My new post: Sparse Field Active Contours
implements quicker, more accurate active contours.
Today, I added demo code for the Hybrid Segmentation project. This segmentation algorithm (in the publications section) can be used to find the boundary of objects in images. This approach uses localized statistics and sometimes gets better results than classic methods. For an example, see the video below: The contour begins as a rectangle, but deforms over time so that it finally forms the outline of the monkey.
This can be used to segment many different classes of image. To try it out, download the demo below and run >>localized_seg_demo
This code is based on a standard level set segmentation; it just optimizes a different energy. I’ve also made a demo which implements the well-known Chan-Vese segmentation algorithm. This technique is similar to the one above, but it looks at global statistics. This makes it more robust to initialization, but it also means that more constraints are placed on the image. Download it and see what you think! Again, unzip the file and run >>region_seg_demo
This code can be used according to the MIT license. As long as this work is appropriately cited and attributed, and not being used for proprietary or commercial purposes, I’m fully supportive of you using it. Please drop me a line if it helps you!