{"id":2230,"date":"2022-01-21T14:51:00","date_gmt":"2022-01-21T19:51:00","guid":{"rendered":"https:\/\/www.1stvision.com\/machine-vision-solutions\/?p=2230"},"modified":"2022-01-21T14:56:23","modified_gmt":"2022-01-21T19:56:23","slug":"how-machine-vision-filters-create-contrast-in-machine-vision-applications","status":"publish","type":"post","link":"https:\/\/www.1stvision.com\/machine-vision-solutions\/2022\/01\/how-machine-vision-filters-create-contrast-in-machine-vision-applications.html","title":{"rendered":"How machine vision filters create contrast in machine vision applications"},"content":{"rendered":"\n

Imaging outcomes depend crucially on contrast. It is only by making a feature “pop” relative to the larger image field in which the feature lies, that the feature can be optimally identified by machine vision software.<\/p>\n\n\n\n

While sensor choice, lensing, and lighting are important aspects in building machine vision solutions with effective contrast creation, effective selection and application of filters can provide additional leverage for many applications. Often overlooked or misunderstood, here we provide a first-look at machine vision filter<\/a> concepts and benefits.<\/p>\n\n\n\n

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Before and after applying filters<\/strong><\/figcaption><\/figure><\/div>\n\n\n\n

In the 4 image pairs above, each left-half image was generated with the same sensor, lighting, and exposure duration as the corresponding right-half images. But the right-half images have had filters applied to reduce glare or scratch-induced scatter, separate or block certain wavelengths, for example. If your brain finds the left-half images to be difficult to discern, image processing software wouldn’t be “happy” with the left-half either! <\/p>\n\n\n\n

While there are also filtering benefits in color and SWIR imaging, it is worth noting that we started above with examples shown in monochrome. Surprising to many, it can often be both more effective and less expensive to create machine vision solutions in the monochrome space – often with filters – than in color. This may seem counter-intuitive, since most humans enjoy color vision, and use if effectively when driving, judging produce quality, choosing clothing that matches our skin tone, etc. But compared to using single-sensor color cameras, monochrome single sensor cameras paired with appropriate filters: <\/p>\n\n\n\n