An estimation step according to an embodiment causes a computer to execute: a calculation step of using a plurality of images obtained by a plurality of imaging devices imaging a three-dimensional space in which a plurality of objects reside, to calculate representative points of pixel regions representing the objects among pixel regions of the images; a position estimation step of estimating positions of the objects in the three-dimensional space, based on the representative points calculated by the calculation step; an extraction step of extracting predetermined feature amounts from image regions representing the objects; and an attitude estimation step of estimating attitudes of the objects in the three-dimensional space, through a preliminarily learned regression model, using the positions estimated by the position estimation step, and the feature amounts extracted by the extraction step.

An estimation step according to an embodiment causes a computer to execute: a calculation step of using a plurality of images obtained by a plurality of imaging devices imaging a three-dimensional space in which a plurality of objects reside, to calculate representative points of pixel regions representing the objects among pixel regions of the images; a position estimation step of estimating positions of the objects in the three-dimensional space, based on the representative points calculated by the calculation step; an extraction step of extracting predetermined feature amounts from image regions representing the objects; and an attitude estimation step of estimating attitudes of the objects in the three-dimensional space, through a preliminarily learned regression model, using the positions estimated by the position estimation step, and the feature amounts extracted by the extraction step.

According to one embodiment, a detection apparatus includes a processor. The processor acquires height information at a plurality of points in a subject. The processor determines whether or not one or more steps are present at a height equal to or higher than a predetermined height from a reference in the subject based on the height information. The processor detects that the subject is in a state in which a plurality of objects are overlapped, if the number of the steps is equal to or greater than a first threshold value.

According to one embodiment, a detection apparatus includes a processor. The processor acquires height information at a plurality of points in a subject. The processor determines whether or not one or more steps are present at a height equal to or higher than a predetermined height from a reference in the subject based on the height information. The processor detects that the subject is in a state in which a plurality of objects are overlapped, if the number of the steps is equal to or greater than a first threshold value.

Methods for characterizing soil stiffness of an area. One example method includes receiving, with an electronic processor, a parameter corresponding to a soil type of the area; receiving, with the electronic processor, a plurality of thermal images of the area; determining, with the electronic processor, an apparent thermal inertia of the area based on the plurality of thermal images; determining, with the electronic processor, a soil gradation of the area based on the parameter; determining, with a machine learning algorithm executed by the electronic processor, an approximate soil stiffness of the area based on the apparent thermal inertia; and outputting, to a display communicatively coupled to the electronic processor, a representation of the approximate soil stiffness.

Methods for characterizing soil stiffness of an area. One example method includes receiving, with an electronic processor, a parameter corresponding to a soil type of the area; receiving, with the electronic processor, a plurality of thermal images of the area; determining, with the electronic processor, an apparent thermal inertia of the area based on the plurality of thermal images; determining, with the electronic processor, a soil gradation of the area based on the parameter; determining, with a machine learning algorithm executed by the electronic processor, an approximate soil stiffness of the area based on the apparent thermal inertia; and outputting, to a display communicatively coupled to the electronic processor, a representation of the approximate soil stiffness.

Disclosed is a method for multi-center effect compensation based on a PET/CT intelligent diagnosis system. The method includes the following steps: estimating multi-center effect parameters of a test center B relative to a training center A by implementing a nonparametric mathematical method for data of the training center A and the test center B based on a location-scale model about additive and multiplicative multi-center effect parameters, and using the parameters to compensate the data of the test center B to eliminate a multi-center effect between the test center B and the training center A. According to the present disclosure, the multi-center effect between the training center A and the test center B can be compensated, so that the compensated data of the test center B can be used in the model trained by the training center A, and the generalization ability of the model is indirectly improved.

Disclosed is a method for multi-center effect compensation based on a PET/CT intelligent diagnosis system. The method includes the following steps: estimating multi-center effect parameters of a test center B relative to a training center A by implementing a nonparametric mathematical method for data of the training center A and the test center B based on a location-scale model about additive and multiplicative multi-center effect parameters, and using the parameters to compensate the data of the test center B to eliminate a multi-center effect between the test center B and the training center A. According to the present disclosure, the multi-center effect between the training center A and the test center B can be compensated, so that the compensated data of the test center B can be used in the model trained by the training center A, and the generalization ability of the model is indirectly improved.

Provided herein are systems and methods for estimating contact pressure of a human lying on a surface including one or more imaging devices having imaging sensors oriented toward the surface, a processor and memory, including a trained model for estimating human contact pressure trained with a dataset including a plurality of human lying poses including images generated from at least one of a plurality of imaging modalities including at least one of a red-green-blue modality, a long wavelength infrared modality, a depth modality, or a pressure map modality, wherein the processor can receive one or more images from the imaging devices of the human lying on the surface and a source of one or more physical parameters of the human to determine a pressure map of the human based on the one or more images and the one or more physical parameters.

A control method for a computer to execute a process includes receiving a plurality of pieces of captured data of a person; generating weight information that indicates a weight applied to each of the plurality of pieces of captured data based on quality of each of the plurality of pieces of captured data and the number of the plurality of pieces of captured data; and applying, when representative data that represents the plurality of pieces of captured data is acquired from the plurality of pieces of captured data, an algorithm in which the smaller the weight indicated by the generated weight information, the smaller an influence of each of the plurality of pieces of captured data on a calculation result of the representative data.