Progress in machine vision processing is charted in major developments that prove significant staying power. One of the first was blob analysis introduced around 1977 as part of the SRI Algorithm. Blob analysis remains a major component of machine vision software. Another was the introduction of morphology around 1985. Morphology also remains part of the image processing tool kit. Then there was geometric pattern matching introduced into machine vision around 1997. It too remains a major tool in machine vision image processing.
Machine vision image processing (shown in Figure 1) consists of a chain of processes typically starting with preprocessing and then followed by segmentation, feature extraction, and interpretation. Preprocessing uses algorithms that transform an image into an image, such as low-pass filtering to remove noise or edge detection to find the edges of objects in the image. Preprocessing is not needed in all machine vision applications. Segmentation isolates the individual objects or the features on an object to allow them to be individually analyzed. Not all applications need segmentation. All applications do need feature extraction – getting feature values out of the image that characterize the properties that are significant for the application. Interpretation often uses logic and calculations to determine if the part is an accept or reject, which bin the part should be sent to, or where to direct the robot to go.