Abstract: This notification device (100) of a notification system (10) detects a portion of the body of a person from distance information from a proximity sensor (110) and image information from an image sensor (120) when an approaching object is a person, and causes an ultrasonic irradiation device (130) to irradiate the detected portion of the body of the person with ultrasonic waves that can generate tactile sensation.

Abstract: Systems and methods described herein relate to jointly training a machine-learning-based monocular optical flow, depth, and scene flow estimator. One embodiment processes a pair of temporally adjacent monocular image frames using a first neural network structure to produce a first optical flow estimate; processes the pair of temporally adjacent monocular image frames using a second neural network structure to produce an estimated depth map and an estimated scene flow; processes the estimated depth map and the estimated scene flow using the second neural network structure to produce a second optical flow estimate; and imposes a consistency loss between the first optical flow estimate and the second optical flow estimate that minimizes a difference between the first optical flow estimate and the second optical flow estimate to improve performance of the first neural network structure in estimating optical flow and the second neural network structure in estimating depth and scene flow.

Abstract: Embodiments of the present invention relate to the technical field of image processing, and disclose an image data processing method and apparatus, an image processing chip, and an aircraft. The method comprises: receiving K channels of image data; performing splitting processing on the L channels of second image data in the K channels of image data to obtain M channels of third image data; performing format conversion processing on the (N?M) channels of first image data and the M channels of third image data to obtain a color image in a preset format; and performing image processing on a gray part component of the color image in the preset format to obtain a depth map. The method can better meet the requirements of multi-channel image data processing.

Abstract: Implementations described herein generally relate to a method for detecting anomalies in time-series traces received from sensors of manufacturing tools. A server feeds a set of training time-series traces to a neural network configured to derive a model of the training time-series traces that minimizes reconstruction error of the training time-series traces. The server extracts a set of input time-series traces from one or more sensors associated with one or more manufacturing tools configured to produce a silicon substrate. The server feeds the set of input time-series traces to the trained neural network to produce a set of output time series traces reconstructed based on the model. The server calculates a mean square error between a first input time series trace of the set of input time series traces and a corresponding first output time series trace of the set of output time-series traces.

Abstract: Gaze data collected from eye gaze tracking performed while training text was read may be used to train at least one layout interpretation model. In this way, the at least one layout interpretation model may be trained to determine current text that includes words arranged according to a layout, process the current text with the at least one layout interpretation model to determine the layout, and output the current text with the words arranged according to the layout.

Abstract: An inference apparatus provides target data to multiple inference models to cause the inference models each derived from local learning data obtained in a different environment to perform predetermined inference to obtain an inference result from each of the inference models. The inference apparatus determines the value of each combining parameter using environment data, weights the inference result from each of the inference models using the determined value of each combining parameter, and combines the weighted inference result from each inference model together to generate an inference result in a target environment.

Abstract: An image interpretation support apparatus includes an acquisition unit, an acceptance unit, and a specifying unit. The acquisition unit acquires a two-dimensional standard image having information on a breast, and a plurality of tomographic images in a plurality of tomographic planes of the breast which are obtained by tomosynthesis imaging of the breast. The acceptance unit accepts a selection instruction of a location on the two-dimensional standard image. In a case where the selection instruction is accepted in the acceptance unit, the specifying unit specifies a corresponding tomographic plane corresponding to a selected location which is the location of which the selection instruction is accepted in the acceptance unit, from among the plurality of tomographic planes on the basis of the plurality of tomographic images.

Abstract: The present disclosure provides a method of training a depth estimation network, which relates to fields of computer vision, deep learning, and image processing technology. The method includes: performing a depth estimation on an original image by using a depth estimation network, so as to obtain a depth image for the original image; removing a moving object from the original image so as to obtain a preprocessed image for the original image; estimating a pose based on the original image and modifying the pose based on the preprocessed image; and adjusting parameters of the depth estimation network according to the original image, the depth image and the pose modified. The present disclosure further provides an apparatus of training a depth estimation network, a method and apparatus of estimating a depth of an image, an electronic device, and a storage medium.

Abstract: Digital pathology is a promising alternative to manual slide analysis, but improvements in imaging and analysis methods are needed to provide an analysis data set of images that can easily be handled, stored, and importantly, delivered from one data location to another. Methods and software are described herein to improve image collection and analysis.

Abstract: An image processing device is provided with: a movement detection unit—for detecting, from time-series images, a first feature relating to the first movement of a head portion of a person and a second feature relating to the second movement of a body portion, which is a part of the person other than the head portion; and an index value calculation unit for calculating an index value which indicates the degree of consistency between the first feature relating to the first movement of the head portion of the person and the second feature relating to the second movement of the body portion of the person.

Abstract: Methods for generating a deep neural net and for localizing an object in an input image, the deep neural net, a corresponding computer program product, and a corresponding computer-readable storage medium are provided. A discriminative counting model is trained to classify images according to a number of objects of a predetermined type depicted in each of the images, and a segmentation model is trained to segment images by classifying each pixel according to what image part the pixel belongs to. Parts and/or features of both models are combined to form the deep neural net. The deep neural net is adapted to generate, in a single forward pass, a map indicating locations of any objects for each input image.

