Learn more about intelligent lighting | Part 2

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Innovative camera features



Learn more about two great innovative camera features

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Fast and precise, flat and wide

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This can be a critical factor when choosing a suitable camera, particularly when the amount of light available in a scene is limited.

Applications that rely on ambient light or need short exposure times are typical examples of the need for good sensitivity. There are mainly two parameters influencing the maximum sensitivity of a camera: The quantum efficiency which indicates the percentage of photons of a specific wavelength that are converted into electrons. Modern sensors reach a maximum efficiency greater than 60 % for certain wavelengths. The second important parameter is the background noise that can be measured without light hitting the sensor. Conse quently a sensor is more sensitive with a higher quantum efficiency and lower background noise.

The sensitivity is often mistaken with the maximum brightness of an image using controlled illumination. This apparent brightness, however, can also be reached easily by using a grey value multiplication, often called gain. The disadvantage of using a higher gain multiplier is increased noise and no additional signal. As the sensitivity of a camera is normally specified under different conditions and in a different way by the manufacturers, it is sometimes difficult to compare true sensitivity.

Typical sensitivity values as indicated by different camera manufacturers

Responsivity = 18.4 DN/(nJ/cm2)

Responsivity = 120x103 DN/(J/m2) @ 610 nm / 8 Bit / gain = 1

Peak response = 400 LSB/nJ/cm2

Sensitivity on sensor = 0.3 Lux, Max gain, 50 % video

Sensitivity = F5.6 (400 lx with FIX Gain)

Sensitivity = 200 lx, F5.6, 3000 K

Dynamic range

This is the term used to describe the ratio between the smallest and largest amplitude of a signal a camera can produce. In the context of machine vision, when describing camera features, it is the theoretically possible maximum variation of the signal. It has to be noted that this is not a true measure for the real dynamic range, as a certain noise component needs to be considered which will hide the smallest amplitude.

Signal-to-noise ratio (SNR)

The signal-to-noise ratio (SNR) considers the noise component. That is to say, the SNR describes the ratio between the maximum signal and the noise floor. The noise floor is a combination of dark noise, quantisation noise and photon shot noise. The value is typically represented in dB. The maximum possible value is limited by the photon shot noise and can be approximated (according to the EMVA1288 standard):

SNRmax= √(Maximum saturation capacity in electrons of a single pixel).

Example: Maximum saturation capacity of a single pixel = 20000 electrons. √(20000) = 141. This can be converted into dB using the following formula: 20 log(141) = 43 dB.

In contrast to the dynamic range which is basically only a theoretical value, the SNR describes the range of the real signal after the A/D conversion that is available for evaluation algorithms. Note: The human eye is only able to differentiate between 60 levels of grey or the equivalent to about 6 bits of data. Many camera manufacturers use A/D converters with a dynamic range significantly higher than the SNR of the sensor, however, in this case the lower bits of information will in fact only be noise.