The ultimate goal of a computer (application) program is to control the operation or processing of some form of data. More specifically, the goal of a machine vision system application program is to extract important features from image data from which a description, interpretation, or understanding of the objects in the image can be provided by the machine (computer) for the purpose of classification, reporting and decision making.
In a machine vision application, for example, a set of operations or steps are executed in sequence. A typical machine vision program flow diagram is shown in FIG. 1. The steps generally begin with the acquisition of an input image, which is captured by a camera and placed in memory, step 1. The image is optionally preprocessed, step 2, before the image is segmented and features are extracted from it. Segmentation and feature extraction, step 3, of the image into one or more distinct objects can be accomplished, for example by extracting their boundaries. Features can be any data that describes or identifies parts, features (holes, etc.) and objects in the camera scene, such as size, area, length, distance or like parameters.
Features are usually expressed in pixel units that describe the geometric position of a feature in the image relative to some fixed position in the image such as the top left corner of the image (also called an image coordinate system), or in physical units like "mm" or inches (also called a camera coordinate system) if the position of the camera in the scene is known or can be measured (usually referred to as calibrating the camera).
These features are typically checked against tolerances or classified into various categories, step 4, before a decision, step 5, can be made or pass/fail status and results obtained, step 6. Reports can also be generated, detailing the processing application, step 7. External control inputs, step 8, are used to activate the tolerance checking and to provide nominal tolerance values during execution for a particular feature.