Abstract: Provided is a method for acquiring labeled data, including acquiring a 3D-3D registration sample, wherein the 3D-3D registration sample includes a sample 3D image, a reference 3D image and 3D-3D registration data, and the 3D-3D registration data includes correspondence data between the sample 3D image and the reference 3D image; generating at least one dual-2D-image based on the sample 3D image, wherein each dual-2D-image is a combination of 2D projection images of the sample 3D image in two different directions; and generating 2D-3D registration data corresponding to each dual-2D-image based on the 3D-3D registration data, wherein each 2D-3D registration data includes correspondence data between the dual-2D-image and the reference 3D image.

Abstract: The present invention provides a graph-based prostate diagnosis network (GPD-Net) and a method for using the same to predict a prostate health status of a patient from a 3D magnetic resonance imaging (MRI) scan containing a plurality of 2D MRI slices. The GPD-Net only demands patient-level annotations of MRI scan for training by formulating the diagnosis task of 3D prostate MRI scan in a multi-instance learning (MIL) strategy, and regarding each 2D MRI slice in the 3D prostate MRI scan as an instance. The GPD-Net includes a plurality of importance-guided graph convolutional layers to explore the diagnostic information with the importance-based topology. The present invention provides accurate prediction of prostate diseases and achieve more reliable diagnosis from MRI scans, therefore can effectively alleviate the workload of clinician in viewing the slices of MRI scan.

Abstract: A method for measuring blood sugar that is performed by an apparatus for measuring blood is provided. The method includes calculating color data from multiple skin regions of interest in a skin image, classifying a user's meal state into a pre-meal state or a post-meal state through analysis of acquired user information, estimating blood sugar by applying the calculated color data to a blood sugar regression linear equation corresponding to the classified user's meal state, and measuring robust blood sugar by weighted averaging the estimated blood sugar.

Abstract: An apparatus according to an embodiment includes a processing circuit. The processing circuit is configured to obtain first Doppler waveform data related to a left ventricular blood inflow. The processing circuit is configured to detect positions of an E-wave and an A-wave in the first Doppler waveform data, by using the first Doppler waveform data and a trained model trained with training data that includes at least positions of an E-wave and an A-wave in each of a plurality of pieces of second Doppler waveform data related to left ventricular blood inflows and the plurality of pieces of second Doppler waveform data.

Abstract: A system and method of identifying and tracking objects comprises registering an identity of a person who visits an area designated for holding objects, capturing an image of the area designated for holding objects, submitting a version of the image to a deep neural network trained to detect and recognize objects in images like those objects held in the designated area, detecting an object in the version of the image, associating the registered identity of the person with the detected object, retraining the deep neural network using the version of the image if the deep neural network is unable to recognize the detected object, and tracking a location of the detected object while the detected object is in the area designated for holding objects.

Abstract: Provided herein are systems and methods for scoring a post-treatment tooth position of a patient's teeth. A patient's dentition may be scanned and/or segmented. Raw dental features, principal component analysis (PCA) features, and/or other features may be extracted and compared to those of other teeth, such as those obtained through automated machine learning systems. A classifier can identify and/or output the post-treatment tooth position of the patient's teeth.

Abstract: A bar is displayed in a second region. In an undetected state in which a region of interest is not detected, the length of the bar is 0, and when the undetected state transitions to a detected state in which the region of interest is detected, the bar increases in length over time. In addition, when the detected state transitions to the undetected state, display of the bar is continued with the length fixed. Thus, even in the undetected state, it is known that the region of interest has been previously detected. In addition, even in the undetected state, the region-of-interest detected duration is known from the length of the bar.

Abstract: Methods and systems for automated video segmentation are disclosed. A sequence of video frames having video segments of contextually-related sub-sequences may be received. Each frame may be labeled according to segment and segment class. A video graph may be constructed in which each node corresponds to a different frame, and each edge connects a different pair of nodes, and is associated with a time between video frames and a similarity metric of the connected frames. An artificial neural network (ANN) may be trained to predict both labels for the nodes and clusters of the nodes corresponding to predicted membership among the segments, using the video graph as input to the ANN, and ground-truth clusters of ground-truth labeled nodes. The ANN may be further trained to predict segment classes of the predicted clusters, using the segment classes as ground truths. The trained ANN may be configured for application runtime video sequences.

Abstract: A method includes obtaining, by the treatment system configured to implement a machine learning (ML) algorithm, one or more images of a region of an agricultural environment near the treatment system, wherein the one or more images are captured from the region of a real-world where agricultural target objects are expected to be present, determining one or more parameters for use with the ML algorithm, wherein at least one of the one or more parameters is based on one or more ML models related to identification of an agricultural object, determining a real-world target in the one or more images using the ML algorithm, wherein the ML algorithm is at least partly implemented using the one or more processors of the treatment system, and applying a treatment to the target by selectively activating the treatment mechanism based on a result of the determining the target.

Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for processing a perspective view range image generated from sensor measurements of an environment. The perspective view range image includes a plurality of pixels arranged in a two-dimensional grid and including, for each pixel, (i) features of one or more sensor measurements at a location in the environment corresponding to the pixel and (ii) geometry information comprising range features characterizing a range of the location in the environment corresponding to the pixel relative to the one or more sensors. The system processes the perspective view range image using a first neural network to generate an output feature representation. The first neural network comprises a first perspective point-set aggregation layer comprising a geometry-dependent kernel.

Abstract: A method of operating a long term evolution (LTE) communication system on a shared frequency spectrum is disclosed. A user equipment (UE) is initialized on an LTE frequency band. A base station (eNB) monitors the shared frequency spectrum to determine if it is BUSY. The eNB transmits to the UE on the shared frequency spectrum if it is not BUSY. The eNB waits for a first time if it is BUSY and directs the UE to vacate the shared frequency spectrum after the first time.

Abstract: A method for modulating data for transmission within a communication system. The method includes establishing a time-frequency shifting matrix of dimension N×N, wherein N is greater than one. The method further includes combining the time-frequency shifting matrix with a data frame to provide an intermediate data frame. A transformed data matrix is provided by permuting elements of the intermediate data frame. A modulated signal is generated in accordance with elements of the transformed data matrix.

Abstract: A method for proactively creating an image product includes capturing an image of an object in a first environment by a device, storing a library of personalized products each characterized by a product type, automatically recognizing the object in the image as having a product type associated with the library of personalized products, automatically creating a design for the personalized product of the product type using personalized content, automatically displaying the design of the personalized product of the product type incorporating the selected photo in the first environment on the device, and manufacturing a physical product based on the design of the personalized product.

Abstract: Implementations relate to crop yield prediction at the field- and pixel-level. In various implementations, a first temporal sequence of high-elevation digital images may be obtained that capture a first geographic area and are acquired over a first predetermined time interval while the first geographic area includes a particular crop. A first plurality of other data points may also be obtained that influence a ground truth crop yield of the first geographic area after the first predetermined time interval. The first plurality of other data points may be grouped into temporal chunks corresponding temporally with respective images of the first temporal sequence. The first temporal sequence and the temporal chunks of the first plurality of other data points may be applied, e.g., iteratively, as input across a machine learning model to estimate a crop yield of the first geographic area at the end of the first predetermined time interval.

Abstract: An image positioning device is provided in the invention. The image positioning device includes a calculation circuit and a positioning circuit. The calculation circuit obtains a plurality of images from an image capturing device and classifies the images into a plurality of main groups based on a first algorithm, wherein each main group corresponds to a different area and the calculation circuit classifies the images of each main group into a plurality of sub-groups based on a feature of each image of each main group and a clustering algorithm. The positioning circuit is coupled to the calculation circuit. The positioning circuit positions each sub-group in the area corresponding to each main group based on the relative position relationship between each sub-group of each main group.

Abstract: In various examples, training methods as described to generate a trained neural network that is robust to various environmental features. In an embodiment, training includes modifying images of a dataset and generating boundary boxes and/or other segmentation information for the modified images which is used to train a neural network.

Abstract: A method for decoding an image by a decoding device, according to the present document, comprises the steps of: acquiring image information; and generating a reconstructed picture on the basis of the image information.

Abstract: Computer-implemented method, systems, computer program products include a processor(s) that obtains input data comprising an image and image data: coordinates (points) defining a region of interest in the image. The processor(s) determines a non-duplicative sequential order for navigating the coordinates. The processor(s) defines a set of points bordering the region of interest: points in the image and points comprising intersection points between two coordinates with an edge of a pixel in the image. The processor(s) determines a combined area of sub pixel subregions in each pixel that includes points in the set of points by: identifying the points of the set, generating one or more sub pixel subregions, and utilizing the sub pixel subregions to determine the combined area of sub pixel subregions.

Abstract: The present invention relates to a device (1) and a corresponding method for determining residual blood in a dialyzer (2) comprising a holding device (6) configured to accommodate a dialyzer (2), an image capturing device (7) configured to capture at least one image of a dialyzer (2) accommodated by the holding device (6), in particular a filter (4) located in the dialyzer (2), and generate corresponding image data having a plurality of image points, each of which assigned at least one respective intensity value, in particular a color value, and a control device (9) configured to determine residual blood information characterizing the presence and/or content of residual blood in the dialyzer (2) on the basis of a statistical frequency of at least a portion of the intensity values in the at least one image. The invention further relates to a dialysis system having such a device (1).

Abstract: Described herein are systems, apparatus, methods and computer program products configured for image detection/recognition of products. The disclosed systems and techniques utilize video data to provide the necessary number of images and view angles needed to train a machine learning product detection/recognition system to recognize a specific product within later provided images. In various embodiments, a user may provide video data and the video data may be transformed in a manner that may aid in training of the machine learning system.