Ep 6: Machine Vision (Intro)


(Note for the reader: You may skip the first 3 paragraphs to jump right into the main part. Thank you!)


Pardon folks! Took a break to revise the previous stuff that I had covered, and most importantly... digging down the rabbit hole of stuff learnt in the past.

You see, Machine Vision is something I have been working on for the past 2-3 years, be it from my scholarship term at AWS (Udacity) or from free stuff, available online (So far, I haven't spent a penny on all the stuff I learnt! Self-bragging, but it's worth it at times. :)). 

However, I tried digging deeper and the topic of machine vision in robotics couldn't have come at a better point in my life, with me getting to learn more on how images actually work (represented digitally) inside a computer, and how as AI engineers we can process them for decision making in fields, like robotics...

Coordinate system in machine (computer) vision:-
Note: For an image, any point in 3D space is actually a pixel.

  • 3D coordinate (or 3-channel) system:
    Used to represent images in RGB form in a 3D space, with the 3 dimensions, where each of the dimensions represents the color channels of Red, Green, and Blue. Represented in the format: P(x, y, z).

  • Cylindrical coordinated system:
    Used to represent images in HSV form in a 3D space. Represented as P(r, theta, z); where
    'r' represents the Hue (H),  'theta' represents the Saturation (S), and 'z' represents the Value (V).

  • Spherical coordinate system: 
    Used to represent images in YUV format in 3D space. Represented as P(S, phi, sin(phi)); where 
    'S' represents the brightness (Y), 'phi' represents the blue formatting (U), and 'sin(phi)' represents the red projection (V).
Comment below for any doubts, add-ons, or other suggestions! Interactions are highly appreciated to create a 2-way learning environment...

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