Showing posts from September, 2005

Some stereo vision illusions

[ Audio Version ] While engaging in some stereo vision experiments, I found myself a little stuck. I stopped working for a while and started staring at a wall on the opposite side of the room, pondering how my own eyes deal with depth perception. I crossed my eyes to study certain facets of my visual system. I got especially interested when I crossed my eyes so that the curtains on either side of the doorway were overlapped. I wasn't surprised to find my eyes were only too happy to lock the two together, given how similar they looked. I was, however, surprised to see how well my visual system fused various differences between the two images together into a single end product. It even became difficult to tell which component of the combined scene came from which eye without closing one eye. I thought it worthwhile to create some visual illusions based on some of these observations. To view them, you'll need to cross your eyes so that your right eye looks at the left image and vi

Topics in machine vision

[ Audio Version ] Once again, I've forgotten to announce a sub-site I created recently that I call Topics in Machine Vision (click here) , back on August 28th. Unlike my earlier Introduction to Machine Vision , it does not set out to give a broad overview of the subject matter. Instead, it's geared toward the researcher with at least some familiarity with the subject. Also, whereas I intended the introduction to stand complete on its own, Topics is more organic, meaning that I'll continue to add content to it as time passes. Knowing that this could get to be difficult to read and manage, I've broken down Topics into separate sections and pages. The first section I've fleshed out is on the Patch Equivalence concept I introduced in an earlier blog entry here. In fact, once I introduced this topic in detail, I went back and ran some experiments in application of the PE concept to stereo vision and published the results , including tons of example images that demonstrat