Background estimation using median
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Background estimation using median

Background estimation is a common problem in image processing that, in some cases, could be resolved by using a simple method. If you imagine a highway with cars passing by, most of the time, the background is visible (e.g., road, trees, traffic signs) with an occasional "disturbance" in the form of a vehicle. These outliers are usually easily solved by using a median filter.

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6 questions that need to be answered before starting a computer vision project
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6 questions that need to be answered before starting a computer vision project

Main reason why there are so many topics related/dedicated to lights and cameras on our page is because I believe programming should always be the last resort for solving a problem. Choosing a correct light or camera could save countless hours of coding or even save a project. Coding is fun but wasting time on low quality images is often just that – wasting time. This article is written from a computer vision perspective, but it can be applied to a bigger picture.

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Making overexposure work for you part I
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Making overexposure work for you part I

Overexposure is the effect when optical sensor reaches saturation and from that point on, only the maximum pixel value (usually 255) is shown. Overexposed objects can cause a lot of headaches in image processing, but there are ways in which it can be made useful.

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Using extension methods in C# to expand external libraries
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Using extension methods in C# to expand external libraries

Extension methods are a great way of advancing an existing library without modifying the original class. But it is possible to do something even more powerful with it. Even the dlls of a closed source commercial library can be extended. Halcon is a commercial computer vision library which will be used for showcasing this application.

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Robust color detection
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Robust color detection

Reliably detecting colors can be a challenging task when the shape of the observed object is curved and/or has inconsistent light from all directions. In the photo shown we have dices for which we can say safely that the colors on each side should be very similar. If we were to inspect the RGB colors of left and bottom sides of the first dice (1 and 3) the results would be very different.

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