Here are some background and pointers. I'm not a computer vision guy, but these might get you started.
Computer vision is really hard. The computational resources we can spend on the problem are orders of magnitude smaller than what we're trying to emulate in the nature. I think (IANACVS!) something like 35% of the human brain's processing capacity deals with vision, for example. So the discipline is both highly mathematical (to root out shortcuts and shrink the problem) and computationally intense. Also, it's often got some kind of real-time requirements.
Don't let me discourage you though. Since I don't know much about the field, I don't know any of its successes. CV researchers have produced useful systems.
1. Image transform methods: looking up the 2D Fourier transform might be a good start.
2. Variational methods. These are hard to explain as they relate to vision, but if you read the first few chapters of Sussman & Wisdom's Structure & Interpretation of Classical Mechanics, you'll get an idea of what they're talking about.
3. Anything that involves sensors also involves noise. At my school, the first stage of the graduate CV track is a course based on [Papoulis & Pillai]'s Probability, Random Variables, and Stochastic Processes.
4. One of my classmates was involved in object modeling with manifolds. A manifold is basically an n-dimensional shape that follows mostly-Euclidean rules like a sphere, torus, or pretzel. I have no idea what to recommend here, because my curiosity about manifolds and repeated frustration in my efforts to find out more about them ultimately landed me in grad school. Watch out for that.
5. Obviously I know more about software than hardware, but you could definitely do worse than reading up on commonly-used sensors. A midlevel text on CCD cameras would be the way to go here.
6. You can read up on Visual Perception in humans. When I took a class on this as an undergrad, it was a senior-level psych elective. A related field is Psychophysics, which uses methods from physics to study sensory stimuli (e.g. acoustic waves) as they relate to perceived experience. See #s 1 and 3 above.
Computer vision is really hard. The computational resources we can spend on the problem are orders of magnitude smaller than what we're trying to emulate in the nature. I think (IANACVS!) something like 35% of the human brain's processing capacity deals with vision, for example. So the discipline is both highly mathematical (to root out shortcuts and shrink the problem) and computationally intense. Also, it's often got some kind of real-time requirements.
Don't let me discourage you though. Since I don't know much about the field, I don't know any of its successes. CV researchers have produced useful systems.
1. Image transform methods: looking up the 2D Fourier transform might be a good start.
2. Variational methods. These are hard to explain as they relate to vision, but if you read the first few chapters of Sussman & Wisdom's Structure & Interpretation of Classical Mechanics, you'll get an idea of what they're talking about.
3. Anything that involves sensors also involves noise. At my school, the first stage of the graduate CV track is a course based on [Papoulis & Pillai]'s Probability, Random Variables, and Stochastic Processes.
4. One of my classmates was involved in object modeling with manifolds. A manifold is basically an n-dimensional shape that follows mostly-Euclidean rules like a sphere, torus, or pretzel. I have no idea what to recommend here, because my curiosity about manifolds and repeated frustration in my efforts to find out more about them ultimately landed me in grad school. Watch out for that.
5. Obviously I know more about software than hardware, but you could definitely do worse than reading up on commonly-used sensors. A midlevel text on CCD cameras would be the way to go here.
6. You can read up on Visual Perception in humans. When I took a class on this as an undergrad, it was a senior-level psych elective. A related field is Psychophysics, which uses methods from physics to study sensory stimuli (e.g. acoustic waves) as they relate to perceived experience. See #s 1 and 3 above.
Hope some of this helps.