A Gentle Introduction to Computer Vision

Last Updated on July 5, 2019

Computer Vision, often abbreviated as CV, is defined as a field of study that seeks to develop techniques to help computers “see” and understand the content of digital images such as photographs and videos.

The problem of computer vision appears simple because it is trivially solved by people, even very young children. Nevertheless, it largely remains an unsolved problem based both on the limited understanding of biological vision and because of the complexity of vision perception in a dynamic and nearly infinitely varying physical world.

In this post, you will discover a gentle introduction to the field of computer vision.

After reading this post, you will know:

  • The goal of the field of computer vision and its distinctness from image processing.
  • What makes the problem of computer vision challenging.
  • Typical problems or tasks pursued in computer vision.

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A Gentle Introduction to Computer Vision

A Gentle Introduction to Computer Vision
Photo by Axel Kristinsson, some rights reserved.


This tutorial is divided into four parts; they are:

  1. Desire for Computers to See
  2. What Is Computer Vision
  3. Challenge of Computer Vision
  4. Tasks in Computer Vision

Desire for Computers to See

We are awash in images.

Smartphones have cameras, and taking a photo or video and sharing it has never been easier, resulting in the incredible growth of modern social networks like Instagram.

YouTube might be the second largest search engine and hundreds of hours of video are uploaded every minute and billions of videos are watched every day.

The internet is comprised of text and images. It is relatively straightforward to index and search text, but in order to index and search images, algorithms need to know what the images contain. For the longest time, the content of images and video has remained opaque, best described using the meta descriptions provided by the person that uploaded them.

To get the most out of image data, we need computers to “see” an image and understand the content.

This is a trivial problem for a human, even young children.

  • A person can describe the content of a photograph they have seen once.
  • A person can summarize a video that they have only seen once.
  • A person can recognize a face that they have only seen once before.

We require at least the same capabilities from computers in order to unlock our images and videos.

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What Is Computer Vision?

Computer vision is a field of study focused on the problem of helping computers to see.

At an abstract level, the goal of computer vision problems is to use the observed image data to infer something about the world.

— Page 83, Computer Vision: Models, Learning, and Inference, 2012.

It is a multidisciplinary field that could broadly be called a subfield of artificial intelligence and machine learning, which may involve the use of specialized methods and make use of general learning algorithms.

Overview of the Relationship of Artificial Intelligence and Computer Vision

Overview of the Relationship of Artificial Intelligence and Computer Vision

As a multidisciplinary area of study, it can look messy, with techniques borrowed and reused from a range of disparate engineering and computer science fields.

One particular problem in vision may be easily addressed with a hand-crafted statistical method, whereas another may require a large and complex ensemble of generalized machine learning algorithms.

Computer vision as a field is an intellectual frontier. Like any frontier, it is exciting and disorganized, and there is often no reliable authority to appeal to. Many useful ideas have no theoretical grounding, and some theories are useless in practice; developed areas are widely scattered, and often one looks completely inaccessible from the other.

— Page xvii, Computer Vision: A Modern Approach, 2002.

The goal of computer vision is to understand the content of digital images. Typically, this involves developing methods that attempt to reproduce the capability of human vision.

Understanding the content of digital images may involve extracting a description from the image, which may be an object, a text description, a three-dimensional model, and so on.

Computer vision is the automated extraction of information from images. Information can mean anything from 3D models, camera position, object detection and recognition to grouping and searching image content.

— Page ix, Programming Computer Vision with Python, 2012.

Computer Vision and Image Processing

Computer vision is distinct from image processing.

Image processing is the process of creating a new image from an existing image, typically simplifying or enhancing the content in some way. It is a type of digital signal processing and is not concerned with understanding the content of an image.

A given computer vision system may require image processing to be applied to raw input, e.g. pre-processing images.

Examples of image processing include:

  • Normalizing photometric properties of the image, such as brightness or color.
  • Cropping the bounds of the image, such as centering an object in a photograph.
  • Removing digital noise from an image, such as digital artifacts from low light levels.

Challenge of Computer Vision

Helping computers to see turns out to be very hard.

The goal of computer vision is to extract useful information from images. This has proved a surprisingly challenging task; it has occupied thousands of intelligent and creative minds over the last four decades, and despite this we are still far from being able to build a general-purpose “seeing machine.”

— Page 16, Computer Vision: Models, Learning, and Inference, 2012.

Computer vision seems easy, perhaps because it is so effortless for humans.

Initially, it was believed to be a trivially simple problem that could be solved by a student connecting a camera to a computer. After decades of research, “computer vision” remains unsolved, at least in terms of meeting the capabilities of human vision.

