Updated: 2011-12-28 00:00:45
Blog reader Mike posed the following question recently: If you have a bunch of point locations (for example, object centroids), how you make a binary image containing just those points? For example, consider this image:bw = imread('text.png'); imshow(bw, 'InitialMagnification', 200) How can we make an image like this, where the dots are located at the [...]
Updated: 2011-12-23 00:00:31
Blog reader Asadullah posted the following question last week on my old post about batch processing: I am trying to process some images by following the MATLAB demo. After getting the names of files when I try to see any of the files then it gives the error. The detail is as follows: >> fileFolder [...]
Updated: 2011-12-21 13:20:18
For the past several weeks I've been writing about shortest-path problems in image processing: finding the shortest path between two points in an image, with and without constraints. Applications included the practical (path finding in a skeleton image) and fun (maze solving). Along the way, I've described: the basic idea of finding shortest paths by [...]
Updated: 2011-12-13 07:00:45
In this post in the Exploring shortest paths series, I make things a little more complicated (and interesting) by adding constraints to the shortest path problem. Last time, I showed this example of finding the shortest paths between the "T" and the sideways "s" in this image: url = 'http://blogs.mathworks.com/images/steve/2011/two-letters-cropped.png'; bw = imread(url);And this was [...]
Updated: 2011-12-06 07:00:11
In my previous posts on Exploring shortest paths (November 1, November 26, and December 3), I have noted several times that there isn't a unique shortest path (in general) between one object and another in a binary image. To illustrate, here's a recap of the 'quasi-euclidean' example from last time. bw = logical([ ... 0 [...]
Updated: 2011-12-02 18:00:55
In part 2 of Exploring shortest paths, I noted a problem with using the 'quasi-euclidean' distance transform to find the shortest paths between two objects in a binary image. Specifically, our algorithm resulted in a big, unfilled gap between the two objects. bw = logical([ ... 0 0 0 0 0 0 0 0 1 [...]
