Cognex Technology and Investment Corporation

Cognex Corporation

Jason Davis

Juha Tapio Koljonen

Adam Wagman

Ivan A Bachelder

Nathaniel R Bogan

Yusuf Akgul

Prabhav Morje

David J Michael

Michael C Moed

Sateesh Nadabar

Zihan Hans Liu

Robert J Tremblay, Ii

Matheen Siddiqui

This invention provides a system and method for displaying color match information on an acquired image of an object. A model/pattern having a plurality of color test points at locations of stable color is provided. A display process generates visible geometric shapes with respect to the color test points in a predetermined color. An alignment process aligns features of the object with respect to features on the model so that the geometric shapes appear in locations on the object that correspond to locations on the model. The geometric shapes can comprise closed shapes that surround a region expected to be stable color on the object. Such shapes can define circles, squares, diamonds or any other acceptable closed or open shape that is visible to the user on the display.

System and method for representing and displaying color accuracy in pattern matching by a vision system

This invention provides a system and method for determining the level of clutter in an image in a manner that is rapid, and that allows a scoring process to quickly determine whether an image is above or below an acceptable level of clutter&#x2014;for example to determine if the underlying imaged runtime object surface is defective without need to perform a more in-depth analysis of the features of the image. The system and method employs clutter test points that are associated with regions on the image that should contain a low gradient magnitude, indicative of emptiness. This enables the runtime image to be analyzed quickly by mapping trained clutter test points at locations in the coordinate space in which lack of emptiness indicates clutter, and if detected, can rapidly indicate differences and/or defects that allow for the subject of the image to be accepted or rejected without further image analysis.

System and method for determining clutter in an acquired image

The disclosed apparatus, systems, and methods can process height maps. For example, the disclosed computerized method of determining posture information of an object in a two-dimensional height map captured by a range sensor system can include receiving the height map of the object captured by the range sensor system, generating a plurality of one-dimensional slices of the height map and a location record indicating locations of the plurality of one-dimensional slices on the height map, identifying points of interest in the plurality of one-dimensional slices, determining locations of the identified points of interest in the height map based on the location record, and determining the posture information of the object based on the locations of the identified points of interest in the height map.

Apparatus, systems, and methods for processing a height map

A machine vision system includes a computer with one or more processors and software that has a plurality of tool routines each performing a different image analysis function. A machine vision application program is created by selecting certain ones of the plurality of tool routines to analyze the image. A maximum number of processors on the computer is designated as available for executing a machine vision application, wherein the maximum number may be less than the total number of processors on the computer. When the machine vision application program operates execution of each tool routine is limited to using simultaneously no more than the maximum number of processors.

Method of configuring a machine vision application program for execution on a multi-processor computer

A method is provided for dividing a pattern into a plurality of sub-patterns, each sub-pattern being adapted for use with an image search method that can provide a plurality of sub-pattern search results. The method represents the pattern as a plurality of feature points, generates candidate partitions of the plurality of feature points, and then scores the candidate partitions by examining characteristics of each potential sub-pattern of each candidate partition. The highest-scoring partition is selected, and then it is applied to the plurality of feature points, creating one or more sub-pluralities of features. The invention advantageously provides a plurality of sub-patterns where each sub-pattern contains enough information to be located with a feature-based search method, where that information has been pre-evaluated as being useful and particularly adapted for running feature-based searches.

Method for partitioning a pattern into optimized sub-patterns

Per one embodiment, a method is provided for refining a pose estimate of a model. The model is coarsely aligned with a run-time image, and it represents a 2D pattern. The pose estimate includes at least one pose estimate parameter. The model has a plurality of model features, and the run-time image has a plurality of run-time features. A given distance value is determined representing a given distance between a given one of the plurality of the model features mapped by a given pose estimate and a corresponding given run-time feature. A two-dimensional model description of the two-dimensional model pattern is provided. The two-dimensional model pattern is mapped using the given pose estimate to create a transformed version of the two-dimensional model pattern. The transformed version represents a non-linear movement of at least portions of the two-dimensional model pattern in a direction orthogonal to a plane of the two-dimensional model description.

General pose refinement and tracking tool

A method is disclosed for finding a deformed pattern in an image using a plurality of sub-patterns. By advantageously restricting sub-pattern search ranges, search speed is improved, and the incidence of spurious matches is reduced. The method also quickly decides which sub-pattern result, of several potential candidates, is most likely to be the correct match for a deformed sub-pattern. Also, a method is provided for characterizing a deformed pattern in an image by using results from feature-based search tools to create a mapping that models the deformation of the pattern. A transform, selectable by a user, is fit to the results from the search tools to create a global deformation mapping. This transformation is fit only to feature points derived from matches resulting from successful sub-pattern search, without including data from areas of the pattern that were blank, not matched, or otherwise didn't contain information about the pattern's distorted location.

Methods for finding and characterizing a deformed pattern in an image

A search method is disclosed for finding bar code symbols and various two-dimensional symbols, regardless of orientation, that is efficient, robust, and easy to use. The method works well on cluttered backgrounds, on skewed symbols, when symbols are almost touching, and when symbols are adjacent to text. The method includes choosing a candidate start region in the image, and then determining whether the candidate start region includes at least a portion of a candidate oriented encoded signal. Next, an approximate orientation of the candidate oriented encoded signal is determined. Then, the candidate start region is grown, using the approximate orientation, so as to form a candidate symbol region that encloses an entire candidate code symbol, including at least a portion of the candidate start region. The method is finely responsive to orientation.

Method for locating bar codes and symbols in an image

A method is provided for detecting spot defects on an object when an allowable variation (called the 'background') in the appearance of the object can be modeled. Methods are also provided for measuring and classifying detected spot defects. An alignment model is used to align the image of the object, a background model is then used to estimate the (possibly different) background in each region, and each background is substantially removed from the image so as to form a foreground image on which blob analysis can be applied to detect spot defects, the blob analysis using a threshold image that accommodates different noise statistics for each region. The method facilitates robust spot defect inspection of fiber optic end faces, or of any object with different object regions. The method also allows use of blob analysis over a larger range of conditions, including conditions that make simple blob analysis infeasible.

Methods and apparatuses for detecting classifying and measuring spot defects in an image of an object

A method of analyzing machine vision images to identify low contrast features such as scratches or cracks on the polished ends of optical fibers. A bank of oriented filters having incremental angles of orientation are tuned to respond to the frequency characteristics of oriented scratches or cracks having an approximate width. The filters are applied to an image to generate a filter response image for each filter orientation. The set of filter response images are combined to form a single data set indicating a magnitude and angle for each pixel in the original image. Elements of the combined data set having corresponding angles and magnitudes are grouped to form contour components that can be optionally input to higher order processes.

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Bellingham, MA, United States of America

Jason Davis