Abstract: Methods and systems for providing a dataset of human in-bed poses include simultaneously gathered images of in-bed poses of humans from imaging modalities including red-green-blue (RGB) and one or more of long wavelength infrared (LWIR), depth imaging, and pressure mapping. The images are obtained under a lighting condition and a cover condition. The dataset can be used to train a model of estimating human in-bed poses and for methods of estimating human in-bed poses. Methods and systems of estimating three-dimensional human poses from two-dimensional input images are provided.

Abstract: A computer-implemented method of reducing noise in magnetic resonance (MR) images is provided. The method includes executing a neural network model of analyzing MR images, wherein the neural network model is trained with a pair of pristine images and corrupted images. The pristine images are the corrupted images with noise reduced, and target output images of the neural network model are the pristine images. The method also includes receiving first MR signals and second MR signals, reconstructing first and second MR images based on the first MR signals and the second MR signals, and analyzing the first MR image and the second MR image using the neural network model. The method further includes deriving a denoised MR image based on the analysis, wherein the denoised MR image is a combined image based on the first MR image and the second MR image and outputting the denoised MR image.

Abstract: An information processing program causes a computer to execute a process including: generating, by inputting a captured image into a machine learning model, skeleton information on a person included in the captured image; detecting, by using the skeleton information, a specific motion of the person related to an object included in the captured image; specifying, by using positional information on the person included in the captured image, a first area in which the person is located at a time of detection of the specific motion from among a plurality of areas; specifying, by reading setting information recorded in a memory, first setting information that is associated with the first area; and identifying, based on the first setting information, a priority level of a notification of an alert related to the specific motion of the person related to the object.

Abstract: Examples of monitoring of follicular fluid flow using image processing are described herein. Image frames pertaining to a pre-defined target area defined for a collection tube during an ovum pick-up (OPU) step of an in-vitro fertilization (IVF) procedure may be obtained in real-time. The target area corresponds to a region indicating entry of a follicular fluid into the collection tube. Thereafter, the image frames may be processed to detect occurrence of a flow of the follicular fluid as well as presence of blood in the follicular fluid. Accordingly, a first alert signal indicating detection of occurrence of flow of the follicular fluid and a second alert signal indicating presence of blood in the follicular fluid, may be generated to notify a medical practitioner.

Abstract: A robot system includes a robot installed in a work area and controlled by a second control device, a 3D camera operated by an operator, a sensor that is disposed in a manipulation area that is a space different from the work area, and wirelessly detects position information and posture information on the 3D camera, a display, and a first control device. The first control device acquires image information on a workpiece imaged by the 3D camera, acquires, from the sensor, the position information and the posture information when the workpiece is imaged by the 3D camera, displays the acquired image information on the display, forms a three-dimensional model of the workpiece based on the image information, and the acquired position information and posture information, displays the formed three-dimensional model on the display, and outputs first data that is data of the formed three-dimensional model to the second control device.

Abstract: In some embodiments, a system is provided that includes an edge computing device and at least one camera configured to obtain image data depicting at least a portion of an operations area. The edge computing device includes a non-transitory computer-readable medium that has a model data store and computer-executable instructions stored thereon. The instructions cause the edge computing device to perform actions including receiving at least one image from the at least one camera; processing the at least one image using at least one machine learning model stored in the model data store to determine at least one environmental state within the operations area; and controlling a device based on the determined at least one environmental state. The machine learning model is trained by a model management computing system that obtains training data via low-bandwidth connections to edge computing devices.

Abstract: A facial expression recognition method includes extracting a first feature from color information of pixels in a first image, and extracting a second feature of facial key points from the first image. The method further includes combining the first feature and the second feature, to obtain a fused feature, and determining, by processing circuitry of an electronic device, a first expression.

Abstract: The present disclosure relates generally to image processing, and more particularly, toward techniques for structured illumination and reconstruction of three-dimensional (3D) images. Disclosed herein is a method to jointly learn structured illumination and reconstruction, parameterized by a diffractive optical element and a neural network in an end-to-end fashion. The disclosed approach has a differentiable image formation model for active stereo, relying on both wave and geometric optics, and a trinocular reconstruction network. The jointly optimized pattern, dubbed “Polka Lines,” together with the reconstruction network, makes accurate active-stereo depth estimates across imaging conditions. The disclosed method is validated in simulation and used with an experimental prototype, and several variants of the Polka Lines patterns specialized to the illumination conditions are demonstrated.

Abstract: Provided are an electronic device for sharpening an image and an operation method thereof. A method, performed by the electronic device, of sharpening an image includes: obtaining an image generated by a camera of the electronic device; obtaining a first sharpening kernel for enhancing sharpness of the image, wherein the first sharpening kernel is data including a plurality of weights to be applied to pixels in the image, the data being of a lower resolution than the image; determining coordinates corresponding to some weights indicating representative values of the first sharpening kernel from among the plurality of weights in the first sharpening kernel; generating a second sharpening kernel by selecting the weights corresponding to the determined coordinates; and obtaining a sharpened image by applying the second sharpening kernel to the image.

