An example wear detection system receives a plurality of images from a plurality of sensors associated with a work machine. Individual sensors of the plurality of sensors have respective fields-of-view different from other sensors of the plurality of sensors. The wear detection system identifies a first region of interest and second region of interest associated with the at least one GET. The wear detection system determines a first set of image points and a second set of images points for the at least one GET based on geometric parameters associated with the GET. The wear detection system determines a wear level or loss for the at least one GET based on the GET measurement.

A system and method for monitoring performance of an industrial process includes an input port for receiving signals representative of one or more performance parameters generated by the industrial process, a user interface including a display and a controller that is operably coupled with the input port and the user interface. The controller is configured to repeatedly receive signals over time via the input port representative of the one or more performance parameters of the industrial process and to generate a plurality of snapshots, wherein each snapshot includes a graphical representation of the one or more performance parameters of the industrial process at a corresponding time. The controller is configured to generate an animatable heat map including two or more of the plurality of snapshots arranged temporally and to display the animatable heat map on the display.

Systems and methods disclosed herein use a first machine learning architecture and a second machine learning architecture where the first machine learning architecture executes on a first processor and receives a first image representing a mouth of a user, determines user feedback for outputting to the user based on a first machine learning model, and outputs the user feedback for capturing a second image representing the mouth of the user. The second machine learning architecture executes on a second processor and receives the first image and the second image, and generates a 3D model of at least a portion of a dental arch of the user based on the first image and the second image where the 3D model is generated based on a second machine learning model of the second machine learning architecture.

Provided is an image and an inspection system that make it possible to effectively utilize a result of past inspection. An audit position indicating a position where an audit is planned or a position where an audit is completed and a past audit position indicating a position where an audit was performed in the past within a predetermined range including the audit position are displayed over a map on a user terminal, the map being a drawing representing a configuration of a manufacturing site. The inspector references the map on the user terminal and effectively utilizes a past audit result to perform an audit.

A method for acquiring at least one two-dimensional image of a part of arches of a patient includes steps carried out by the patient or other person who is not a dental health professional, for example, including placing a dental separator in the mouth of the patient in order to separate the lips of the patient and improve the visibility of the teeth during the acquisition of said at least one two-dimensional image, and acquiring, in a mouth closed position and with a personal image acquisition apparatus, said at least one two-dimensional image.

A fish-quality determination system (1) analyzes, through machine learning, a relationship among image data taken of cross-sections of tails of fish, boat data indicating fishing boats that caught the fish, and quality data indicating quality of the fish. When having acquired, from a user device (3), image data of a cross-section of the tail of a fish subject to determination and boat data indicating a fishing boat that caught the fish, the system (1) uses, as an input, the image data of the cross-section of the tail of the fish subject to the determination and the boat data indicating the fishing boat that caught the fish that have been acquired so as to estimate and output quality of the fish subject to the determination on the basis of the analyzed relationship. The output quality of the fish subject to the determination is displayed on the user device (3).

A computer device obtains a medical image set. The device identifies a difference between the reference medical image and the target medical image to obtain a candidate non-lesion region in the target medical image. The device determines area size information of the candidate non-lesion region as candidate area size information. The device adjusts the candidate non-lesion region according to the annotated area size information when the candidate area size information does not match the annotated area size information, so as to obtain a target non-lesion region in the target medical image.

A defect inspecting system includes a detector configured to image a sample and a host control device that acquires an inspection image including a defect and a plurality of reference images not including a defect site and generates a pseudo defect image by editing a predetermined reference image among the plurality of acquired reference images. An initial parameter is determined with which the pseudo defect site is detectable from the pseudo defect image. The host control device acquires a defect candidate site from the inspection image using the initial parameter, estimates a high-quality image from an image of a site corresponding to the defect candidate site using the parameter acquired in image quality enhancement, and specifies an actual defect site in the inspection image by executing defect discrimination. A parameter is determined with which a site close to the specified actual defect site is detectable using the inspection image.

The invention relates to the field of computer engineering for processing images that provides increased accuracy of finding and classifying a similar object . The technical result is achieved by: downloading files of a radiographic image which comprise metadata including information about the object or subject of the image and information about the image itself; encrypting the downloaded files if the above-mentioned files comprise personal data about a person; decrypting the above-mentioned, encrypted, downloaded files; and processing the radiographic image, wherein, as a result of the processing, the following occurs: finding and capturing a relevant region of the radiographic image; removing noise from the captured, relevant region of the radiographic image, wherein a region with a found object is meant by a relevant region of the radiographic image; compressing or unzipping a previously processed radiographic image; and finding a similar object in two previously processed images, and processing said object.

An image processing system includes a processor, the processor performing processing, based on association information of an association between a biological image captured under a first imaging condition and a biological image captured under a second imaging condition, of outputting a prediction image corresponding to an image in which an object captured in an input image is to be captured under the second imaging condition. The association information is indicative of a trained model obtained through machine learning of a relationship between a first training image captured under the first imaging condition and a second training image captured under the second imaging condition. The processor is capable of outputting a plurality of different kinds of prediction images based on a plurality of trained models and the input image, and performs processing, based on a given condition, of selecting the prediction image to be output among a plurality of prediction images.