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As a sort of abstract question, do readers here think of <Class X> 101 as meaning fundamentals of X, or basic techniques in X? Having taken image processing fro

As a sort of abstract question, do readers here think of <Class X> 101 as meaning fundamentals of X, or basic techniques in X? Having taken image processing from both sides, I'd say that learning the principles was much more useful (and would have been more useful still if I'd had a proper background in linear algebra). This article is the equivalent of naming some tools and showing us where they fit.

echelon 11y ago
I've taken a course in image processing, and I think this presents a fantastic high-level introduction for those that have not been presented with the material before. There's a lot that's missing, but that is to be expected.
pen2l 11y ago
> I'd say that learning the principles was much more useful Well, of course one should learn both :) An interesting question to ask is: principles first or applications and techniques first? Maybe it depends on the individual. For me, certainly it has to be the application. Principles without application would not keep me engaged long enough... Ideally, it should be something like 30% high level principles with applications, and then the rest of the principles and fundamentals later on, which really elucidate how and why the technique works. That's my two cents anyway. :)
anjc 11y ago
I agree. I'm terrible at maths so struggled with aspects of Computer Vision in my degree, but I don't see how you could use OpenCV without understanding the principles to at least a basic degree. It seems like you're purposely creating a black box within which magic is happening. Which would be nice and fine and abstract in many circumstances, except OpenCV keeps you greatly abstracted from the concepts, and any non-trivial Vision application needs you to get close to the theoretical-metal anyway, in my experience. If anyone's interested in the theory, I'd recommend Sonka
- piratefsh 11y ago
  totally. while OpenCV is really useful and magical (i did learn a lot of theory from the OpenCV tutorials though), it didn't help me wrap my head around concepts, but implementing the algorithms (gaussian blur, edge detection, etc) without having fully grasped the math helped a whole lot with understanding how things work. i still can't explain the math behind edge detection, for example, but i can describe how it works. when i wrote this, i had in mind a person who would like to get a big picture idea of what image processing is, and will hopefully be inspired to learn what happens under the hood.

echelon 11y ago

I've taken a course in image processing, and I think this presents a fantastic high-level introduction for those that have not been presented with the material before. There's a lot that's missing, but that is to be expected.

pen2l 11y ago

> I'd say that learning the principles was much more useful Well, of course one should learn both :) An interesting question to ask is: principles first or applications and techniques first? Maybe it depends on the individual. For me, certainly it has to be the application. Principles without application would not keep me engaged long enough... Ideally, it should be something like 30% high level principles with applications, and then the rest of the principles and fundamentals later on, which really elucidate how and why the technique works. That's my two cents anyway. :)

anjc 11y ago

I agree. I'm terrible at maths so struggled with aspects of Computer Vision in my degree, but I don't see how you could use OpenCV without understanding the principles to at least a basic degree. It seems like you're purposely creating a black box within which magic is happening. Which would be nice and fine and abstract in many circumstances, except OpenCV keeps you greatly abstracted from the concepts, and any non-trivial Vision application needs you to get close to the theoretical-metal anyway, in my experience. If anyone's interested in the theory, I'd recommend Sonka

piratefsh 11y ago
totally. while OpenCV is really useful and magical (i did learn a lot of theory from the OpenCV tutorials though), it didn't help me wrap my head around concepts, but implementing the algorithms (gaussian blur, edge detection, etc) without having fully grasped the math helped a whole lot with understanding how things work. i still can't explain the math behind edge detection, for example, but i can describe how it works. when i wrote this, i had in mind a person who would like to get a big picture idea of what image processing is, and will hopefully be inspired to learn what happens under the hood.