Abstract: An example computing platform comprising is configured to (i) receive, via one or more cameras positioned on a construction site, a plurality of images, (ii) detect, within the plurality of images, a plurality of objects being worn by respective workers on the construction site, (iii) select, from the plurality of images, a set of images depicting a particular worker, and (iv) based on the selected set of images depicting the particular worker, determine a plurality of trade probabilities for the particular worker, each trade probability in the plurality of trade probabilities indicating a likelihood that the particular worker belongs to a particular trade from among a plurality of trades.

Abstract: A machine learning system is provided to enhance various aspects of machine learning models. In some aspects, a substantially photorealistic three-dimensional (3D) graphical model of an object is accessed and a set of training images of the 3D graphical mode are generated, the set of training images generated to add imperfections and degrade photorealistic quality of the training images. The set of training images are provided as training data to train an artificial neural network.

Abstract: A method of and a system for synchronizing data for operating a Self-Driving Vehicle (SDV) are provided. The method comprises: causing, by an electronic device, a camera system and a LIDAR system to provide the image data and the map data to the electronic device in a common time referential, determining, by the electronic device, a first timestamp for the map data, determining, by the electronic device, a second timestamp for the image data, determining, by the electronic device, a temporal offset between the first timestamp and the second timestamp, using, by the electronic device, the temporal offset to trigger a delay between subsequent snapshots generated by the camera system.

Abstract: A system for Magnetic Resonance Imaging (MRI) is provided. The system may obtain at least one training sample each of which includes full MRI data. The system may also obtain a preliminary subsampling model and a preliminary MRI reconstruction model. The system may further generate a subsampling model corresponding to an MRI reconstruction model by jointly training the preliminary subsampling model and the preliminary MRI reconstruction model using the at least one training sample. The subsampling model may be the trained preliminary subsampling model, and the MRI reconstruction model may be at least a portion of the trained preliminary MRI reconstruction model.

Abstract: Disclosed herein is digital object generator that makes uses a one-way function to generate unique digital objects based on the user specific input. Features of the input are first extracted via a few-shot convolutional neural network model, then evaluated weight and integrated fit. The resulting digital object includes a user decipherable output such as a visual representation, an audio representation, or a multimedia representation that includes recognizable elements from the user specific input.

Abstract: A deep neural network for metal artifact reduction is described. A method for computed tomography (CT) metal artifact reduction (MAR) includes receiving a first CT image data; receiving a second CT image data; and generating, by an artificial neural network (ANN), CT output image data configured to include fewer artifacts compared to the first and second CT image data. The ANN includes at least two parallel CT image data streams and a CT output image data stream. A first of the at least two parallel CT image data streams is based, at least in part, on the first CT image data, a second of the at least two parallel CT image data stream is based, at least in part, on the second CT image data. The CT output image data stream is based, at least in part, on respective outputs of the at least two parallel CT image data streams.

Abstract: A system and method are provided for analysing a functional magnetic resonance imaging (MRI) scan representing a time-sequence, comprising t time points, of images comprising v voxels, where each scan can be represented by a data matrix X?t×v. The method comprises modelling the scan data X as the convolution of neural activation time courses N?+t×v and a haemodynamic filter ?, and performing an inverse operation to estimate N from X and ?. The method further compasses decomposing the neural activation time courses N into multiple brain network components by representing N as the product of a first matrix H defining, for each component, a spatial map of the voxels belonging to that component, and a second matrix W defining, for each component, the time sequence of activation of that component during the scan. In other implementations, the matrix decomposition may be performed before the deconvolution.

Abstract: A microscope system comprising an eyepiece, an objective that guides light from a sample to the eyepiece, a tube lens that is disposed on a light path between the eyepiece and the objective and forms an optical image of the sample on the basis of light therefrom, a projection apparatus that projects a projection image including a first assistance image onto an image plane on which the optical image is formed, and a processor that performs processes. The processes include generating projection image data representing the projection image. The first assistance image is an image of the sample in which a region wider than an actual field of view corresponding to the optical image is seen, The first assistance image is projected onto a portion of the image plane that is close to an outer edge of the optical image.

Abstract: A ML model arrangement configured for evaluating motion patterns in a sequence of image data structures is described. The ML model arrangement comprises a first ML model configured for predicting a set of key data elements for each image data structure of the sequence of image data structures, a key data element indicating a respective position of a landmark in the image data structure. The ML model arrangement further comprises at least one second ML model, each second ML model being a ML model configured for evaluating a corresponding specific motion pattern. Each second ML model is configured for determining, based on input data comprising at least one of the key data elements predicted for at least one image data structure or data derived therefrom, class labels for each image data structure, said class labels identifying at least one of: at least one motion phase of the specific motion pattern, at least one evaluation point of the specific motion pattern.