of lives and property.
• Surveillance: It is one of computer vision technology’s most important and beneficial use cases. Nowadays, CCTV cameras are almost fitted in every place, such as streets, roads, highways, shops, stores, etc., to spot various doubtful or criminal activities. It helps provide live footage of public places to identify suspicious behavior, identify dangerous objects, and prevent crimes by maintaining law and order.
• Fingerprint recognition and biometrics: Computer vision technology detects fingerprints and biometrics to validate a user’s identity. Biometrics deals with recognizing persons based on physiological characteristics, such as the face, fingerprint, vascular pattern, or iris, and behavioral traits, such as gait or speech. It combines Computer Vision with knowledge of human physiology and behavior.

How to become a computer vision engineer?

Computer vision is one of the world’s most popular & high-demand technologies. Although starting your career in this domain is not easy, if you have a good command of machine learning basics, advanced mathematics concepts, and the basics of computer vision, you can easily start your career as a computer vision engineer.

There are some roles and responsibilities required to become a computer vision engineer, which is as follows

• To create and implement a vision algorithm for working with image and video content pixels
• To develop a data-based approach for better problem solutions.
• Whenever required, you have to work on various AI and ML tasks required for computer vision, such as image processing.
• Experience in working on various real-time project scenarios for problem-solving.
• Hierarchical problem decomposition, implementation of solutions, and integration with other sub-systems.
• Hierarchical problem decomposition, implementation of solutions, and integration with other sub-systems.
• Should be capable of understanding business objectives and can connect to technical solutions through effective system design and architecture.

Job description (JD) for Computer vision engineer

• The candidate must have cumulative work experience in visual data processing and analysis using machine learning and deep learning.
• Hands-on experience with various AI/ML frameworks such as Python, TensorFlow, PyTorch, Keras, CPP, etc.
• Candidates must have good experience in implementing AI techniques.
• Must have good written and verbal communication skills.
• Candidates should be aware of object detection techniques and models such as YOLO, RCNN, etc.

Which programming language is best for computer vision?

Computer vision engineers require in-depth knowledge of machine learning and deep learning concepts with strong command over at least one programming language. There are so many programming languages that can be used in this domain, but Python is among the most popular. However, one can also choose OpenCV with Python, OpenCV with C++, or MATLAB to learn and implement computer vision applications.

OpenCV with Python could be the most preferred choice for beginners due to its flexibility, simple syntax, and versatility. Various reasons make Python the best programming language for computer vision, which is as follows:

• Easy-to-use: Python is very famous as it is easy to learn for entry-level persons and professionals. Further, Python is also easily adaptable and covers all business needs.
• Most used programming language: Python is one of the most popular programming languages as it contains complete learning environments to get started with machine learning, artificial intelligence, deep learning, and computer vision.
• Debugging and visualization: Python has an in-built facility for debugging via ‘PDB’ and visualization through Matplotlib.

Computer Vision Challenges

Computer vision has emerged as one of the most growing domains of artificial intelligence, but it still has a few challenges to becoming a leading technology. There are a few challenges observed while working with computer vision technology.

• Reasoning and analytical issuesAll programming languages and technologies require the basic logic behind any task. To become a computer vision expert, you must have strong reasoning and analytical skills. If you don’t have such skills, then defining any attribute in visual content may be a big problem.
• Privacy and security: Privacy and security are among the most important factors for any country. Similarly, vision-powered surveillance is also having various serious privacy issues for lots of countries. It restricts users from accessing unauthorized content. Further, various countries also avoid such face recognition and detection techniques for privacy and security reasons.
• Duplicate and false content: Cyber security is always a big concern for all organizations, and they always try to protect their data from hackers and cyber fraud. A data breach can lead to serious problems, such as creating duplicate images and videos over the internet.

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