As I mentioned in the last post, I am at the initial stages of writing a book on elementary computer vision. It makes sense at this point to provide a rationale for such a book.

Current textbooks either have extensive prerequisites or take too long to get the student to use what’s been learned in real-life computer vision projects.

Let’s consider an example. Suppose we know freshman or sophomore students in a technical discipline. They have to take their first course in image processing. What are they capable of doing at the end of a typical course? They know about image representation and how to handle image files. They know how to increase contrast and remove noise. They are familiar with image restoration, image enhancement, and image compression. All good, but this choice of topics draws students toward photo editing and away from the scientific and industrial applications.

I am talking about the image processing vs. image analysis dilemma. The former produces images and the latter produces data. More on this here.

As image processing is a time consuming topic, the students may only get a little taste of image analysis (image segmentation and related topics about image content).

The result is that in order to make their skills applicable to scientific image analysis, they will need to take a more advanced course on the subject. Such a course would require (some combination of) calculus 1-3, linear algebra, probability. Even then, 3D images, especially their topology, are rarely addressed.

So, there may be a need for something even more elementary than Digital Image Processing Using MATLAB by Gonzalez, Woods, and Eddins discussed last time.