Cognex Corporation

Ali Moghaddam Zadeh

David J Michael

John Petry

Aaron S Wallack

Hongwei Zhu

Gang Liu

Robert A Wolff

Ruibing Wang

Matthew R Reome

Kim Marie Steiner

Steven Patrick Shuman

Benjamin D Klass

A system and method for three dimensional (3D) vision inspection using a 3D vision system. The system and method comprising acquiring at least one 3D image of a 3D object using the 3D vision system, using the 3D vision system; extracting a 3D visible runtime mask of the 3D image; using the 3D vision system, comparing the 3D runtime visible mask to a 3D reference visible mask; and, using the 3D vision system, determining if a difference of pixels exists between the 3D runtime visible mask and the 3D reference visible mask.

Using 3D vision for automated industrial inspection

This invention provides a system and method for reading and decoding ID features located on a surface of a curved, sloped and/or annular object, such as a tire moving on a conveyor. A plurality of 3D sensors are operatively connected to a vision system processor. The sensors are calibrated by calibration parameters to generate a stitched-together 3D image of a field of view in a common coordinate space. A motion conveyance (e.g. a conveyor) causes the object and the 3D sensors to move in relative motion, and the conveyance provides motion information to the vision system processor. An ID finder locates ID features within a version of the 3D image and a decoder (e.g. an OCR reader) generates data from the ID features. The ID finder can locate a trained portion of the ID and the search for variable code elements at a known orientation relative to the trained portion.

System and method for reading and decoding ID codes on a curved, sloped and/or annular object

A system and method for training a bead detection system can include identifying a starting region on a bead based on a starting indicator. The bead can be analyzed at the starting region to identify bead characteristics. The bead can then be analyzed away from the starting region, based on the identified bead characteristics, to identify a bead profile for use during non-training bead inspection.

Automated bead detection and detection training

A computer-implemented method for selecting at least one segmentation parameter for optical character recognition is provided. The method can include receiving an image having a character string that includes one or more characters. The method can also include receiving a character string identifying each of the one or more characters. The method can also include automatically generating at least one segmentation parameter. The method can also include performing segmentation on the image having the character string using the at least one segmentation parameter. The method can also include determining if a resultant segmentation satisfies one or more criteria and if the resultant segmentation satisfies the one or more criteria, selecting the at least one segmentation parameter.

System and method for selecting segmentation parameters for optical character recognition

System and method for selecting and displaying segmentation parameters for optical character recognition

This invention overcomes the disadvantages of the prior art by providing a system and method for flexibly detecting flaws in the acquired runtime/live images of objects based upon an inspection process that employs a training or model image of the object. This system and method enables predetermined flaws, and other predetermined features, within predetermined tolerances, to be disregarded as a flaw at inspection time. Typically, flexible flaw detection and repositioning of the model image occurs locally with respect to predetermined flaws and imperfections, and is undertaken after the image is acquired and the model image has undergone a global affine transformation/positioning with respect to the runtime image. Subsequent inspection then occurs with a locally repositioned model image that subsumes any imperfection or variation that is to be disregarded.

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Greenville, SC, United States of America

Ali Moghaddam Zadeh