I have been working in and learning about both Computer vision and Image processing for a few years now, and I believe that I am not a complete beginner any more.

Still, after all these years, it is hard for me to tell for any particular part of my work whether it is mostly Computer vision related, or if it is Image processing. I just can't see the line -- when I work, study and research, I read reference materials with both keywords.

So, I am interested in the definition of the Computer vision and Image processing fields, with focus on the comparison (differences as well as overlaps) of the fields.

Additionally, I think it would be valuable to have an example of a (conceptual or existing) practical applications, projects and works dealing with/utilizing:

  • solely (or mostly) Computer vision tools and ideas
  • solely (or mostly) Image processing tools and ideas
  • a combination of tools and ideas from both fields

with special attention paid to what makes it one and not the other, or what makes it both.

I understand that these fields are highly related and that the "line" might not be as clear as this question is asking for, but I hope you understand that the point of this question was not to design a simple decision rule for classifying (my) work, but rather a better understanding of the focus and goals of these fields. Also, any additional information that seems on-topic with my question tone is welcome, even if I did not specifically ask for it.

  • $\begingroup$ What about "Machine Vision"? Do you think of it as a synonym for "Computer Vision"? $\endgroup$ Commented Feb 26, 2013 at 18:33
  • $\begingroup$ @uvts_cvs To be honest, I never taught of "Machine Vision" ... maybe that could have a meaning of "Machines (with embedded systems) specialized to preform some Computer Vision task", e.g. I read once about cameras capable of recognizing licence plates. But then again, I might be wrong about this one :) $\endgroup$
    – penelope
    Commented Feb 27, 2013 at 11:36

2 Answers 2


I believe Gonzalez and Woods's book - Digital Image Processing are competent enough to rely on their opinion:

There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer vision, start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images.We believe this to be a limiting and somewhat artificial boundary. For example, under this definition, even the trivial task of computing the average intensity of an image (which yields a single number) would not be considered an image processing operation. On the other hand, there are fields such as computer vision whose ultimate goal is to use computers to emulate human vision, including learning and being able to make inferences and take actions based on visual inputs. This area itself is a branch of artificial intelligence (AI) whose objective is to emulate human intelligence. The field of AI is in its earliest stages of infancy in terms of development, with progress having been much slower than originally anticipated. The area of image analysis (also called image understanding) is in between image processing and computer vision.

So I would say, that primary difference is in goals, not methods. For example, if the goal is to enhance image for later use by humans, then this may be called image processing. And if the goal is to emulate human vision (be it object recognition, defect detection or automatic driving), then it is closer to computer vision. Note, however, that emulating human vision by definition may also require image enhancement, so in most real cases computer vision relies on image processing.

Image understanding (feature extraction) may be equally used in both - pure image processing and computer vision.

  • $\begingroup$ Good point(s).. $\endgroup$
    – Spacey
    Commented Jun 22, 2012 at 15:50
  • $\begingroup$ very nice answer. just the perfect ratio of reference material and interpretation from experience. thanks $\endgroup$
    – penelope
    Commented Jun 22, 2012 at 20:03

The way I understand it, the objective of image processing is to get a (somehow transformed) image. The objective of computer vision is to find out something about the things in the image (like is the guy on the picture happy or sad, how many cars are there in the image and which way are they driving).

solely (or mostly) Computer vision tools and ideas

I don't think that's possible, not the way I understand the terms.

solely (or mostly) Image processing tools and ideas

Take for example, Adobe Photoshop: It can take an image and transform it into an image of a slimmer person with better skin. But it doesn't "know" anything about the objects depicted in the image.

  • $\begingroup$ Let me give you an example: I am currently working on Content based image retrieval. Most people are insisting that's image processing. I'm not sure it that fits to your answer (not saying it's a bad answer, just wondering) $\endgroup$
    – penelope
    Commented Jun 22, 2012 at 7:07
  • $\begingroup$ One indicator is: Is the result of your task an image (IP) or some other data structure (CV). In CBIR, the result is some other data structure (e.g. a similarity measure between images), so I'd say it's computer vision. Wikipedia says it's computer vision, too. $\endgroup$ Commented Jun 22, 2012 at 7:48
  • $\begingroup$ There, I've done some googling myself now, and I've found this overview article, and I quote: "In these systems, image processing algorithms (usually automatic) are used to extract feature vectors that represent image properties such as color, texture, and shape." Article looks pretty solid to me... $\endgroup$
    – penelope
    Commented Jun 22, 2012 at 8:33
  • 2
    $\begingroup$ Yes, every computer vision system uses image processing algorithms. $\endgroup$ Commented Jun 22, 2012 at 9:31
  • $\begingroup$ I don't disagree with any particular point in your answer and/or explanations... It's just not... fitting in as nicely as I would like I guess. But, this is turning in to an discussing, and that's counter-productive. So, I'm just hoping there will be other contributions to the question that will hopefully give a different/clearer perspective ;) $\endgroup$
    – penelope
    Commented Jun 22, 2012 at 9:57

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