Abstract: Methods and apparatus to locate and decode an arranged plurality of barcodes in an image are disclosed. An example method includes obtaining image data representing an image of an environment appearing within a FOV of an imaging device that includes the image sensor, wherein an arranged plurality of barcodes appear in the image. A first subset of the plurality of barcodes is decoded from the image data. One or more parameters representing a predicted arrangement of the plurality of barcodes in the image is determined based upon location information associated with each of the decoded first subset of the plurality of barcodes. Possible locations for respective ones of a second subset of the plurality of barcodes are determined based upon the one or more parameters, and the second subset of the plurality of barcodes are attempted to be decoded from the image data in vicinities of the respective possible locations.

Abstract: Methods and apparatus to locate barcodes in color images are disclosed. An example method includes: obtaining a color digital image using an image sensor, the image including a plurality of pixels represented by respective ones of a plurality of luminance components and respective ones of a plurality of color components; determining, based on the luminance components, a plurality of barcode indicative characteristics for respective ones of a plurality of regions of the image; determining, based on the plurality of color components, a plurality of color content amounts for respective ones of the plurality of regions; identifying one or more regions of the plurality of regions that have their respective color content amount satisfy a first criteria, and their respective barcode indicative characteristic satisfy a second criteria; and processing image data corresponding to each of the one or more regions to attempt to identify one or more barcodes in the image.

Abstract: Systems and methods for tracking objects in space are disclosed. The systems and methods include capturing two-dimensional (2D) image data from which a barcode is decoded and capturing, generating, or otherwise accessing three-dimensional (3D) image data from which a 3D object is identified. A 2D image of a barcode and barcode data is combined with the 3D object to form reference 3D object data that is used for comparison to subsequently captured 3D and 2D image data. In some examples, a four-dimensional (4D) projection of the reference 3D object data is used for comparison and validation of the subsequently captured 3D and 2D image data.

Abstract: Methods and apparatus to locate and decode an arranged plurality of barcodes in an image are disclosed. An example method includes obtaining image data representing an image of an environment appearing within a FOV of an imaging device that includes the image sensor, wherein an arranged plurality of barcodes appear in the image. A first subset of the plurality of barcodes is decoded from the image data. One or more parameters representing a predicted arrangement of the plurality of barcodes in the image is determined based upon location information associated with each of the decoded first subset of the plurality of barcodes. Possible locations for respective ones of a second subset of the plurality of barcodes are determined based upon the one or more parameters, and the second subset of the plurality of barcodes are attempted to be decoded from the image data in vicinities of the respective possible locations.

Abstract: Systems and methods for implementing a hybrid machine vision model to optimize performance of a machine vision job are disclosed herein. An example method includes: (a) receiving, at a machine vision job including one or more machine vision tools, a set of training images; (b) generating, by the machine vision tools, prediction values corresponding to the set of training images; (c) inputting the prediction values into a machine learning (ML) model configured to receive prediction values and output a change value corresponding to the machine vision job; (d) adjusting the machine vision job based on the change value to improve performance of the machine vision job; (e) iteratively performing steps (a)-(e) until the ML model determines that the prediction values satisfy a prediction threshold; and executing, on a machine vision camera, the machine vision job to analyze a run-time image of a target object and output an inspection result.

Abstract: In some implementations, a device may identify, for a printing operation, a media type associated with media involved in the printing operation. The device may receive, from a sensor, a sensor measurement associated with an ambient condition of the printer. The device may determine, using a print optimization model, a printing configuration for the printing operation based on the media type and the ambient condition, wherein the print optimization model is trained based on reference data associated with historical printing operations associated with one or more printers, wherein the reference data includes reference configurations associated with the historical printing operations, respective media types of media used in the historical printing operations, and corresponding ambient conditions of the one or more printers during the historical printing operations. The device may cause the printer to perform the printing operation according to the printing configuration.

Abstract: In some implementations, a device may identify, for a printing operation, a media type associated with media involved in the printing operation. The device may receive, from a sensor, a sensor measurement associated with an ambient condition of the printer. The device may determine, using a print optimization model, a printing configuration for the printing operation based on the media type and the ambient condition, wherein the print optimization model is trained based on reference data associated with historical printing operations associated with one or more printers, wherein the reference data includes reference configurations associated with the historical printing operations, respective media types of media used in the historical printing operations, and corresponding ambient conditions of the one or more printers during the historical printing operations. The device may cause the printer to perform the printing operation according to the printing configuration.

Abstract: Methods and apparatus to locate barcodes in color images are disclosed. An example method includes: obtaining a color digital image using an image sensor, the image including a plurality of pixels represented by respective ones of a plurality of luminance components and respective ones of a plurality of color components; determining, based on the luminance components, a plurality of barcode indicative characteristics for respective ones of a plurality of regions of the image; determining, based on the plurality of color components, a plurality of color content amounts for respective ones of the plurality of regions; identifying one or more regions of the plurality of regions that have their respective color content amount satisfy a first criteria, and their respective barcode indicative characteristic satisfy a second criteria; and processing image data corresponding to each of the one or more regions to attempt to identify one or more barcodes in the image.

Abstract: Systems and methods for object recognition with limited input are disclosed herein. An example method includes updating a neural network trained to perform object recognition on a first rendition of an object, so that the neural network performs object recognition on a second rendition of the object, using a limited set of input images. The method includes receiving a limited set of model images of the second rendition of the object, accessing a corresponding image mapping, and generating a large number of training images from the limited set, where image mappings include geometric, illumination, and/or obscuration transformations. The neural network is then trained, from this initial small set, to classify the second rendition of the object.

