Abstract: Imaging devices, systems, and methods for analyzing an image taken by an imaging system are described herein. An example method includes: receiving a bank image from the imaging device pursuant to one of a plurality of banks of imaging parameters; rendering the bank image in an image display; receiving an indication of a region of interest (ROI) in the image display associated with a user-selected tool; determining whether the user-selected tool is set to use the bank image, an output of a parent tool, or an output of the user-selected tool; and generating, in the image display, at least a portion of a display image in the ROI representative of: (i) the bank image, (ii) the output of the parent tool, or (iii) the output of the user-selected tool; wherein the portion of the display image is associated with the user-selected region.

Abstract: Example systems and methods for interacting with overlapping regions of interest (ROIs) in machine vision applications are disclosed. An example system includes a machine vision camera, and a client computing device coupled to the machine vision camera. The client computing device configured to: receive an image from the machine vision camera; present the image on a canvas, wherein the canvas is part of a first user interface of a machine vision application displayed on the client computing device; present a first ROI associated with a first machine vision tool on the canvas; present a second ROI associated with a second machine vision tool on the canvas; and target the first ROI or the second ROI as a targeted ROI for interaction based upon distances from a pointer displayed on the canvas to features of the first ROI and the second ROI.

Abstract: Machine vision systems and methods for automatically generating machine vision job(s) based on region of interests (ROIs) of digital images are disclosed herein. The systems and methods comprise configuring a machine vision tool for capturing an image ID depicted in training images and labeling each training image to indicate a success or failure status of an object depicted by the training images. Candidate image feature(s) are extracted from the training images for generation of candidate ROI(s). A training set of ROIs are selected from the candidate ROI(s) and are designated as an included or excluded ROIs. The training set of ROIs and the training images are used to train a vision learning model configured to output a machine vision job deployable to an imaging device that implements the machine vision job to detect the success or failure statuses of additional image(s) depicting the object.

Abstract: Machine vision systems and methods for automatically generating machine vision job(s) based on region of interests (ROls) of digital images are disclosed herein. The systems and methods comprise configuring a machine vision tool for capturing an image ID depicted in training images and labeling each training image to indicate a success or failure status of an object depicted by the training images. Candidate image feature(s) are extracted from the training images for generation of candidate ROI(s). A training set of ROls are selected from the candidate ROI(s) and are designated as an included or excluded ROls. The training set of ROls and the training images are used to train a vision learning model configured to output a machine vision job deployable to an imaging device that implements the machine vision job to detect the success or failure statuses of additional image(s) depicting the object.

Abstract: Example systems and methods for interacting with overlapping regions of interest (ROls) in machine vision applications are disclosed. An example system includes a machine vision camera, and a client computing device coupled to the machine vision camera. The client computing device configured to: receive an image from the machine vision camera; present the image on a canvas, wherein the canvas is part of a first user interface of a machine vision application displayed on the client computing device; present a first ROI associated with a first machine vision tool on the canvas; present a second ROI associated with a second machine vision tool on the canvas; and target the first ROI or the second ROI as a targeted ROI for interaction based upon distances from a pointer displayed on the canvas to features of the first ROI and the second ROI.