Making a computer see was something that leading experts in the field of Artificial Intelligence thought to be at the level of difficulty of a summer student’s project back in the sixties. Forty years later the task is still unsolved and seems formidable.

— Page xi, Multiple View Geometry in Computer Vision, 2004.

One reason is that we don’t have a strong grasp of how human vision works.

Studying biological vision requires an understanding of the perception organs like the eyes, as well as the interpretation of the perception within the brain. Much progress has been made, both in charting the process and in terms of discovering the tricks and shortcuts used by the system, although like any study that involves the brain, there is a long way to go.

Perceptual psychologists have spent decades trying to understand how the visual system works and, even though they can devise optical illusions to tease apart some of its principles, a complete solution to this puzzle remains elusive

— Page 3, Computer Vision: Algorithms and Applications, 2010.

Another reason why it is such a challenging problem is because of the complexity inherent in the visual world.

A given object may be seen from any orientation, in any lighting conditions, with any type of occlusion from other objects, and so on. A true vision system must be able to “see” in any of an infinite number of scenes and still extract something meaningful.

Computers work well for tightly constrained problems, not open unbounded problems like visual perception.

Tasks in Computer Vision

Nevertheless, there has been progress in the field, especially in recent years with commodity systems for optical character recognition and face detection in cameras and smartphones.

Computer vision is at an extraordinary point in its development. The subject itself has been around since the 1960s, but only recently has it been possible to build useful computer systems using ideas from computer vision.

— Page xviii, Computer Vision: A Modern Approach, 2002.

The 2010 textbook on computer vision titled “Computer Vision: Algorithms and Applications” provides a list of some high-level problems where we have seen success with computer vision.

  • Optical character recognition (OCR)
  • Machine inspection
  • Retail (e.g. automated checkouts)
  • 3D model building (photogrammetry)
  • Medical imaging
  • Automotive safety
  • Match move (e.g. merging CGI with live actors in movies)
  • Motion capture (mocap)
  • Surveillance
  • Fingerprint recognition and biometrics

It is a broad area of study with many specialized tasks and techniques, as well as specializations to target application domains.

Computer vision has a wide variety of applications, both old (e.g., mobile robot navigation, industrial inspection, and military intelligence) and new (e.g., human computer interaction, image retrieval in digital libraries, medical image analysis, and the realistic rendering of synthetic scenes in computer graphics).

— Page xvii, Computer Vision: A Modern Approach, 2002.

It may be helpful to zoom in on some of the more simpler computer vision tasks that you are likely to encounter or be interested in solving given the vast number of publicly available digital photographs and videos available.

Many popular computer vision applications involve trying to recognize things in photographs; for example:

  • Object Classification: What broad category of object is in this photograph?
  • Object Identification: Which type of a given object is in this photograph?
  • Object Verification: Is the object in the photograph?
  • Object Detection: Where are the objects in the photograph?
  • Object Landmark Detection: What are the key points for the object in the photograph?
  • Object Segmentation: What pixels belong to the object in the image?
  • Object Recognition: What objects are in this photograph and where are they?

Other common examples are related to information retrieval; for example: finding images like an image or images that contain an object.

Further Reading

This section provides more resources on the topic if you are looking to go deeper.




In this post, you discovered a gentle introduction to the field of computer vision.

Specifically, you learned:

  • The goal of the field of computer vision and its distinctness from image processing.
  • What makes the problem of computer vision challenging.
  • Typical problems or tasks pursued in computer vision.

Do you have any questions?
Ask your questions in the comments below and I will do my best to answer.

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42 Responses to A Gentle Introduction to Computer Vision

  1. Avatar
    Bashir Ghariba March 22, 2019 at 5:31 am #

    Good work.

    • Avatar
      Jason Brownlee March 22, 2019 at 8:41 am #


      • Avatar
        HASSAN October 21, 2019 at 11:26 pm #

        Great Job Sir,
        I have 2 Questions .
        1.Could you enlist some modern and professional tools (industrial point of view) for CV,
        e.g Python with Matlav or Anaconda.
        What will you recommend ?
        2. Name and minimum number of Tools at least one should must get command over them to be a professional from student level .
        (maybe you can merge both questions)

  2. Avatar
    adane gebru fkadu March 22, 2019 at 7:37 pm #

    hi sir,
    it is all good that you are blogging here for me and thanks for all. but i have a question if you would answer that is in deep learning for image classification what are the best feature extractors better than CNN and the best classifiers after extracting the features to fed in to?


    • Avatar
      Jason Brownlee March 23, 2019 at 9:21 am #

      A high-performing pre-trained model fit on a subset of the imagenet dataset can be used as a very effective feature extraction model.

