What is Image Processing?

Machine vision applications need high-quality images. Data is lost when images are unfocused, warped or poorly lit, which can cause data-extraction functions such as Blob or Histogram analysis to operate poorly or even fail. For more information, see Blob and Histogram.

The first step to producing high-quality images is to ensure that hardware and environmental issues such as lighting and lenses are optimized during equipment setup so that the acquired images are sharply focused, undistorted and evenly illuminated. If an In-Sight job has difficulty extracting the desired data from the images, the second step might be to use an Image function to enhance the images further.

As are all photos, In-Sight images are raster graphics. Raster graphics store information about image characteristics in a grid of picture elements ("pixels"), which are the smallest complete samples of an image and are not scalable. The quality of a raster image is determined by the total number of pixels (known as "resolution") and the amount of information in each pixel.

To enhance the desired object and remove or diminish distracting features in images—for example, by adjusting color, brightness, contrast or scaling—the In-Sight Image functions employ sophisticated image-processing algorithms to add or subtract data from individual pixels or groups of adjacent pixels (known as "neighbors").