Shawn Lankton Online

Koch Industries is the second largest privately held company in the world. In “The Science of Success,” the C.E.O. talks about the management philosophies that his company uses to be so successful.

In short, the techniques called “Market-based Management” (MBM) described in the book are string of very sensible ideas. The true power of the book is that it shows how to join these sensible ideas together into a cohesive and effective management strategy. In this review, I’ll briefly describe the points that really hit home with me.    Click to continue →

Active contour methods for image segmentation allow a contour to deform iteratively to partition an image into regions. Active contours are often implemented with level sets. The primary drawback, however, is that they are slow to compute. This post presents a technical report describing, in detail, the sparse field method (SFM) proposed by Ross Whitaker [pdf], which allows one to implement level set active contours very efficiently. The algorithm is described in detail, specific notes are given about implementation, and source code is provided.

Fast Level Sets Demo

The links below point to the technical report and a demo written in C++/MEX that can be run directly in MATLAB. The demo implements the Chan-Vese segmentation energy, but many energies can be minimized using the provided framework.

Sparse Field Method – Technical Report [pdf]
Sparse Field Method – Matlab Demo [zip]

To run the MATLAB demo, simply unzip the file and run:
>>sfm_chanvese_demo
at the command line. On the first run, this will compile the MEX code on your machine and then run the demo. If the MEX compile fails, please check your MEX setup. The demo is for a 2D image, but the codes work for 3D images as well.

My hope is that other researchers wishing to quickly implement Whitaker’s method can use this information to easily understand the intricacies of the algorithm which, in my opinion, were not presented clearly in Whitaker’s original paper. Personally, these codes have SUBSTANTIALLY sped up my segmentations, and are allowing me to make much faster progress towards completing my PhD!

Thanks to Ernst Schwartz and Andy for helping to find small bugs in the codes and documentation. (they’re fixed now!)

For more information regarding active contour, segmentation, and computer vision, check here: Computer Vision Posts

In LaTeX, the hyperref package can be challenging to work with. It is well known that the hyperref package must be loaded last, BUT when using the algorithm package as well things get tricky. algorithm must be loaded after hyperref BUT, doing the following error if figures are used at the same time with pdfTeX:

ERROR: pdfTeX warning (ext4): destination with the same identifier (name{figure.1})

The solution is to load the float package (which is normally loaded by other more high-level packages) before hyperref like so:

\usepackage{amsmath,amsymb,graphicx,subfigure,etc.,etc.}
\usepackage{float}
\usepackage{hyperref}
\usepackage{algorithm}


The other solution is to not use pdfLaTeX, and use regular LaTeX instead.