Abstract: A method of generating neural network training image annotations includes training a first neural network to identify and segment hands in images using a first set of 2D images with hand portions segmented in each image; substantially simultaneously capturing both a second set of 2D images, and a third set of images including depth images, depicting hands holding a particular type of object; correlating each of the second set of images with corresponding images of the third set to identify and segment foregrounds from backgrounds in the second set of images; applying the trained first neural network to the identified foregrounds to identify hand portions of the foregrounds and segment object portions from identified hand portions; and training a second neural network, using the segmented object portions of the second set of images as training data, to identify the particular type of object in new images.

Abstract: Example methods and apparatus to read barcodes on reflective surfaces are disclosed herein. An example method for handling a reflective surface in an imaging reader includes capturing one or more images of a surface of an item; detecting whether a specular reflection is present in the one or more images; when a specular reflection is detected, processing one or more additional images of the surface using a predetermined pattern of first processing suitable to identify a barcode on a reflective surface and second processing suitable to identify a barcode on a non-reflective surface; and when a specular reflection is not detected, processing one or more additional images of the surface using the second processing suitable to identify a barcode on a non-reflective surface.

Abstract: Example methods and apparatus to read barcodes on reflective surfaces are disclosed herein. An example method for handling a reflective surface in an imaging reader includes capturing one or more images of a surface of an item; detecting whether a specular reflection is present in the one or more images; when a specular reflection is detected, processing one or more additional images of the surface using a predetermined pattern of first processing suitable to identify a barcode on a reflective surface and second processing suitable to identify a barcode on a non-reflective surface; and when a specular reflection is not detected, processing one or more additional images of the surface using the second processing suitable to identify a barcode on a non-reflective surface.

Abstract: A method of generating neural network training image annotations includes training a first neural network to identify and segment hands in images using a first set of 2D images with hand portions segmented in each image; substantially simultaneously capturing both a second set of 2D images, and a third set of images including depth images, depicting hands holding a particular type of object; correlating each of the second set of images with corresponding images of the third set to identify and segment foregrounds from backgrounds in the second set of images; applying the trained first neural network to the identified foregrounds to identify hand portions of the foregrounds and segment object portions from identified hand portions; and training a second neural network, using the segmented object portions of the second set of images as training data, to identify the particular type of object in new images.

Abstract: Systems and methods for object recognition with limited input are disclosed herein. An example method includes updating a neural network trained to perform object recognition on a first rendition of an object, so that the neural network performs object recognition on a second rendition of the object, using a limited set of input images. The method includes receiving a limited set of model images of the second rendition of the object, accessing a corresponding image mapping, and generating a large number of training images from the limited set, where image mappings include geometric, illumination, and/or obscuration transformations. The neural network is then trained, from this initial small set, to classify the second rendition of the object.

Abstract: A method and apparatus for correcting auto-exposure settings of a barcode reader based on modifying an auto-exposure region at a barcode reader for decoding a barcode in response to identifying a barcode and failing to decode the barcode due to incorrect initial exposure parameters, wherein the modified auto-exposure region is based at least in part on the barcode location.

Abstract: A barcode reader is provided. The barcode reader includes a first image acquisition assembly having a first imager assembly configured to capture infrared (IR) light and an IR illumination assembly configured to emit IR light over at least a portion of a first field of view (FOV) of the first imager assembly so as to illuminate targets within the first FOV. The barcode reader further includes a second image acquisition assembly having a second imager assembly configured to capture visible light and a visible-light illumination assembly configured to emit visible light over at least a portion of a second FOV of the second imager assembly so as to illuminate targets within the second FOV.

Abstract: Systems and methods for object recognition with limited input are disclosed herein. An example method includes updating a neural network trained to perform object recognition on a first rendition of an object, so that the neural network performs object recognition on a second rendition of the object, using a limited set of input images. The method includes receiving a limited set of model images of the second rendition of the object, accessing a corresponding image mapping, and generating a large number of training images from the limited set, where image mappings include geometric, illumination, and/or obscuration transformations. The neural network is then trained, from this initial small set, to classify the second rendition of the object.

Abstract: Systems and methods for object recognition with limited input are disclosed herein. An example method includes updating a neural network trained to perform object recognition on a first rendition of an object, so that the neural network performs object recognition on a second rendition of the object, using a limited set of input images. The method includes receiving a limited set of model images of the second rendition of the object, accessing a corresponding image mapping, and generating a large number of training images from the limited set, where image mappings include geometric, illumination, and/or obscuration transformations. The neural network is then trained, from this initial small set, to classify the second rendition of the object.

Abstract: A method and apparatus for correcting auto-exposure settings of a barcode reader based on modifying an auto-exposure region at a barcode reader for decoding a barcode in response to identifying a barcode and failing to decode the barcode due to incorrect initial exposure parameters, wherein the modified auto-exposure region is based at least in part on the barcode location.

Abstract: A barcode reader is provided. The barcode reader includes a first image acquisition assembly having a first imager assembly configured to capture infrared (IR) light and an IR illumination assembly configured to emit IR light over at least a portion of a first field of view (FOV) of the first imager assembly so as to illuminate targets within the first FOV. The barcode reader further includes a second image acquisition assembly having a second imager assembly configured to capture visible light and a visible-light illumination assembly configured to emit visible light over at least a portion of a second FOV of the second imager assembly so as to illuminate targets within the second FOV.