Cognex Corporation

Cognex Technology and Investment Corporation

Ivan A Bachelder

Chen Gao

James A Negro

Francisco Azcona Guerrero

Albert A Montillo

William M Silver

Cyril C Marrion

Esther Oteo Lozano

Andreas Weber

Robert Drisko

Adam Wagman

Juha Tapio Koljonen

Karen B Sarachik

Jose Fernandez Dorado

Raymond A Fix

Venkat K Gopalakrishnan

Yusuf Akgul

Prabhav Morje

Shekhar Sastry

Yael Steinsaltz

Aaron S Wallack

Jason Davis

Karen Roberts

Savithri Vaidyanathan

Laurens Nunnink

Lowell D Jacobson

Xiangyun Ye

Sateesha Nadabar

Yasunari Tosa

Sateesh Nadabar

John Bryan Boatner

Torsten Kempf

Earle Stokes

Yoshikazu Ohashi

Yian Leng Chang

Kyra Moed

Cyril C Marrion Jr

Yun Chang

Rob Milligan

Therese Hunt

Robert Milligan, Jr

Leonid Segal

Jay Negro

Edward Collins

Edward A Collins, Jr

Fenghua Jin

Masayoki Kawata

Toshimitsu Motoyama

José Fernandez-Dorado

Emilio Pastor Mira

This invention provides a system and method for detecting and acquiring one or more in-focus images of one or more barcodes within the field of view of an imaging device. A measurement process measures depth-of-field of barcode detection. A plurality of nominal coarse focus settings of a variable lens allow sampling, in steps, of a lens adjustment range corresponding to allowable distances between the one or more barcodes and the image sensor, so that a step size of the sampling is less than a fraction of the depth-of-field of barcode detection. An acquisition process acquires a nominal coarse focus image for each nominal coarse focus setting. A barcode detection process detects one or more barcode-like regions and respective likelihoods. A fine focus process fine-adjusts, for each high-likelihood barcode, the variable lens near a location of the barcode-like regions. The process acquires an image for decoding using the fine adjusted setting.

System and method for auto-focusing a vision system camera on barcodes

Embodiments relate to predicting height information for an object. First distance data is determined at a first time when an object is at a first position that is only partially within the field-of-view. Second distance data is determined at a second, later time when the object is at a second, different position that is only partially within the field-of-view. A distance measurement model that models a physical parameter of the object within the field-of-view is determined for the object based on the first and second distance data. Third distance data indicative of an estimated distance to the object prior to the object being entirely within the field-of-view of the distance sensing device is determined based on the first distance data, the second distance data, and the distance measurement model. Data indicative of a height of the object is determined based on the third distance data.

Methods and apparatus for using range data to predict object features

The techniques described herein relate to methods, apparatus, and computer readable media configured to decode a symbol in a digital image. A digital image of a portion of a symbol is received, which includes a grid of pixels and the symbol includes a grid of modules. A spatial mapping is determined between a contiguous subset of modules in the grid of modules to the grid of pixels. Causal relationships are determined, using the spatial mapping, between each module and the grid of pixels. A set of valid combinations of values of neighboring modules in the contiguous subset of modules are tested against the grid of pixels using the causal relationships. A value of at least one module of the two or more neighboring modules is determined based on the tested set of valid combinations. The symbol is decoded based on the determined value of the at least one module.

Methods and apparatus for decoding under-resolved symbols

Embodiments related to local tone mapping for symbol reading. A local pixel neighborhood metric is determined for at least one raw pixel in a region-of-interest, which identifies on one or more raw pixels near the at least one raw pixel. A local mapping function is determined for the at least one raw pixel that maps the value of the raw pixel to a mapped pixel value with a mapped bit depth that is smaller than the bit depth associated with the raw image. The local mapping function is based on a value of at least one other raw pixel near the at least one raw pixel within the local pixel neighborhood metric, and at least one parameter determined based on the raw image. A mapped image is computed for the region-of-interest by applying the local mapping function to the raw image.

Local tone mapping for symbol reading

Determining dimensions of an object can include determining a distance between the object and an imaging device, and an angle of an optical axis of the imaging device. One of more features of the object can be identified in an image of the object. The dimensions of the object can be determined based upon the distance, the angle, and the one or more identified features.

Object dimensioning system and method

Systems and methods are provided for decoding barcodes. A scan signal is acquired along a scan through a barcode. A first character unit grid for a unit width pattern within the barcode along the scan is determined. At least one set of sampling coefficients relating the unit width pattern to a portion of the scan signal is determined based on the first character unit grid. The element width pattern for the unit width pattern is determined based on the at least one set of sampling coefficients and the portion of the scan signal.

Decoding barcodes

Computerized methods and systems for locating barcodes applied to objects are provided. A method can receive a first image of a first barcode fragment applied to a first object captured at a first time and identify a first position of the first barcode fragment. The method can also receive a second image of a second barcode fragment captured at a second time and identify a second position of the second barcode fragment. The method can also predict a range of possible positions of the first barcode fragment at the second time based on a tracking model that tracks the first barcode fragment based on the first position, and determine that the first barcode fragment and the second barcode fragment correspond to the same barcode, if the second position is within the range of possible positions of the first barcode fragment at the second time.

Systems and methods for tracking optical codes

This invention provides a system and method for finding features in images that exhibit saddle point-like structures using relatively computationally low-intensive processes, illustratively consisting of an anti-correlation process, and associated anti-correlation kernel, which operates upon a plurality of pixel neighborhoods within the image. This process enables an entire image to be quickly analyzed for any features that exhibit such saddle point-like structures by determining whether the anti-correlation kernel generates a weak or strong response in various positions within the image. The anti-correlation kernel is designed to generate a strong response regardless of the orientation of a saddle point-like structure. The anti-correlation process examines a plurality of pixel neighborhoods in the image, thereby locating any saddle point-like structures regardless of orientation, as it is angle-independent. The structures are then grouped and refined (for example in a grid) in an effort to locate and decode ID topologies within the image.

System and method for finding saddle point-like structures in an image and determining information from the same

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Hillsbobough, NC, United States of America

Ivan A Bachelder