Jim Carnicelli's AI Blog

[ Audio Version ] As I've been dodging about among areas of machine vision, I've been searching for similarities among the possible techniques they could employ. I think I've started to see at least one important similarity. For lack of a better term, I'm calling it "patch equivalence", or "PE". The concept begins with a deceptively simple assertion about human perception: that there are neurons (or tight groups of them) that do nothing but compare two separate "patches" of input to see if they are the same. A "patch", generally, is just a tight region of neural tissue that brings input information from a region of the total input. With one eye, for example, a patch might represent a very small region of the total image that that eye sees. For hearing, a patch might be a fraction of a second of time spent listening to sounds within a somewhat narrow band of frequencies, as another example. A "scene", here, is a contiguou

[ Audio Version ] I've been experimenting, with limited success, with different ways of finding objects in images using what some vision researchers would call "preattentive" techniques, meaning not involving special knowledge of the nature of the objects to be seen. The work is frustrating in large part because of how confounding real-world images can be to simple analyses and because it's hard to nail down exactly what the goals for a preattentive-level vision system should be. In machine vision circles, this is generally called "segmentation", and usually refers more specifically to segmentation of regions of color, texture, or depth. Jeff Hawkins ( On Intelligence ) would say that there's a general-purpose "cortical algorithm" that starts out naive and simply learns to predict how pixel patterns will change from moment to moment. Appealingly simple as that sounds, I find it nearly impossible to square with all I've been learning about