A spot size of at least 3 to 4 pixels is required for clean optical detection. This means that an optical feature must cover at least this number of pixels on the sensor in order to be clearly and reliably detected.

Smaller spots can also be detected, but only if the contrast is good. If the contrast is poor, the detection accuracy drops significantly. The minimum size of a detectable feature therefore depends on the contrast of the feature.

Rule of thumb

A general rule of thumb is that a camera with 3 times the number of pixels is needed to detect smaller features. This means that the higher the physical resolution of the camera, the more accurately the smallest features can be detected.

Practical example

A clear example illustrates the importance of physical resolution: for a 64 mm wide image with 640 pixels, the physical resolution is 0.1 mm per pixel. This means that clearly visible features are not 0.1 mm in size, but 0.3 mm. This increase in size takes into account the required spot size of at least 3 pixels for reliable detection.

Measurement Algorithms and Accuracy

Modern measurement algorithms have an impressive accuracy of less than 1 pixel. An example of this is the calculation of distances: Instead of measuring 28 or 29 pixels, precise algorithms allow measurements of 28.37 pixels. This decimal accuracy is achieved through 'sub-pixel interpolation', which uses the inflection point of the greyscale function to achieve more accurate results.

Subpixel Interpolation

Sub-pixel interpolation is a technique that can increase measurement accuracy to less than 1 pixel. Under laboratory conditions, an accuracy of up to 1/20 pixel can be achieved. In real-world applications, however, the measurement accuracy is typically between 1/2 and 1/10 pixel, taking into account probing and calibration errors.

Averaging local anomalies

Another important aspect of measurement accuracy is the averaging of local anomalies such as imaging errors, contour variations and sensor noise. With a large measurement range, averaging of these anomalies is critical to improving accuracy and achieving consistent measurement results.

In the example shown, it therefore makes a difference to the achievable accuracy if the measurement tool window is only one pixel wide or many pixels wide!

Practical example

A concrete example illustrates the importance of measurement accuracy. If a distance of 64 mm is to be measured and the camera has a resolution of 640 pixels, the measurement accuracy is not limited to whole pixels. By using precise algorithms, the distance can be measured with an accuracy of one decimal place, which means that clearly visible features are not just 0.1 mm in size, but can be measured with an accuracy of around 0.05 to 0.01 mm.

This means that a much lower camera resolution is required than for detecting the smallest structures.

Another approach to determining the required camera resolution is to consider the software algorithms used.

A few examples

• For reliable text recognition, characters should be around 30 pixels high to distinguish characters such as a 'G' from a '6', an 'O' from a '0', etc.
• Barcodes require a minimum of 2 to 3 pixels per smallest bar width.
• Data Matrix codes can be recognised from a module size of 3 to 4 pixels, depending on the marking process, but code grading requires approximately 10 pixels.