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.

A non-transitory computer-readable recording medium having stored therein an analysis program causing a computer to execute processing includes: generating a regression function including a plurality of explanatory variables and an objective variable based on regression analysis using observed values of a plurality of physical elements each related to motion of a moving object; dividing an explanatory variable space containing the plurality of explanatory variables into a plurality of regions by using the regression function; and generating contribution information on a contribution of each of the plurality of explanatory variables to the objective variable for each of the plurality of regions.

An onboard aircraft landing system includes one or more event-based cameras disposed at known locations to capture the runway and visible surrounding features such as lights and runway markings. The event-based cameras produce a continuous stream of event data that may be quickly processed to identify both light and dark features contemporaneously, and calculate an aircraft pose relative to the runway based on the identified features and the known locations of the event-based cameras. Composite features are identified via the relative location of individual features corresponding to pixel events.

An image processing system analyzes each of a set of vehicle images to determine one or more vehicle segments present in the images. The image processing system uses a characterization engine in the form of a neural network based image model to process each of the pixels of each of the images to determine the particular pixels of the image (or of the object depicted within the image) within or associated with a particular vehicle segment, such as a vehicle door, bumper, right or left panel, rear panel, windshield, etc. The characterization engine or image model may be developed or trained using a training engine that analyzes a plurality of images of different vehicles damaged in various different manners which have been annotated, on a pixel by pixel basis, to indicate the segments of the vehicle to which the various pixels belong or depict, and which have also been annotated, on an image basis, to indicate the view and/or zoom level of the image.

An image processing system analyzes each of a set of vehicle images to determine one or more vehicle segments present in the images. The image processing system uses a characterization engine in the form of a neural network based image model to process each of the pixels of each of the images to determine the particular pixels of the image (or of the object depicted within the image) within or associated with a particular vehicle segment, such as a vehicle door, bumper, right or left panel, rear panel, windshield, etc. The characterization engine or image model may be developed or trained using a training engine that analyzes a plurality of images of different vehicles damaged in various different manners which have been annotated, on a pixel by pixel basis, to indicate the segments of the vehicle to which the various pixels belong or depict, and which have also been annotated, on an image basis, to indicate the view and/or zoom level of the image.

Systems and methods for improved vision diagnostics are disclosed. Some embodiments relate to machine learning models for the analysis of vision diagnostics data. Some embodiments relate to improved, robust regression methods that can be used with vision diagnostics data to detect trends that may warrant medical intervention.

Systems and methods for improved vision diagnostics are disclosed. Some embodiments relate to machine learning models for the analysis of vision diagnostics data. Some embodiments relate to improved, robust regression methods that can be used with vision diagnostics data to detect trends that may warrant medical intervention.

The current subject matter can include non-invasive systems and methods for detecting hemoglobin. In one implementation, a method includes receiving data characterizing a plurality of images associated with palpebral conjunctiva region of one or more patients. The method also includes receiving data indicative of a user selection of a first pixel of a first image of the plurality of images. The method further includes determining, a region of interest associated with the selected first pixel. The determining can include identifying a plurality of pixels adjacent to the first pixel having color parameter values within a predetermined range of a first color parameter value of the first pixel. The method also includes determining a first plurality of parameters associated with the region of interest, and providing the first plurality of parameters.

The current subject matter can include non-invasive systems and methods for detecting hemoglobin. In one implementation, a method includes receiving data characterizing a plurality of images associated with palpebral conjunctiva region of one or more patients. The method also includes receiving data indicative of a user selection of a first pixel of a first image of the plurality of images. The method further includes determining, a region of interest associated with the selected first pixel. The determining can include identifying a plurality of pixels adjacent to the first pixel having color parameter values within a predetermined range of a first color parameter value of the first pixel. The method also includes determining a first plurality of parameters associated with the region of interest, and providing the first plurality of parameters.

A method comprising receiving data associated with a business, the data comprising first values for first attributes; processing the data, in accordance with a common data attribute schema that indicates second attributes, to generate second values for at least some of the second attributes including a group of attributes, the second values including a group of attribute values for the group of attributes; identifying, using the common data attribute schema and from among pre-existing software codes, software code implementing an ML data processing pipeline configured to generate a group of feature values; processing the group of attribute values with the software code to obtain the group of feature values; and either providing the group of feature values as inputs to a machine learning (ML) model for generating corresponding ML model outputs, or using the group of feature values to train the ML model.