Abstract: A computer-implemented method of an embodiment is for automatically estimating and/or correcting an error due to artifacts in a multi-energy computed tomography result dataset relating to at least one target material. In an embodiment, the method includes determining at least one first subregion of the imaging region, which is free from the target material and contains at least one, in particular exactly one, second material with known material-specific energy dependence of x-ray attenuation; for each first subregion, comparing the image values of the energy dataset for each voxel, taking into account the known energy dependence, to determine deviation values indicative of artifacts; and for at least a part of the at least one remaining second subregion of the imaging region, calculating estimated deviation values by interpolating and/or extrapolating from the determined deviation values in the first subregion, the estimated deviation values being used as estimated error due to artifacts.

Abstract: Disclosed are an image-based indoor positioning service system and method. A service server includes a communication unit configured to receive a captured image of a node set in an indoor map, and a location estimation model generation unit configured to learn the captured image of the node received through the communication unit, segment the learned captured image to obtain objects, and selectively activate the objects in the learned image to generate a location estimation model.

Abstract: A model may be trained on a training dataset, e.g., for medical image processing or medical signal processing tasks. Systems and computer-implemented methods are provided for associating a population descriptor with the trained model and using the population descriptor to determine whether records to which the model is to be applied, conform to the population descriptor. The population descriptor characterizes a distribution of the one or more characteristic features over the training dataset, with the characteristic features characterizing the training record and/or a model output provided when the trained model is applied to the training record. For instance, the model may be applied only to records conforming to population descriptor, or model outputs of applying the model to non-conforming records may be flagged as possibly untrustworthy.

Abstract: A scene reconstruction system renders images of a scene with high-quality geometry and appearance and supports view synthesis, relighting, and scene editing. Given a set of input images of a scene, the scene reconstruction system trains a network to learn a volume representation of the scene that includes separate geometry and reflectance parameters. Using the volume representation, the scene reconstruction system can render images of the scene under arbitrary viewing (view synthesis) and lighting (relighting) locations. Additionally, the scene reconstruction system can render images that change the reflectance of objects in the scene (scene editing).

Abstract: A console includes a CPU that acquires an image to be processed which is an object to be subjected to a support process that is a diagnosis support process or an imaging support process and selects a support process to be applied to the image to be processed from a plurality of processes of the support process on the basis of a part of a subject which is included in the image to be processed.

Abstract: The present disclosure is related to systems and methods for noise reduction. The method includes obtaining a current frame including a plurality of first pixels. The method includes determining an interframe difference between each first pixel in the current frame and a corresponding pixel in a previous frame obtained prior to the current frame. The method includes generating a denoised frame by performing a first noise reduction operation on the current frame. The method includes determining an intraframe difference for each second pixel in the denoised frame. The method includes generating a target frame by performing a second noise reduction operation on the denoised frame.

Abstract: A control system for a vehicle includes a camera mounted on the vehicle and configured to take an image of an occupant of the vehicle, and an anti-droplet protective equipment providing device mounted on the vehicle and configured to provide anti-droplet protective equipment to the occupant, a determination unit configured to determine whether the occupant is wearing the anti-droplet protective equipment based on the image of the occupant taken by the camera, and a provision control unit configured to provide the anti-droplet protective equipment to the occupant with the anti-droplet protective equipment providing device when the determination unit determines that the occupant is not wearing the anti-droplet protective equipment.

Abstract: A method includes obtaining an image data set that depicts semiconductor components, and applying a hierarchical bricking to the image data set. In this case, the bricking includes a plurality of bricks on a plurality of hierarchical levels. The bricks on different hierarchical levels have different image element sizes of corresponding image elements.

Abstract: An inpainting method includes obtaining an image including an object having a delicate shape and identifying a target region within the image, where the target region is adjacent to the object. The method also includes using a first mask to separate the image into a number of semantic categories and aggregating neighboring contexts for the target region based on the semantic categories. The method further includes restoring, based on the aggregated contexts, textures in the target region without affecting the delicate shape of the object. In addition, the method includes displaying a refined image including the restored textures in the target region and the object.

Abstract: This specification relates to reconstructing three-dimensional (3D) scenes from two-dimensional (2D) images using a neural network. According to a first aspect of this specification, there is described a method for creating a three-dimensional reconstruction of a scene with multiple objects from a single two-dimensional image, the method comprising: receiving a single two-dimensional image; identifying all objects in the image to be reconstructed and identifying the type of said objects; estimating a three-dimensional representation of each identified object; estimating a three-dimensional plane physically supporting all three-dimensional objects; and positioning all three-dimensional objects in space relative to the supporting plane.

Abstract: A method for the simultaneous imaging of different physical properties of an examined medium from multi-physics datasets and for digital enhancement and restoration of multiple multidimensional digital images is described. The method introduces nonnegative joint entropy determined as a joint weighted average of the logarithm of the corresponding density of the model parameters and/or images and/or their attributes. The joint entropy measures are introduced as additional constraints, and their minimization results in enforcing of the order and consistency between the different model parameters and/or multiple images and/or their transforms. The method does not require a priori knowledge about specific physical, analytical, empirical or statistical relationships between the different model parameters and/or multiple images and their attributes, nor does the method require a priori knowledge about specific geometric or structural relationships between different model parameters, images, and/or their attributes.