      Examples include VGG, Inception and Resnet.

      The features can be fed into a neural net or another algorithm, such as an SVM.

  3. Avatar
    Abkul March 26, 2019 at 4:00 am #

    Great work.

    When do we expect the book to be published? .

  4. Avatar
    Richard March 27, 2019 at 12:11 am #

    Good article, nice balance. Not too technical and not too fluffy!

    Well done!

  5. Avatar
    Shahbaz June 5, 2019 at 10:27 pm #

    Great work , u r great teacher.
    i m confusing computer vision and image processing as u mention above, detection of object computer vison from existing image, and create new image from existing is image procssing, so after u example the computer vision is OCR, ANPR(automatc number plate detection) is also get info from existing image , but we use image prosecng in this
    plz u could explain if u understand my question?

    (sorry for my english , its not my native)

    • Avatar
      Jason Brownlee June 6, 2019 at 6:30 am #

      Yes, often a computer vision project will include aspects of image processing.

  6. Avatar
    Gowtham August 17, 2019 at 11:04 pm #

    Good article, nice work.

  7. Avatar
    Keena Byrd PhD August 21, 2019 at 7:00 am #

    Finally! Thank you for your clear explanations…they will also help me when explaining to others.

  8. Avatar
    rj stefka August 27, 2019 at 1:54 am #

    you so gucci fam lit dude

  9. Avatar
    Rakesh November 14, 2019 at 4:32 pm #

    Nice post good information provided for Machine Learning

  10. Avatar
    Kayden January 16, 2020 at 5:59 am #

    Can anyone tell me about its data?

  11. Avatar
    RIPLEY LER February 5, 2020 at 3:21 am #


    What about 3D modelling? Or perhaps building a 3D model using stereo cameras or photogrammetry? would that be considered at computer vision as well?

  12. Avatar
    Joanne Greer February 6, 2020 at 5:29 am #

    read this to understand a course my granddaughter is taking. It was clear and helpful – I do have a math background.

  13. Avatar
    Ahmed March 15, 2020 at 2:30 am #

    Hello, Prof
    thanks for for this great article could you help me how to recognize sign languages

  14. Avatar
    Ovindi Bandara April 10, 2020 at 5:04 pm #

    Hello Jason,

    Is there any of your articles posted on “machine learning for medical imaging”?

  15. Avatar
    Muhammad Waqas September 14, 2020 at 3:04 am #

    sir I have a question

    Are feature selection and extraction methods in machine vision applicable to all images? Or will the algorithm change be depending on the image?

    • Avatar
      Jason Brownlee September 14, 2020 at 6:53 am #

      Feature selection and extraction is learned by the CNN model and performed on all images automatically by the model.

  16. Avatar
    M. Yahya December 6, 2020 at 5:12 am #

    Well explained article but I have question is moral of cv Is that it can help to knew properties of image and objects in image but can’t extract real means of image??
    Am I right??

    • Avatar
      Jason Brownlee December 6, 2020 at 7:11 am #

      No, we solve specific engineering problems using the tools of computer vision.

  17. Avatar
    Avinash Singh March 15, 2021 at 8:13 pm #

    Amazing article.

  18. Avatar
    Shobi May 31, 2021 at 2:33 am #

    Hi Jason,

    Thank you so much for your nice article. I have a basic question. Could you please help me to understand the following question?

    Could we say an image a natural image collected from internet sources flickers, google , and many other source.

    Looking forward to your kind response.

    Many Thanks!

    • Avatar
      Jason Brownlee May 31, 2021 at 5:51 am #

      Sorry, I don’t understand. Perhaps you could rephrase your question or elaborate.

  19. Avatar
    Shobi May 31, 2021 at 6:22 am #

    Hi Jason,

    Thank you so much for your quick feedback!

    I have dataset “MIT-Indoor-67” which contains images collected from internet source such as flicker, google, and labelMe. Could I call the images of this dataset “natural images” in the context of deep learning / image preprocessing? I am confused about natural images vs real images concepts in context of deep learning.

    Many Thanks!

    • Avatar
      Jason Brownlee June 1, 2021 at 5:26 am #

      Sorry, I have not heard these terms before, I don’t know the differnce.

  20. Avatar
    Martin January 21, 2022 at 11:59 pm #

    Its not true that CV is a subfield of AI. CV and AI are distinct fields and have, in its core, nothing to do with each other. Beside object detection, recoginition and so on, CV also deals with camera calibration, computational imaging, stereoscopic depth estimation and much more. But its true that a lot of classical CV algorithms have been superseded by ML algorithms.

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