A scene rendered by ZeroRay illustrating reflection and refraction. The glass orbs both reflect and transmit light through them.

ZeroRay began its life as a ray tracer. Building a ray tracer from scratch was one of the large projects for a graduate level computer science course I took called Image Computation. I ended up using ZeroRay in a lot of other projects and it has evolved into a ray tracer/computer vision (CV) toolkit with a suite of deep applications that are both interesting, and illustrate how to use ZeroRay.

I have released ZeroRay as open source software, under the BSD license. The BSD License is pretty much the most liberal open source license. The basic jist is that you can use the code for anything you want, be it open or closed source, commercial or non-commercial. All you have to do is give the author credit for using their library. You can visit the ZeroRay project page at sourceforge. At the time of this writing, I haven’t prepared any convenient downloadable packages so you will have to check the code out of the svn repository. To do so click Develop on the project page and follow the instructions there.

If you’re wondering, “What’s a Ray Tracer?” check out my previous article What is Ray Tracing?

Back to what exactly ZeroRay is. ZeroRay is a software library (or more precisely a set of software libraries). That means it is a set of tools that are used by programmers to create applications. A simple application could be one that, when executed, draws a pre-set picture and saves it to an image file. A more complex application could be a program that allows you to set up a 3D scene by dragging objects into it a low quality rendering of the scene and then invokes the ZeroRay ray tracer to generate a high quality version of the image. As a bit of an aside: ray tracing is a way to generate very high quality computer graphics, however it is significantly more computationally intensive than the techniques used in real-time renderers, like the sort that are used in video games. To make the ZeroRay ray tracer widely useful to computer artists (who generally are not programmers), it would need an application similar to the second example. But ZeroRay is more than a ray tracer. (more…)

An example of a modern, real-time, feature-based, structure from motion system is ProFORMA by Pan, Reitmayr and Drummond. To clear up the acronym, ProFORMA stands for Probabilistic Feature-based On-line Rapid Model Acquisition. Which is a mouthful. The video is impressive, though it is worth noting that the example model, with its simple geometry and texture rich surface, is ideal for the system [2].

Like most state-of-the-art structure from motion techniques, Pan, Reitmayr and Drummond’s approach is feature based [1]. Image features is an entire class of research in computer vision. The premise is that rather than operating on the entire image, in the form of raw pixels, it is smarter to pick “interesting” parts of the image and just consider the information around those spots. The process of finding interesting spots is called feature (or interest point) detection. There are many types of feature detectors (bearing all sorts of different names). Some look for bright or dark blobs, some look for points with a lot of local texture and some decide whether a point is interesting on some other criteria entirely. ProFORMA uses the FAST corner detector as their feature detector. (more…)

An example of a background/foreground segmentation from Janusz Konrad at Boston University.

After working for quite a while on the “motion detection” algorithms described in my last article, I was clued in to background subtraction. Or rather, it finally hit me why, when explaining what I was working on, people kept saying, “Oh, you’re doing background subtraction.”

Background subtraction is the keyword for a relatively well explored nook of computer vision. The motivation for background subtraction research is that, in an image, there is usually a part of the image that you care about (the foreground), and a part that you don’t care about (the background) and it would be nice to focus only on the parts that we care about. There are many justifiable ways to divide a single image into foreground and background sections if we use the criteria that the foreground is “things we care about” and the background is “things we don’t care about.” The colloquial distinction is that the foreground is usually closer to the camera, in focus, and more interesting than the background. The last is where subjectivity enters the equation. In the field of background subtraction and in the context of video, the consensus is that the background is the part of the image that does not belong to a sizable moving object. This is still a big vague but different algorithms have different ideas about what constitutes a background. (more…)