Abstract: A controller of a barcode reader is disclosed. The controller may receive, via a user input, a charge threshold associated with presenting a battery level indicator on the display panel. The controller may monitor, during a charging operation, a state of charge of the battery. The controller may determine that the state of charge satisfies the charge threshold. The controller may cause, during the charging operation, the display panel to indicate the battery level indicator.

Abstract: At least some embodiment of the present invention are directed to mean for operating an illumination assembly associated with a group of imaging devices. An example method includes causing each of a plurality of imaging devices to capture image data during a group acquisition operation, each of the plurality of imaging devices has (i) a respective delay until a start of an exposure duration, (ii) the respective exposure duration, and (iii) a total operation duration. The method also includes causing an illumination assembly to transition from a first state to a second state based on a shortest delay of the imaging devices and further causing the illumination assembly to transition from the second state to a third state based on a longest total operation duration of the imaging devices.

Abstract: At least some embodiments are directed to systems and methods to optimize relative signal delays in vision systems having illumination assemblies separate from a host. In an example embodiment there is a system that includes a host device having, an imaging assembly coupled to the host device and operable to capture image data, and an illumination assembly coupled to the host device and operable to provide illumination. The system is configured such that the host transmits, to the imaging assembly, a series of exposure signals causing the imaging assembly to capture a series of frames and transmits, to the illumination assembly, a series of illumination signals causing the illumination assembly to provide the illumination as a series of strobes. Thereafter the host evaluate each frame to identify a peak-brightness frame and from that, based on a corresponding illumination signal, determines an appropriate relative signal delay for future operations.

Abstract: Imaging devices, systems, and methods for imaging or scanning objects using composite color light are described herein. An example device includes: a plurality of light sources, each configured to emit light in a different wavelength range; an imaging assembly having an imaging sensor operable to receive light from a field of view (FOV), the imaging sensor configured to capture image data during a frame having an exposure duration and a non-exposure duration; and a microprocessor and computer-readable media storing machine readable instructions that, when executed, cause: a first subset of the plurality of light sources to emit a first light during a first illumination duration that at least partially overlaps with the exposure duration of the frame; and a second subset of the plurality of light sources to emit a second light during a second illumination duration that at least partially overlaps with the non-exposure duration of the frame.

Abstract: Systems and methods for changing programs on imaging devices are disclosed herein. An example method includes capturing, by a first imaging device, an image of a target object, and transmitting the image of the target object to a leader device that is communicatively coupled to the first imaging device and a second imaging device. The second imaging device may be different from the first imaging device. The example method may further include analyzing, by the leader device, the image of the target object using a photogrammetric technique to determine a dimension of the target object, and changing a program executing on the second imaging device based on the dimension of the target object.

Abstract: Techniques for optimizing one or more imaging settings for a machine vision job are provided. An example method includes configuring a machine vision job by setting a plurality of banks of imaging parameters, with each of the plurality of banks of imaging parameters being different from each other; transmitting the machine vision job to an imaging device; and executing the machine vision job on the imaging device to: (a) capture an image with the imaging device operating pursuant to one of the plurality of banks of imaging parameters; (b) attempt to decode a barcode within the image; (c) responsive to successfully decoding the barcode within the image, successfully ending the barcode reader tool; and (d) responsive to unsuccessfully decoding the barcode within the image, repeating (a)-(d) with another one of the one of the plurality of banks of imaging parameters.

Abstract: Systems and methods for enhancing trainable optical character recognition (OCR) performance are disclosed herein. An example method includes receiving, at an application executing on a user computing device communicatively coupled to a machine vision camera, an image captured by the machine vision camera, the image including an indicia encoding a payload and a character string. The example method also includes identifying the indicia and the character string; decoding the indicia to determine the payload; and applying an optical character recognition (OCR) algorithm to the image to interpret the character string and identify an unrecognized character within the character string. The example method also includes comparing the payload to the character string to validate the unrecognized character as corresponding to a known character included within the payload; and responsive to validating the unrecognized character, adding the unrecognized character to a font library referenced by the OCR algorithm.

