A method for operating an electronic device is provided. The method comprises displaying, on a display, a content including at least one area where a graphic object may be placed, obtaining at least one image, identifying a line drawing and at least one marker, based on the at least one image, identifying a first graphic object corresponding to the identified line drawing and the at least one identified marker among a plurality of pre-stored graphic objects, and displaying the first graphic object on a first area of the content corresponding to a position of at least one of the line drawing or the at least one marker.

A method includes generating first contrast significance data for a first computer vision model generated from a first training set of medical scans. First significant contrast parameters are identified based on the first contrast significance data. A first re-contrasted training set is generated based on performing a first intensity transformation function on the first training set of medical scans, where the first intensity transformation function utilizes the first significant contrast parameters. A first re-trained model is generated from the first re-contrasted training set, which is associated with corresponding output labels based on abnormality data for the first training set of medical scans. Re-contrasted image data of a new medical scan is generated based on performing the first intensity transformation function. Inference data indicating at least one abnormality detected in the new medical scan is generated based on utilizing the first re-trained model on the re-contrasted image data.

Methods and systems for facilitating a scavenger hunt. The systems and methods described herein involve receiving at an interface a list of a plurality of attractions, and communicating the list of the plurality of attractions to at least one device associated with a participant over a network. Scavenger hunt participants may then gather imagery of the required attractions. The systems and methods described herein then involve receiving imagery from the at least one participant and executing at least one computer vision procedure to determine whether the received imagery includes at least one of the plurality of attractions.

Methods and systems for milling and imaging a sample based on multiple fiducials at different sample depths include forming a first fiducial on a first sample surface at a first sample depth; milling at least a portion of the sample surface to expose a second sample surface at a second sample depth; forming a second fiducial on the second sample surface; and milling at least a portion of the second sample surface to expose a third sample surface including a region of interest (ROI) at a third sample depth. The location of the ROI at the third sample depth relative to the first fiducial may be calculated based on an image of the ROI and the second fiducial as well as relative position between the first fiducial and the second fiducial.

A method for determining a location of a vehicle including one or more image sensors in a manufacturing environment includes determining, when a key cycle transition condition of the vehicle and a vehicle gear transition condition of the vehicle are satisfied, a location parameter of the vehicle based on an image including a location tag and a previous image obtained from the one or more image sensors. The method includes determining a vehicle time period based on the image and the previous image. The method includes validating a manufacturing routine of the vehicle when the location parameter satisfies a location condition and the vehicle time period satisfies a time condition.

Computing devices and methods for reading gauges are disclosed. A gauge reading method includes capturing image data corresponding to a captured image of one or more gauges, detecting one or more gauges in the captured image, cropping a detected gauge in the captured image to provide a use image including the detected gauge, and classifying the detected gauge to correlate the detected gauge with a template image. The gauge reading method also includes attempting to perform feature detection rectification on the use image to produce a rectified image of the detected gauge, performing template matching rectification on the use image to produce the rectified image responsive to a failure to perform the feature detection rectification, and estimating a gauge reading responsive to the rectified image. A computing device may implement at least a portion of a gauge reading method.

An image identification method is applied to an image surveillance apparatus and includes utilizing an object detecting function to generate a first bounding box and a second bounding box within a surveillance image, utilizing a foreground detecting function to generate a plurality of markers within the surveillance image, defining some of the plurality of markers which conform to the first bounding box as a first marker group and further defining other markers which do not conform to the first bounding box as a second marker group, determining whether the second marker group conforms to the second bounding box, and deciding whether the second bounding box belongs to an error of the object detecting function in accordance with a determination result.

The medical image processing device includes a processor, in which the processor acquires a medical image obtained by imaging a subject with an endoscope, identifies a tumor region and a non-tumor region from the medical image, generates a demarcation line that is a boundary between the tumor region and the non-tumor region, generates a virtual incision line at a position separated from the demarcation line by a designated distance, and performs control for superimposing the demarcation line and the virtual incision line on the medical image to be displayed.

A system for updating a training dataset of an item identification model determines that an item is not included in a training dataset. In response to determining that the item is not included in the training dataset, the system obtains an identifier of the item. The system detects a triggering event at a platform, where the triggering event corresponds to a user placing the item on a platform. The system captures images of the item. The system extracts a set of features associated with the item from the images. The system associates the item to the identifier and the set of features. The system adds a new entry to the training dataset, where the new entry represents the item labeled with the identifier and the set of features.

A system for identifying items based on aggregated metadata obtains images of an item. The system extracts a set of features from images of the item. The system identifies a first value of a first feature associated with a first image of the item. The system identifies a second value of the first feature associated with a second image of the item. The system aggregates the first value and the second value. The system associates the item to the aggregated first value and the second value, where the aggregated first value and the second value represent the first feature of the item. The system adds a new entry for each image of the item to a training dataset associated with an item identification model.