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Introduction to Computer Vision: How Machines See the World

Couresesteach — Sat, 01 Nov 2025 08:14:43 +0000

📑 Table of Contents

Difference between CV and CP
What is Computer Vision?
What is Computer Vision NOT?
How does Computer Vision work?
Real life Example?
History of Computer Vision

1- Introduction

This is a course on computer vision. It's aimed at covering the foundational aspects of how to analyze images and to extract content from images. That is, how can we build a computer or a machine that can see and interpret an image. First what do I mean by foundational? I mean that we are going to cover the mathematical and computational methods to provide you with core concepts of how can a computer be built to interpret images. Notice I am using the word interpret. In Computer Vision we are interested in extracting information, knowledge from an image. Many want to go beyond processing an image to really knowing what is inside the image, what's the content of the image. So we will learn the math and the basic concepts how to compute with an image and extract information from it.

Difference between CV and CP

What is the difference between these two classes and the material covered in it? There is indeed some overlap between the classes, especially in the initial few modules where we learn about computing with images and extracting information from images.

Computational photography is really about capturing a light from a scene to record a scene into a photograph or such other related novel artifact that showcases the scene. Image analysis is done to support the capture and display of the scene in novel ways. Some of it's actually about building newer forms of cameras and softwares to facilitate that process. Computer vision is really about interpreting an analysis of the scene. That is what is the content of the image of the scene, who is in there, what is in the image and what is happening.

2- What is Computer Vision

Definition: Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they “see.”

Defination1: “Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do”

Definition 2 :“Computer Vision is just a field of AI that enables computers or machines to see and understand the world and the things in it”

Definition 3-GPT: Computer vision is a field of study in computer science and artificial intelligence that focuses on enabling computers to interpret and understand visual data from the world around us. It involves developing algorithms and techniques that allow computers to analyze and make sense of images and videos, just like humans do.

Computer Vision is really about analyzing images and videos to extract knowledge from them. Mostly these images are of real scenes like that of a street image with cars and such where autonomous vehicle they'll have to navigate through or it could be other types of images like that of an X-ray inside a human head and we need to do image analysis to be able to extract things about of interest in medical applications.

Computer vision is the field of computer science that focuses on creating digital systems that can process, analyze, and make sense of visual data (images, videos) in the same way that humans do. Computer Vision uses convolutional neural networks to process visual data at the pixel level and deep learning recurrent neural networks to understand how one pixel relates to another [2] So essentially the goal is image and video understanding which means labeling interesting things in an image and also tracking them as they move.

Well, there's a couple of ways of thinking about it. I like this slide that I borrowed from Steve Seitz, where he talks about every picture tells a story. And one way of thinking about computer vision is the goal is to interpret images. That is, say something about what's present in the scene or what's actually going on. So, what we're doing is, we're going to take images in, and what's going to come out is something that has some meaning to it. That is, we're going to extract, we're going to create some sort of interpretations, some sort of an understanding of what that image is representative of. This is different, many of you may have some exposure to image processing, which is the manipulation of images. That's images in and images out. And we'll talk a little bit about that because you use image processing for per, for computer vision. But fundamentally computer vision is about understanding something that's in the image.
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Why Study Computer Vision

Computer vision is an art with the help of which you are giving the computer to understand the visual world. There are many real-world life examples of computer vision that we use in our day-to-day life.
But there’s actually some really good reasons to do that. These days images and imagery have become ubiquitous in all of our technology. cameras, video, you can stream them, you can send them etc. So what's become fundamental to an awful lot of systems is the manipulation in the processing of imagery. And extracting information from that. There are domains such as surveillance. There are building 3D models for medical imaging. Or capturing for motion capture. These are all different current industries that leverage computer vision in a variety of ways. When you are clicking a selfie why is there a small square on your face, When you scan a document you get edges of the documents already detected, how do streamers change their backgrounds, and how did tesla create self-driving cars? It was all thanks to advancements in Computer Vision that it made all these things were possible [2].

But most of all, the reason to do it is, it is just a really cool and deep set of problems. And it's way more fun than learning how to build compilers. And now, I have to go apologize to all my compiler friends, but they know it's true.

History of computer vision

1950s,

Early experiments in computer vision took place in the 1950s, using some of the first neural networks to detect the edges of an object and to sort simple objects into categories like circles and squares [1].

1970s

In the 1970s, the first commercial use of computer vision interpreted typed or handwritten text using optical character recognition. This advancement was used to interpret written text for the blind.

1990s

As the internet matured in the 1990s, making large sets of images available online for analysis, facial recognition programs flourished. These growing data sets helped make it possible for machines to identify specific people in photos and videos.

3-How does computer vision work?

