#And the color machine how to

This is particularly challenging when light levels are lower because color values become compressed into a smaller range of possible values. Pieces with slightly different shades can be used together on other jackets or purses, but mixing different shades on a single item would not be acceptable.Īchieving high levels of differentiation requires color accuracy that can be calculated with a high degree of precision and repeatability. For example, when multiple pieces of leather are used to create a purse or jacket, it is important that all the pieces for a single item have the same shade of color. Two cameras looking at the same target may render different color values even when both are similarly white balanced.ĭepending on the application, it might be crucial that the machine vision camera can distinguish subtle variances of the same color. The goal is to produce values that most closely match the “true” color value that might be calculated using precise laboratory equipment under the same lighting conditions. While two cameras may both produce “pleasing” images, the specific pixel values may be different depending on the type, quality, and/or age of the cameras. Machine vision color cameras provide the host computer with pixel-level data generated by the reflected or incident light coming from the scene. The degree to which a color machine vision system can perform in any of the broad application categories listed above depends on its ability to measure up to several key challenges: Precise color matching ensures consistency in automobiles, packaging, wood flooring and more. This matching process can be used to make sure printed material matches a predefined corporate color, or to make sure that a car’s side view mirror matches the door panel, or for many other applications. Once the application has assigned a color value, it can compare that against a target color or a range of target color values.

For an application to use the data coming from the color machine vision camera, the host computer first needs to connect a color value to each pixel or to a histogram representing an area or blob of pixels. Sorting by color is a common application in food products, but is also used for many industrial applications.Ĭolor detection aims to teach the camera what color it is looking at.

For example, cherries, apples, and other fruits might be sorted by their color as a way of indicating ripeness. Subsequently, it can also be used as a way of grading certain objects. That means color imaging can be used in classifying or categorizing objects by color. The use of color enables inspections that can't be performed in black-and-white, such as checking color coded components on printed circuit boards.Ĭolor imaging in machine vision can also be used to separate items based on their color. If you want to check if each wire is connected to the right connector on the board, your machine vision system has to be able to read the color of the wire and see if there’s a match. Take the inspection of color-coded wires for example. Especially when your aim is to classify defects or check the shape of colored products, the use of color imaging is crucial. The majority of these color imaging applications fall into three broad categories:Ĭolor imaging can provide you with additional data that can optimize your inspection process. Applications of color imaging in machine visionĪ wide range of applications can benefit from the use of color imaging in machine vision.

#And the color machine how to

Where once designing a color machine vision system required having extensive knowledge of color science and how to work with color image data, advanced software libraries and built-in camera capabilities have simplified the process to make working with color more straightforward than ever before. Now that cameras with resolutions of five megapixels or higher are commonplace, the resolution penalty has become less of a factor and machine vision designers can more easily meet their requirements using color cameras.Īlong with the improvements in sensor technology have come software libraries and camera firmware much better tuned to color imaging requirements. When most camera resolutions were under two megapixels, the resolution penalty on standard single-sensor color cameras made them unsuitable for many tasks.

This process, however, typically reduces the effective resolution of the image. Since image sensors can’t actually “see” colors, color cameras must use filter arrays and other techniques to capture light in a way that allows color imaging information to be derived. Part of this improvement has been the result of the increasing resolution of camera imagers. In recent years, the quality of color machine vision systems has increased significantly.