Abstract: A method and apparatus are provided for identifying diffe rences between a stored pattern and a matching image subset, where variations in pattern position, orientation, and size do not give rise to false differences. The invention is also a system for analyzing an object image with respect to a model pattern so as to detect flaws in the object image. The system includes extracting pattern features from the model pattern; generating a vector-valued function using the pattern features to provide a pattern field; extracting image features from the object image; evaluating each image feature, using the pattern field and an n-dimensional transformation that associates image features with pattern features, so as to determine at least one associated feature characteristic; and using at least one feature characteristic to identify at least one flaw in the object image. The invention can find at least two distinct kinds of flaws: missing features, and extra features.

Abstract: A method and apparatus are provided for identifying differences between a stored pattern and a matching image subset, where variations in pattern position, orientation, and size do not give rise to false differences. The invention is also a system for analyzing an object image with respect to a model pattern so as to detect flaws in the object image. The system includes extracting pattern features from the model pattern; generating a vector-valued function using the pattern features to provide a pattern field; extracting image features from the object image; evaluating each image feature, using the pattern field and an n-dimensional transformation that associates image features with pattern features, so as to determine at least one associated feature characteristic; and using at least one feature characteristic to identify at least one flaw in the object image. The invention can find at least two distinct kinds of flaws; missing features, and extra features.

Abstract: The disclosed methods and apparatuses leverage a known value of a characteristic of an object to partially calibrate an imaging system “on-the-fly”, and minimize, if not eliminate, the need for a separate calibration image(s). Specifically, the scale relationship (i.e. the relationship between physical dimensions and image dimensions) is calculated using the known value and a measured value of the characteristic from the image. The same image used to calculate the scale relationship is also processed, such as inspected, for example. The known value can be a measurement of an aspect of many things, including an inherent feature, or a relationship between features, for example. One embodiment uses a model to find the characteristic. A described preferred embodiment inspects an end-face of a fiber-optic cable, wherein the known value is the diameter of an annular cladding of the fiber-optic cable.

Abstract: A single fiducial mark with at least one characteristic of known value is placed on one or more objects that are examined by a machine vision system. Alternatively, a pre-existing object feature that has a known characteristic can be used. An image acquisition system can be calibrated by examining the objects with a vision tool that can align objects with translation and at least one additional degree of freedom and that can measure the value of the characteristic. In general, information measured by the vision tool can be used to infer similar information concerning the calibration. For example, in one embodiment, the single fiducial mark has a precise dimension and the vision tool can measure the dimension in order to calibrate the scale of the system. In another embodiment, each object to be examined is held in a predetermined position by a fixture on the materials handling system and a fiducial mark is placed on each object.

Abstract: A single non-rotationally symmetric fiducial mark, such as a Universal Alignment Target, is placed on an object and the alignment of the object is determined with a vision tool that can align objects with translation and at least one additional degree of freedom, such as angular orientation. Alternatively, if a unique non-rotationally symmetric feature occurs naturally on the object, that feature may be used instead of a fiducial mark. In one embodiment, a geometrical feature matching vision tool is used to locate the single fiducial mark. In another embodiment, a normalized correlation search tool is used to locate the mark. The tool internally generates a set of rotated and scaled patterns that are then stored. The stored patterns are subsequently used to perform the search. In still another embodiment, a normalized correlation search tool internally generates a set of rotated and scaled patterns at runtime when the search for the mark is being performed.

Abstract: A method and apparatus are provided for identifying differences between a stored pattern and a matching image subset, where variations in pattern position, orientation, and size do not give rise to false differences. The invention is also a system for analyzing an object image with respect to a model pattern so as to detect flaws in the object image. The system includes extracting pattern features from the model pattern; generating a vector-valued function using the pattern features to provide a pattern field; extracting image features from the object image; evaluating each image feature, using the pattern field and an n-dimensional transformation that associates image features with pattern features, so as to determine at least one associated feature characteristic; and using at least one feature characteristic to identify at least one flaw in the object image. The invention can find at least two distinct kinds of flaws: missing features, and extra features.