Computer vision technology tends to mimic the way the human brain works. But how does our brain solve visual object recognition? One of the popular hypothesis states that our brains rely on patterns to decode individual objects. This concept is used to create computer vision systems [5].Computer vision algorithms that we use today are based on pattern recognition. We train computers on a massive amount of visual data — computers process images, label objects on them, and find patterns in those objects. For example, if we send a million images of flowers, the computer will analyze them, identify patterns that are similar to all flowers and, at the end of this process, will create a model “flower.” As a result, the computer will be able to accurately detect whether a particular image is a flower every time we send them pictures.
Computer vision works in three basic steps:

1- Acquiring an image

Images, even large sets, can be acquired in real-time through video, photos or 3D technology for analysis.

2- Processing the image

Deep learning models automate much of this process, but the models are often trained by first being fed thousands of labeled or pre-identified images. Computer vision algorithms are based on pattern recognition. We train our model on a massive amount of visual(images) data. Our model processes the images with label and find patterns in those objects(images).

3- Understanding the image

The final step is the interpretative step, where an object is identified or classified.

Real life Example

For example, If we send a million pictures of vegetable images to a model to train, it will analyze them and create an Engine (Computer Vision Model) based on patterns that are similar to all vegetables. As a result, Our Model will be able to accurately detect whether a particular image is a Vegetables every time we send it .

References

1-What is Computer Vision? & Its Applications

2--Introduction of Computer Vision

4-How computer vision works

5-Computer Vision 🤖 Fundamentals with OpenCV

6-Computer Vision

7-Computer Vision Tutorial Series M1C1

0-Computer Vision SAS

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Understanding Artificial Intelligence: History, Definitions, and Modern Applications

Couresesteach — Fri, 31 Oct 2025 04:08:53 +0000

Artificial Intelligence (AI) has transformed from a visionary concept into an indispensable technology reshaping our world. From its philosophical roots and early computations to modern machine learning breakthroughs, AI now powers everyday tools—voice assistants, recommendation systems, and cutting-edge healthcare diagnostics. In this post, we’ll explore why humans created AI, trace its evolution from symbolic logic to neural networks, clarify what AI truly means today, and showcase real-world applications driving innovation across industries.

📚Chapter 1: Introduction to Machine Learning

If you want to read more articles about Machine Learning, don’t forget to stay tuned :) click here.

Section

Why we used AI
History of Artificial intelligence
Natural Intelligence (NI)
What is Artificial intelligence (AI)

Section 1 — Why we used AI

In traditional programming, when aiming to implement new functionalities or automate tasks, software development is typically required. This involves writing code containing a predetermined set of instructions, such as if-then-else statements, to direct the computer’s actions. Consequently, to accomplish a variety of tasks, a corresponding number of rules must be provided to the computer, posing a significant challenge. This limitation highlights that conventional programming approaches lack generalizability.

if you haven’t done it by yourself, requires laying out in excruciating detail every single step that you want the computer to do in order to achieve your goal. Now, if you want to do something that you don’t know how to do, then this is going to be a great challenge. Basically, regular programming is pretty limited and can’t make decisions on its own. That’s why we need generalized programming, which is more than just a programmer and can make decisions from our perspective.

https://youtu.be/A6e4JBoh6Ik?si=tTyH_oUvWf7nAXxE

So, basically, this Arthur Samuel had a challenge in 1956. He wanted to teach a computer to beat him at checkers. Like, how do you even do that? Well, he came up with a plan. He had the computer play against itself over and over again, like thousands of times, until it learned how to play checkers really well. And guess what? It actually worked! By 1962, the computer had even beaten the Connecticut state champion. Pretty impressive, right?

Section 2- History of Artificial intelligence

Even though artificial intelligence, or AI, has only been around for less than a hundred years, the idea of machines that can think goes way back. Even in ancient Greece, people were talking about intelligent robots and artificial beings. The whole idea of AI really starts with asking if machines can think like humans

1955

In 1955, Allen Newell and Herbert A. Simon made the first computer program meant to act like a smart thinker. They called it the “Logic Theorist.” This program tried to prove math ideas using logic symbols. It used a special way of searching for answers that imitated how humans solve problems. [7]. The Logic Theorist was like the first computer tool that could solve lots of different problems, not just one. It was a big deal in the world of smart computer programs.

Alan Turing, was an eminent mathematician, who is famous for breaking the Nazi Enigma code during World War II. This code gave the Allied Powers the edge they needed to win the war — it also laid the foundation for the creation of the computer.

1956

It was the year when the term “Artificial Intelligence” was first coined as an academic field by American computer scientist John McCarthy at the Dartmouth Conference. This conference was attended by some of the leading researchers in the field of AI, including Marvin Minsky, Claude Shannon, and Nathaniel Rochester.

