A method for automatically creating an equipment inventory from a plurality of images of a site. The method includes receiving, by a processor, the plurality of images of the site, labeling, by the processor, equipment present in each image of the plurality of images of the site, creating, by the processor, an overlap curve for neighboring images of the plurality of images of the site, determining, by the processor, a useful image of each side of the site based on the overlap curve, the useful image including a subset of the plurality of images, creating, by the processor, an overlay of equipment in the useful image of each side of the site, counting, by the processor, equipment present in the overlay of each side of the site, and generating, by the processor, the equipment inventory by adding a number of equipment counted in the overlay of each side of the site.

A method for automatically creating an equipment inventory from a plurality of images of a site. The method includes receiving, by a processor, the plurality of images of the site, labeling, by the processor, equipment present in each image of the plurality of images of the site, creating, by the processor, an overlap curve for neighboring images of the plurality of images of the site, determining, by the processor, a useful image of each side of the site based on the overlap curve, the useful image including a subset of the plurality of images, creating, by the processor, an overlay of equipment in the useful image of each side of the site, counting, by the processor, equipment present in the overlay of each side of the site, and generating, by the processor, the equipment inventory by adding a number of equipment counted in the overlay of each side of the site.

Implementations are described herein for localizing individual plants by aligning high-elevation images using invariant anchor points while disregarding variant feature points, such as deformable plants. High-elevation images that capture the plurality of plants at a resolution at which wind-triggered deformation of individual plants is perceptible between the high-elevation images may be obtained. First regions of the high-elevation images that depict the plurality of plants may be classified as variant features that are unusable as invariant anchor points. Second regions of the high-elevation images that are disjoint from the first set of regions may be classified as invariant anchor points. The high-elevation images may be aligned based on invariant anchor point(s) that are common among at least some of the high-elevation images. Based on the aligned high-elevation images, individual plant(s) may be localized within one of the high-elevation images for performance of one or more agricultural tasks.

It is judged that a relevant tool is lost at a second time point after the elapse of a certain amount of time or longer after a first time point when it is judged that the tool is not recognized at all in images; and regarding the tool which is judged to have been lost, trace data corresponding to heads-up time, which is immediately before the first time point to immediately after the first time point, is read with reference to a corresponding identifier and a movement locus of the tool based on the trace data is displayed on a screen.

It is judged that a relevant tool is lost at a second time point after the elapse of a certain amount of time or longer after a first time point when it is judged that the tool is not recognized at all in images; and regarding the tool which is judged to have been lost, trace data corresponding to heads-up time, which is immediately before the first time point to immediately after the first time point, is read with reference to a corresponding identifier and a movement locus of the tool based on the trace data is displayed on a screen.

Computer apparatuses, methods, and non-transitory computer-readable recording media having recorded thereon a program executed in a computer apparatus to perform functions of image processing are provided. An example non-transitory recording medium comprising a program executed in a computer apparatus causes the computer apparatus to perform functions comprising: identifying two or more subjects in a first image from an imaging device; and if the identified two or more subjects satisfy a predetermined condition, superimposing a predetermined effect either on the first image or on a second image from the imaging device that is different from the first image to generate a third image, and providing the third image.

Systems and methods are provided for a surgical needle counting device for an operating room. An example system includes a collecting enclosure and a counting apparatus having a sensor configured for determining when a needle is dropped into the collecting enclosure. The counting apparatus is configured to maintain a count of needles introduced into a surgical field associated with the operating room and a count of needles accounted for in the counting apparatus.

A method of training a model of a diagnostic apparatus includes providing one or more first tube assemblies of a first type and one or more second tube assemblies of a second type in a diagnostic apparatus; capturing one or more first images of at least a portion of each of the one or more first tube assemblies and the second tube assemblies using the imaging device. Training the model includes identifying tube assemblies of the first type and tube assemblies of the second type based on the one or more first images and the one or more second images. Tubes assemblies of the first type are grouped into a first group and tube assemblies of the second type are grouped into a second group. Other methods and apparatus are disclosed.

A method of training a model of a diagnostic apparatus includes providing one or more first tube assemblies of a first type and one or more second tube assemblies of a second type in a diagnostic apparatus; capturing one or more first images of at least a portion of each of the one or more first tube assemblies and the second tube assemblies using the imaging device. Training the model includes identifying tube assemblies of the first type and tube assemblies of the second type based on the one or more first images and the one or more second images. Tubes assemblies of the first type are grouped into a first group and tube assemblies of the second type are grouped into a second group. Other methods and apparatus are disclosed.

There is provided a method of training a machine learning model, comprising: extracting faces from first images, creating an age training dataset comprising records each including a face and a ground truth label indicating whether the face is below a legal age, training an age component on the age training dataset for generating a first outcome indicative of a target face of the target image being below the legal age, creating a sexuality training dataset comprising second records each including a second image and ground truth label indicative of sexuality, training a sexuality component on the sexuality training dataset for generating a second outcome indicative of sexuality depicted in the target image, defining a combination component that receives an input of a combination of the first outcome and the second outcome, and generates a third outcome indicative of child sexual abusive materials (CSAM) depicted in the target image.