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I am trying to read up on the theory (for Hardware) of how hardware works in a Vision/Camera System from the Camera Sensor all the way to the pixels on the screen.

I dont know what to start googling to understand how the time domain relates to the spatial domain and the hardware in between (like in cell phones). What should I be searching for?

PS: Sorry in advance for the vague question, but looking for a direction.

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closed as unclear what you're asking by Andy aka, pipe, Leon Heller, Dmitry Grigoryev, Voltage Spike Mar 15 '17 at 17:02

Please clarify your specific problem or add additional details to highlight exactly what you need. As it's currently written, it’s hard to tell exactly what you're asking. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.

  • \$\begingroup\$ Its really dependent on the sensor and technology. If you really want to dive into this then a good way would be to try to interface a cmos imager with a microcontroller \$\endgroup\$ – Voltage Spike Mar 15 '17 at 17:02
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That's an extremely broad question, but to get you started, here are some topics.

For cameras, you'll want to look into

  • nonuniformity correction
  • dead pixel removal
  • automatic exposure control
  • contrast enhancement
  • gamma correction
  • signal formatting and transmission

For displays, you'll want

  • signal reception and decoding
  • frame rate adjustment
  • geometry (frame size) adjustment
  • panel drivers

Machine vision is a very different topic from getting an image on a display; it involves finding edges in the image data and inferring the existence of shapes and objects in the scene so that software can make decisions about what it "sees". A good place to start for that is OpenCV.org

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The human retina provides localized convolution (Mexican-hat) gradient detection; machine vision uses the same methods. Once local gradients are detected, the retina uses its parallelism (100Million pixels) to examine local, medium and large scale "textures"; upsets to the "texture" become very interesting to humans. Shape-from-texture and shape-from-monocular-occlusion lets humans derive a 3_D model of the world.

Machine vision does not yet have this 100Million-pixel-in-parallel processing.

Regarding the process of sensor-to-pixel-on-screen, the sensor uses a grid of diodes, these diodes having sufficient junction depth to with some likelihood absorb the photons and collect an electron or two or thousands. DarkCurrent sets the detection floor. Periodically, these diodes are flushed of their electrons as the electrons are converted into voltages and those voltages become quantized numbers, sent offchip to various compute engines. Or your screen.

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