Abstract: Methods and systems for optimizing performance of a machine vision system are disclosed herein. An example method includes obtaining one or more first and second images of a target object, where each of the one or more first and second images include a pass indication and a fail indication, respectively. The example method further includes conducting, by a feasibility setup tool, a feasibility setup analysis by (i) performing machine vision techniques on each of the one or more first and second images and (ii) generating a respective updated result indication for each of the one or more first and second images. The example method further includes comparing the respective updated result indication to the respective pass indications and fail indications for the one or more first and second images, respectively; and based on the comparing, generating one or more suggestions to optimize the performance of the machine vision system.

Abstract: A controller of a barcode reader is disclosed. The controller may receive, via a user input, a charge threshold associated with presenting a battery level indicator on the display panel. The controller may monitor, during a charging operation, a state of charge of the battery. The controller may determine that the state of charge satisfies the charge threshold. The controller may cause, during the charging operation, the display panel to indicate the battery level indicator.

Abstract: Techniques for optimizing one or more imaging settings for a machine vision job are provided. An example method includes configuring a machine vision job by setting a plurality of banks of imaging parameters, with each of the plurality of banks of imaging parameters being different from each other; transmitting the machine vision job to an imaging device; and executing the machine vision job on the imaging device to: (a) capture an image with the imaging device operating pursuant to one of the plurality of banks of imaging parameters; (b) attempt to decode a barcode within the image; (c) responsive to successfully decoding the barcode within the image, successfully ending the barcode reader tool; and (d) responsive to unsuccessfully decoding the barcode within the image, repeating (a)-(d) with another one of the one of the plurality of banks of imaging parameters.

Abstract: An example method for managing power within an imaging device includes: determining an interface of the imaging device via which the imaging device is receiving power; based on the interface, determining a power budget associated with the interface; determining which fixed load components of the imaging device are enabled; calculating a remaining power budget by subtracting a power consumption of the enabled fixed load components from the power budget; responsive to the remaining power budget being positive, configuring non-fixed load components such that a total power consumption of the non-fixed load components does not exceed the remaining power budget; and responsive to the remaining power budget being negative, providing, via a user interface, a notification of insufficient power budget.

Abstract: Methods and systems for optimizing a performance of a machine vision system are disclosed herein. An example method includes capturing, by an imaging device, an image of a target object in accordance with a set of imaging device settings. The example method further includes analyzing, by one or more processors, the image in accordance with a machine vision job. The example method further includes comparing, by the one or more processors, each of (i) the image to a baseline image, (ii) the machine vision job to a baseline machine vision job, and (iii) the set of imaging device settings to a baseline set of imaging device settings. The example method further includes, based on the comparing, generating, by the one or more processors, one or more suggestions to optimize the performance of the machine vision system.

Abstract: Methods and systems for optimizing performance of a machine vision system are disclosed herein. An example method includes obtaining one or more first and second images of a target object, where each of the one or more first and second images include a pass indication and a fail indication, respectively. The example method further includes conducting, by a feasibility setup tool, a feasibility setup analysis by (i) performing machine vision techniques on each of the one or more first and second images and (ii) generating a respective updated result indication for each of the one or more first and second images. The example method further includes comparing the respective updated result indication to the respective pass indications and fail indications for the one or more first and second images, respectively; and based on the comparing, generating one or more suggestions to optimize the performance of the machine vision system.

Abstract: Methods for creating a machine vision job including barcode scanning are disclosed. An example method may be performed by one or more processors and includes detecting a first configuration of a barcode scanning tool including settings of the barcode scanning tool, wherein the first configuration, when executed by a first imaging device operating in a fixed scanning mode, causes the first imaging device to execute the barcode scanning tool in accordance with the settings. The method also includes upgrading the first configuration to a second configuration of the barcode scanning tool, wherein the second configuration, when executed by a second imaging device operating in a machine vision mode, causes the second imaging device to execute the barcode scanning tool in accordance with the settings. The method further includes displaying a user-selectable option to add the barcode scanning tool configured in accordance with the second configuration to the machine vision job.