Some weeks ago I received an e-mail from a reader pointing to an interesting task in the field of image processing. He kindly let me to publish the problem and the images we were working on, so I decided to explain the problem and write a small guide showing how such a task can be solved.
Some weeks ago someone posted me a problem on segmenting regions of an image by using color information. She also attached a sample source code for doing this task. In this post this source code is analyzed and we also create a much better and general solution.
Image processing often requires transforming the intensity values for example to turn the image easier to process or to highlight certain objects. In this post an effective way of some simple transforms are explained and an interactive demonstration is also available.
The rand function in MATLAB returns uniformly distributed pseudorandom values, but we often need random numbers of other kind of distributions.
A great article written by John S. Denker explains a method of generating random numbers with arbitrary distribution. This post is based on his work, and shows a simple MATLAB implementation.
In a previous post the basics of the Hough transform were explained. Although we have the built-in hough function in MATLAB to do this operation, it is definitely worth to write our optimized version for study purposes. In addition this lets us to have a function meeting our needs better if needed.
In the last post a simple trick of creating two-dimensional histograms from integer coordinates was explained. As a small supplement, let us see, how to create histograms from any floating-point data. This method works for single vectors: in case of two-dimensional histograms we just have to apply the method for each individual dimension.