At the conference, McCarthy gave a talk titled “The Limitations of and Prospects for Information Processing in Problem-Solving Machines.” In this talk, he proposed the creation of a new field of study called “Artificial Intelligence” that would focus on developing intelligent machines
Dartmouth scientist, John McCarthy, expanded on Turing’s ideas and coined the term “artificial intelligence” in 1955. McCarthy assembled a team of computer scientists and mathematicians to investigate if robots could learn the same way that children do, through trial and error, to build formal reasoning. The team hoped to ascertain how they could make machines “use language, form abstractions and concepts, solve [the] kinds of problems now reserved for humans, and improve themselves.”

Let me explain the history of AI. First, Alan Turing, a founding father of AI, came up with the question that “Can machines think like humans?”. Later, John McCarthy created the term “Artificial Intelligence” and invented the programming language LISP, played computer chess via the telegraph with opponents in Russia, and invented computer time-sharing. At that time, the computer was big enough to fill a room. But the concept of AI has created great hope and enthusiasm for the world of science and technology.

In recent years, AI has focused on tasks that only humans can do, such as image and voice recognition. Thus, the following problems that were previously unsolvable were now overcome with AI.

Image recognition
Object recognition
Language-to-language translations
Natural language comprehension
Image and speech recognition
Assistant assistants
Driverless cars

While AI was seen in the 60s and 70s as a computer skill that could play chess and checkers, perform simple calculations, and solve mathematical problems, in the 80s and 90s it was seen as a risk assessment and decision-making ability, and in the 2000s, with the development of the computational potential of computers, it was understood that learning systems could be possible

Thought: Facebook uses an artificial neural network for facial recognition
Speech and Hearing: Google Assistant, Siri
Vision: Capturing Traffic Violations

Section 3- Natural Intelligence (NI)

Def: NI is the intelligence naturally evolved in living beings in response to the level of complexity in problems, challenges etc

Def: Natural intelligence (NI) is the inherent ability of living organisms to adapt to and interact with their environment. It encompasses a wide range of cognitive capabilities, including learning, problem-solving, reasoning, and communication.

Helps in “ What to do, When we do not know”

Humans, animals, birds etc

Thought, Vision, Speech, Hear, Feel

Section 4- What is Artificial intelligence (AI)

There are many definitions and versions of Artificial Intelligence, WHICH IS GENUINE, TRUE AND REAL.

Def: Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

Def: AI is a technology that allows machines to simulate human behavior. It is a field of computer science that allows machines to execute complex tasks such as image recognition, decision making, and conversing [4].

Def: The term Artificial intelligence (AI) was first coined a decade ago in the year 1956 by John McCarthy at the Dartmouth conference. He defined “Artificial intelligence as the science and engineering of making intelligent machines”. In a sense, artificial intelligence is a technique of getting a machine to work and behave like humans. AI works best by combining a large number of data sets with fast, repetitive, and intelligent algorithms.

IBM

Def: Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

Oracle

Def: Artificial Intelligence refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect.

Accenture

Def: Artificial intelligence is a constellation of many different technologies working together to enable machines to sense, comprehend, act, and learn with human-like levels of intelligence

SAS

Def: Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks.
Encyclopedia Britannica

Def: Artificial Intelligence is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.

Stanford University

Def: Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs.

Amazon AWS
Def: Artificial Intelligence is the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition.

European Parliament

Def: AI is the ability of a machine to display human-like capabilities such as reasoning, learning, planning and creativity.

Qualcomm

Def :AI is an umbrella term representing a range of techniques that allow machines to mimic or exceed human intelligence.

Def: This allows AI software to automatically learn from patterns or symbols in those big data sets.

Def: AI is the simulation of human intelligence by machines. It enables the machine to think like a human.

Background: It is a program that can learn, reach and sense the same as humans do or Similar to the intelligence humans possess, artificial intelligence is the one donned by machines. The only difference remains the absence of emotionality and consciousness. The Capacity is given by humans to machines to memorize and learn from experience and to think and create, to speak, to judge, and make decisions. The brain is the most wonderful organ of the human body. The brain controls thought, memory, emotion, motor skills, vision, breathing, and touch. This complex structure of the brain became a source of inspiration for scientists and the concept of AI emerged. AI is the ability of a computer or robot to perform humanoid tasks .

Let’s connect: If you found this helpful, share your thoughts or questions in the comments! 😊

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Sources

[1] How different are Conventional Programming and Machine Learning?

[2] AI vs ML vs DL

[3] 6 Best Programming Languages for AI

[4] Artificial Intelligence Chapter 0: What It Is & Why You Should Care

[5] Machine Learning Algorithms — What, Why, and How?

[6]- Machine Learning — Andrew Ng

[7] History of AI: The Birth of Artificial Intelligence (1952–1956)

[8] From Silence to Syntax: How the Machine Learned Language