Systems and techniques for multispectral lighting reproduction, in one aspect, include: one or more light sources having different lighting spectra; and one or more computers comprising at least one processor and at least one memory device, the one or more computers programmed to drive the one or more light sources directly using intensity coefficients that have been determined by comparing first data for a multi-color reference object photographed by a camera in a scene with second data for the multi-color reference object photographed when lit by respective ones of the different lighting spectra of the one or more light sources.

A tentative local score between a point in a feature image in a template image and a point, in a target object image, at a position corresponding to the point in the feature image is calculated, and a determination is performed as to whether the tentative local score is smaller than 0. In a case where the tentative local score is greater than or equal to 0, the tentative local score is employed as a local score. In a case where the tentative local score is smaller than 0, the tentative local score is multiplied by a coefficient and the result is employed as a degree of local similarity.

An electronic device and method for human skin detection based on a human body-prior is provided. A color image of a person is acquired, and a 3D body model of the person is estimated based on the color image. One or more unclothed parts of the 3D body model are identified. The one or more unclothed parts correspond to one or more body parts, of which at least a portion of skin remains uncovered by clothes. From the color image, pixel information corresponding to the one or more unclothed parts is extracted and classification information is determined based on the pixel information. The classification information includes a set of values, each indicating a likelihood of whether a corresponding pixel of the color image is part of an unclothed skin portion of the person. The unclothed skin portion is detected in the color image based on the classification information.

Systems for identifying threat materials such as CBRNE threats and locations are provided. The systems can include a data acquisition component configured to determine the presence of a CBRNE threat; data storage media; and processing circuitry operatively coupled to the data acquisition device and the storage media. Methods for identifying a CBRNE threat are provided. The methods can include: determining the presence of a CBRNE threat using a data acquisition component; and acquiring an image while determining the presence of the CBRNE threat. Methods for augmenting a real-time display to include the location and/or type of CBRNE threat previously identified are also provided. Methods for identifying and responding to CBRNE threats are provided as well.

To efficiently and effectively display a plurality of pieces of information that can be acquired from measurement data. Measurement data on a spaced amount from a measurement reference plane at each point of a road measured for a predetermined road width along a measurement path of a road to be measured and road image data including the road acquired along the measurement path are acquired; a unit area to become a unit of processing is set based on the measurement data; a model plane in the unit area is set from the measurement data in the unit area; point group data indicating a spaced amount from the model plane at each point of the unit area is generated; spaced amount display data displaying shading or hue corresponding to a spaced amount specified with the point group data in a contour of the road is generated; and a road image based on road image data corresponding to a specified area of the road and a spaced amount image based on the spaced amount display data corresponding to the road image are simultaneously displayed.

A control method for a robot cleaner includes acquiring a plurality of images of surroundings during travel of the robot cleaner in a cleaning area, estimating a plurality of room-specific feature distributions according to a rule defined for each of a plurality of rooms, based on the images acquired while acquiring the plurality of images, acquiring an image of surroundings at a current position of the robot cleaner, obtaining a comparison reference group including a plurality of room feature distributions by applying the rule for each of the plurality of rooms to the image acquired while acquiring the image at the current position, comparing the obtained comparison reference group with the estimated room-specific feature distributions, and determining a room from the plurality of rooms having the robot cleaner currently located therein.

A method of creating a diagnostic evaluation for usual interstitial pneumonia is provided, including obtaining a first plurality of series of HRCT lung slices indicating the presence of UIP, obtaining an identification of UIP and non-UIP voxels, extracting textural and localization features from the UIP and non-UIP voxels, selecting features that are more accurate in differentiating UIP voxels from non-UIP voxels than other features are, eliminating features highly correlated with a more accurate feature, and constructing a predictive model by performing a second classifier to provide a probability that a voxel signifies the presence of UIP. Also provided is a method of identifying UIP in a subject's lung by applying a diagnostic evaluation for UIP that was created with the foregoing method.

One of the aspects of the present invention discloses an image processing apparatus. The apparatus comprises: a unit configured to acquire a current image and a background image; a unit configured to detect a foreground object from the current image according to the background image; a unit configured to determine a first parameter according to luminance information of the foreground object in the current image and luminance information of portion corresponding to the foreground object in the background image, wherein the first parameter represents change degree of luminance direction of the portion corresponding to the foreground object from the background image to the foreground image; and a unit configured to identify whether the foreground object is a false foreground object or not according to the first parameter. According to the present invention, the accuracy of identifying the false foreground objects will be improved.

A machine learning (ML) task system trains a neural network model that learns a compressed representation of acquired data and performs a ML task using the compressed representation. The neural network model is trained to generate a compressed representation that balances the objectives of achieving a target codelength and achieving a high accuracy of the output of the performed ML task. During deployment, an encoder portion and a task portion of the neural network model are separately deployed. A first system acquires data, applies the encoder portion to generate a compressed representation, performs an encoding process to generate compressed codes, and transmits the compressed codes. A second system regenerates the compressed representation from the compressed codes and applies the task model to determine the output of a ML task.

An image processing device converts an image that is a recognition object image to high-resolution, medium-resolution, and low-resolution images. The device sets the pixel of interest of the high-resolution image, and votes the co-occurrence in a gradient direction with offset pixels, the co-occurrence in the gradient direction pixels in the medium-resolution image, and the co-occurrence in the gradient direction pixels in the low-resolution image, to a co-occurrence matrix. The device creates such a co-occurrence matrix for each pixel combination and for each resolution. The device executes the process on each of the pixels of the high-resolution image, and creates a co-occurrence histogram wherein the elements of a plurality of co-occurrence matrices are arranged in a line. The device normalizes the co-occurrence histogram and extracts, as a feature quantity of the image, a vector quantity having as a component a frequency resulting from the normalization.