US-20260129280-A1 - SYSTEMS AND METHODS FOR CONFIGURING AN IMAGING TOOL USING GENERATIVE AI

US20260129280A1US 20260129280 A1US20260129280 A1US 20260129280A1US-20260129280-A1

Abstract

Systems and methods for controlling an imaging tool associated with performing an imaging task are disclosed. An example method may include a model receiving image data comprising at least one image including text, and control data associated with the imaging tool that indicates a description of the imaging task, a plurality of settings of the imaging tool associated with performing the imaging task, and a schema of tool configuration data that configures the plurality of settings. The method may include the model generating tool configuration data for the imaging tool that indicates one or more values corresponding to at least a portion of the plurality of settings. The method may include configuring the imaging tool using the tool configuration data.

Inventors

Matthew M. Degen

Assignees

ZEBRA TECHNOLOGIES CORPORATION

Dates

Publication Date: 20260507
Application Date: 20241101

Claims (20)

1 . A method for configuring an imaging tool associated with performing an imaging task, the method comprising: receiving, at a model: image data comprising at least one image, wherein the at least one image includes text, and control data associated with the imaging tool, wherein the control data indicates: a description of the imaging task, a plurality of settings of the imaging tool associated with performing the imaging task, and a schema of tool configuration data that configures the plurality of settings; generating, via the model, the tool configuration data for the imaging tool, wherein the tool configuration data indicates one or more values corresponding to at least a portion of the plurality of settings; and configuring the imaging tool using the tool configuration data.
2 . The method of claim 1 , wherein generating the tool configuration data comprises: eliminating at least one setting of the plurality of settings being configured by the tool configuration data based upon the image data; and/or limiting a range of values of the one or more values corresponding to at least the portion of the plurality of settings based upon the image data.
3 . The method of claim 1 , further comprising: receiving an indication of the imaging tool, of a plurality of imaging tools, wherein receiving at least the control data at the model is responsive to receiving the indication of the imaging tool.
4 . The method of claim 1 , wherein the tool configuration data includes a JSON file.
5 . The method of claim 1 , wherein the plurality of settings are associated with performing optical character recognition on an image.
6 . The method of claim 1 , wherein the plurality of settings include one or more of: a confidence metric, an average character height, a color of the text, a contrast threshold, a character width, a character range, a region of interest, a string match, or text optimization.
7 . The method of claim 1 , further comprising a base model configured to generate a plurality of configuration datasets corresponding to a plurality of tools, wherein: the plurality of configuration datasets includes the tool configuration data, the plurality of tools include the imaging tool, fine-tuning of the base model generates the model, the fine-tuning configures the model to generate the tool configuration data for the imaging tool, and the fine-tuning causes the model to have better performance generating the tool configuration data for the imaging tool respective to performance of the base model generating the tool configuration data for the imaging tool.
8 . The method of claim 1 , wherein the model includes one or more of a neural network, a generative model, or a language model.
9 . The method of claim 1 , wherein the control data includes one or more prompts configured for the model.
10 . The method of claim 1 , wherein the model is configured to determine a region of interest (ROI) and/or generate the tool configuration data is based upon the ROI in the at least one image.
11 . The method of claim 1 , further comprising: performing the imaging task on a set of test images comprising test image data using the imaging tool configured with the tool configuration data; determining whether performance of the imaging task achieves one or more metrics associated with the imaging task; responsive to achieving the one or more metrics, generating imager configuration data for configurating operational parameters of an imaging device, the imager configuration data based upon the tool configuration data, and providing the imager configuration data to the imaging device; and responsive to not achieving the one or more metrics, modifying the tool configuration data via the model to improve the performance of the imaging task on the set of test images using the imaging tool.
12 . The method of claim 11 , wherein the operational parameters are associated with one or more of: an exposure, a focal distance, a spatial resolution, an aperture, a shutter speed, a sensor gain, an image processing, an illumination, or a decoder.
13 . The method of claim 11 , wherein the model iteratively modifies the tool configuration data until the one or more metrics are achieved by the imaging tool.
14 . A system for configuring an imaging tool associated with performing an imaging task, the system comprising: a model stored on one or more memories; one or more processors; and the one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the system to: receive, at the model: image data comprising at least one image, wherein the at least one image includes text, and control data associated with the imaging tool, wherein the control data indicates: a description of the imaging task, a plurality of settings of the imaging tool associated with performing the imaging task, and a schema of tool configuration data that configures the plurality of settings, generate, via the model, the tool configuration data for the imaging tool, wherein the tool configuration data indicates one or more values corresponding to at least a portion of the plurality of settings, and configure the imaging tool using the tool configuration data.
15 . The system of claim 14 , wherein to generate the tool configuration data further comprises instructions that, when executed, cause the system to: eliminate at least one setting of the plurality of settings being configured by the tool configuration data based upon the image data; and/or limit a range of values of the one or more values corresponding to at least the portion of the plurality of settings based upon the image data.
16 . The system of claim 14 , wherein the plurality of settings are associated with performing optical character recognition on an image, and/or include one or more of: a confidence metric, an average character height, a color of the text, a contrast threshold, a character width, a character range, a region of interest, a string match, or text optimization.
17 . The system of claim 14 , further comprising a base model configured to generate a plurality of configuration datasets corresponding to a plurality of tools, wherein: the plurality of configuration datasets includes the tool configuration data, the plurality of tools include the imaging tool, fine-tuning of the base model generates the model, the fine-tuning configures the model to generate the tool configuration data for the imaging tool, and the fine-tuning causes the model to have better performance generating the tool configuration data for the imaging tool respective to performance of the base model generating the tool configuration data for the imaging tool.
18 . The system of claim 14 , further comprising instructions that, when executed, cause the system to: perform the imaging task on a set of test images comprising test image data using the imaging tool configured with the tool configuration data; determine whether performance of the imaging task achieves one or more metrics associated with the imaging task; responsive to achieving the one or more metrics, generate imager configuration data for configurating operational parameters of an imaging device, the imager configuration data based upon the tool configuration data, and provide the imager configuration data to the imaging device; and responsive to not achieving the one or more metrics, modify the tool configuration data via the model to improve the performance of the imaging task on the set of test images using the imaging tool.
19 . The system of claim 18 , wherein the operational parameters are associated with one or more of: an exposure, a focal distance, a spatial resolution, an aperture, a shutter speed, a sensor gain, an image processing, an illumination, or a decoder.
20 . A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to: receive, at a model: image data comprising at least one image, wherein the at least one image includes text, and control data associated with an imaging tool, wherein the control data indicates: a description of an imaging task, a plurality of settings of the imaging tool associated with performing the imaging task, and a schema of tool configuration data that configures the plurality of settings; generate, via the model, the tool configuration data for the imaging tool, wherein the tool configuration data indicates one or more values corresponding to at least a portion of the plurality of settings; and configure the imaging tool using the tool configuration data.

