Cognex Corporation

Karen Roberts

Karen B Sarachik

Ivan A Bachelder

William M Silver

Aaron S Wallack

Yasunari Tosa

Raymond A Fix

Venkat K Gopalakrishnan

Rob Milligan

Therese Hunt

Edward Collins

Yun Chang

A method and system create a geometric object model for use in machine vision inspection. A pixel image representation of an object is acquired. Based on this pixel image representation, part models for the parts of the object are generated. Each part model corresponds to a different part of the object. From the part models of the object, a model for the entire object can be created. Using this created object model, a test inspection is performed on a set of test images, and each of the test images is associated with a set of known inspection measurements. The test inspection produces a set of testing inspection measurements. If the test inspection yields satisfactory performance, the object models created are stored.

Creating geometric model descriptions for use in machine vision inspection systems

A method is disclosed for finding a pose of a geometric model of a fiducial mark within an image of a scene containing the fiducial mark. The method includes receiving input from a user including geometric model shape information and geometric model dimensions, selecting a pre-defined model having sub-models by using the model shape information, the pre-defined model being parameterized by the geometric model dimensions; and then using the pre-defined model to find an image of the fiducial mark in the image of the scene. The act of selecting a pre-defined model having sub-models, includes pre-defining a model such that the sub-models and the relationships among the sub-models are selected by exploiting knowledge of features and properties of the fiducial sought so as to provide more accurate location of the fiducial. The invention provides enhanced ease of use of a sub-model-based search method by requiring only the shape and dimensions of the fiducial sought. An appropriate model and it's sub-models are automatically provided. The model and it's sub-models are particularly appropriate to the application when they are based on knowledge of the variability of the fiducial, and other aspect of the behavior of the fiducial throughout a manufacturing process, for example.

Fiducial mark search using sub-models

A machine vision method for locating a leaded electronic device, such as integrated circuit chips, includes searching the image with a template depicting only a portion&mdash;e.g., one or two &ldquo;side&rdquo; edges&mdash;of each of one or more of the leads expected on the device. The template can include portions representing expected leads, as well as unexpected leads or unexpected lead positions. Positive and negative weightings are applied to such portions, as appropriate. A location of the device, or of one or more of its leads, is based on the results of the search.

Machine vision methods for inspection of leaded components

A method is disclosed for finding a pose of a geometric model of a fiducial mark within an image of a scene containing the fiducial mark. The method includes receiving input from a user including geometric model shape information and geometric model dimensions, selecting a pre-defined model having sub-models by using the model shape information, the pre-defined model being parameterized by the geometric model dimensions; and then using the pre-defined model to find an image of the fiducial mark in the image of the scene. The act of selecting a pre-defined model having sub-models includes pre-defining a model such that the sub-models and the relationships among the sub-models are selected by exploiting knowledge of features and properties of the fiducial sought so as to provide more accurate location of the fiducial. The invention provides enhanced ease of use of a sub-model-based search method by requiring only the shape and dimensions of the fiducial sought. An appropriate model and it's sub-models are automatically provided. The model and it's sub-models are particularly appropriate to the application when they are based on knowledge of the variability of the fiducial, and other aspect of the behavior of the fiducial throughout a manufacturing process, for example.

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Natick, MA, United States of America

Karen Roberts