Recently, I've been spending a lot of my free time thinking about machine vision. I've been running a variety of simple experiments into different techniques and trying somehow to formulate a cohesive theory and tool set for creating a general purpose vision system. I feel bad that I haven't been blogging lately, though. I guess I've just assumed I need something significant to blog about so it's not a waste of people's time.
Ironically, I've been keeping a small, ad hoc journal of some ideas about the subject. I figured that perhaps it's worth sharing. The next new entries are simply extracts from it. They're far less formal than most of my already informal blog entries. I apologize for not putting them in sufficient context, which I usually try to do when I blog. So, without further ado, following is the first entry.
I keep trying to figure out a way to isolate regions. My bubble growth algorithm isn't all that bad, but not great. There's a nasty problem with spill-over where edges are poorly defined.
I'm reminded of my reading of Visual Intelligence. Hoffman addresses the concept of what I like to call "segmentation" of figures. A complex silhouette of a human, for example, might be segmented into a head, arms, legs, and a torso. The key to segmentation, in Hoffman's view, is finding the convex portions and starting cuts through the silhouette at them. To my thinking, the result tends to be smaller segments that generally don't have major concave corners or curves any more.
I've been trying to think of it from the other side, though. What if one took the bubble concept and added a certain "desire" of a bubble to keep from having small bulges? Perhaps just avoiding sharp concave corners would provide an interesting result. A bubble that begins growing in the center of the head in a silhouette might stop growing as it reaches the neck because further growth would create sharp concavities.
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