Description

BACKGROUND The background description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventor, to the extent it is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure. An application for performing an imaging task on an image, such as a machine vision tool for optical character recognition or barcode decoding, can have many settings which configure the application for performing the imaging task. Some examples of setting may include determining a region of interest of the image to analyze for performing the particular imaging task, the average character height of text to be recognized in an image, the contrast of text or other symbology in the image, the color of the text to be identified, etc. The values of the application settings are not always self-evident or easily determined, causing difficulty for the application user while configuring the setting and/or resulting in settings that may not be appropriate for a given subject image and/or imaging task. The trial and error process to arrive at suitable application settings can be frustrating and time consuming for the user, and also unnecessarily expends computing resources (e.g., processing cycles, memory, power, etc.) while testing various applications settings on subject images. Thus, there exists an opportunity for configuring an imaging tool generative artificial intelligence. BRIEF SUMMARY In one aspect, a method for configuring an imaging tool associated with performing an imaging task may include: receiving, at a model image data comprising at least one image, wherein the at least one image includes text, and control data associated with the imaging tool, wherein the control data indicates: a description of the imaging task, a plurality of settings of the imaging tool associated with performing the imaging task, and a schema of tool configuration data that configures the plurality of settings; generating, via the model, the tool configuration data for the imaging tool, wherein the tool configuration data indicates one or more values corresponding to at least a portion of the plurality of settings; and configuring the imaging tool using the tool configuration data. In a variation of the aspect, generating the tool configuration data may include eliminating at least one setting of the plurality of settings being configured by the tool configuration data based upon the image data; and/or limiting a range of values of the one or more values corresponding to at least the portion of the plurality of settings based upon the image data. In another variation of the aspect, the method may include receiving an indication of the imaging tool, of a plurality of imaging tools, wherein receiving at least the control data at the model is responsive to receiving the indication of the imaging tool. In yet another variation of the aspect, the tool configuration data may include a JSON file. In still yet another variation of the aspect, the plurality of settings may be associated with performing optical character recognition on an image. In a variation of the aspect, the plurality of settings may include one or more of: a confidence metric, an average character height, a color of the text, a contrast threshold, a character width, a character range, a region of interest, a string match, or text optimization. In another variation of the aspect, the method may include a base model configured to generate a plurality of configuration datasets corresponding to a plurality of tools, wherein: the plurality of configuration datasets includes the tool configuration data, the plurality of tools include the imaging tool, fine-tuning of the base model generates the model, the fine-tuning configures the model to generate the tool configuration data for the imaging tool, and the fine-tuning causes the model to have better performance generating the tool configuration data for the imaging tool respective to performance of the base model generating the tool configuration data for the imaging tool. In yet another variation of the aspect, the model may include one or more of a neural network, a generative model, or a language model. In still yet another variation of the aspect, the control data may include one or more prompts configured for the model. In a variation of the aspect, the model may be configured to determine a region of interest (ROI) and/or generate the tool configuration data is based upon the ROI in the at least one image. In another variation of the aspect, the method may include performing the imaging task on a set of test images comprising test image data using the imaging tool configured with the tool configuration data; determining whether performance of the imaging task achieves one or more metrics associated with th