Jim Carnicelli's AI Blog

[ Audio Version ] Following is another in my series of ad hoc journal entries I've been keeping of my thoughts on machine vision. I've been nursing the idea that it's not necessary to have a detailed sense of how far away things in an image are. It's probably sufficient, in some basic contexts, to just know that one thing is in front of another and not care about absolute distances. It seems some MV researchers have gone ape over telling exactly how far away an apple on a table is using lasers, stereo displacement, and all sorts of tricks. Maybe just knowing how big an apple typically is is good enough for telling how far away it is. When I think about 3D vision in this context, I have been likening the visible world to a collage of 2D images. Take the scene seen by a stationary camera looking at a road as cars go by. One could take the unchanging background as one image. A car moving by would be the only object of interest. What's interesting is that the image of t...

[ Audio Version ] Following is another in my series of ad hoc journal entries I've been keeping of my thoughts on machine vision. One thing that most of us in MV don't want to admit is that we arbitrarily set thresholds for distinguishing where one thing ends and another begins. I don't think we work that way, per se. I'd like to see edge- or color-blob-finding techniques having varying thresholds. One use would be in finding large regions with high thresholds, then using ever narrower thresholds to find the sub-regions within the broader ones. In a similar vain, I'm considering using low-res images to find homogeneous-color blobs in image Rich textures can disappear when the resolution is low, leaving just the overall color. A field of grass, for example, becomes a solid sheet of green. Once the field is isolated, it can be scrutinized in finer detail to see if there's something small that's of interest in it.

[ Audio Version ] Following is another in my series of ad hoc journal entries I've been keeping of my thoughts on machine vision. One thing that seems to dog many MV techniques is how slow or otherwise resource-hungry they are. I'm realizing that one thing that seems a must is a set of basic vision tools that allow for trading time for effectiveness. For example, given a whole image, the agent should be able to focus on a small portion - like your own fovea does - instead of trying to analyze the entire image. Also, the agent should be able to choose a lower quality image in order to reducing processing time. Ideally, an agent would be able to learn to estimate how much time each operation will take and to thus be able to choose which techniques to use and how intently to apply them based on how well they serve various goals. If, for example, the goal is to track the movement of one or more objects, a full-image, low-res approach might do. To study a stationary object in detail...

[ Audio Version ] Following is another in the aforementioned series of ad hoc journal entries I've been keeping of my thoughts on machine vision. Shadows and light splotches that fall on even perfectly smooth surfaces really trip up systems designed to detect objects by finding contiguous surfaces. We don't seem to be fooled by such issues very often. We are fooled when there are ambiguities in what we see. Perhaps understanding what makes one situation ambiguous versus another will help isolate what differentiates the two for the benefit of codification. Looking at a picture I took looking down a tree-lined sidewalk, I found a great example of the issue. Shadows of trees fall on the sidewalk, creating a fairly smooth, two-tone division between shadowed and non-shadowed portions. I see a continuous sidewalk.

[ Audio Version ] 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 gr...

[ Audio Version ] When I was in the store eyeing up On Intelligence , I also noticed an interesting looking book titled Visual Intelligence , by Donald D. Hoffman , that I was pretty sure I'd have to get back to. I finally bought it a few days ago. Owning to the circumstances of a bit of recent travel, I found I have had a bunch of time to read it. I'm a slow reader, but my inner geek found this book so gripping that I finished this roughly 200 page book off in two days. (I suppose if it had more words and fewer of the pretty pictures, it might have taken a few more days and been less gripping.) Given that I found Visual Intelligence a very cool book, I thought it worth writing a review. I'll begin by saying it's incredibly well written and that most of it should be easily reachable by the casual reader. It lends many cool insights into the curious nature of human vision and, by implication, all the other senses. My read of On Intelligence and its implications ...

[ Audio Version ] Poor researchers like me can't generally afford to put together sophisticated research projects. One of the interesting things about researching intelligence, though, is that we have at least one human research subject that's available for experiments 24 hours a day. If you'd like to learn more about the nature of learning in the human brain, there's a simple but interesting experiment you can run. Like most people, I'm right handed. For about a year, now, I've been trying to teach myself to brush my teeth with my left hand. I've gotten pretty good at it, but I'm still in awe at how bad my fine control skills with my left hand are compared to my right. Now, I'm sure people who understand handedness better than I will say my left hand will probably never be as dexterous as my right hand. And, sure enough, I don't work out my left hand as often as my right, so it'll probably never be as strong, which affects dexterity. S...