Abstract: An image obtaining means including: a processor; a memory module communicatively coupled to the processor; a camera communicatively coupled to the processor; and the processor being configured to: receive, from the camera, image data; determine whether a predetermined object is present based on the image data; if a predetermined object is detected as present, determine if the predetermined object is moving or stationary; and if the predetermined object is stationary for at least a predetermined time period, record the duration that the predetermined object is stationary in the memory module.

Abstract: Disclosed are techniques for image processing that mitigate artifacts that otherwise appear when an IQVR camera moves with respect to a scene between image frames.

Abstract: A local variance or an edge amount is calculated as an image feature amount by analyzing a reference image of the same field of view as multiple band images constituting a multispectral image, a filter parameter is calculated from the image feature amount, and smoothing the band images while preserving edges is performed by using the filter parameter. When a multispectral image including a band image of a wavelength band in which a sufficient signal amount is not obtained is obtained, it is possible to improve the S/N ratio of the multispectral image without destroying edges.

Abstract: Provided is a method of creating a local database for local optimization of an object detector based on a deep neural network. The method includes performing preprocessing on an image extracted from real-time collected or pre-collected images from an edge device, modeling a static background image based on the image received through the pre-processing unit and calculating a difference image between a current input image and a background model to model a dynamic foreground image, detecting an object image from the image based on a training model, and creating a local database based on the background image, the foreground image synthesized with the background image, and the object image synthesized with the background image.

Abstract: An embodiment device for detecting a boundary of a road in a 3D point cloud using a cascade classifier includes a rule-based classifier configured to determine whether a received LiDAR cluster has a likelihood of becoming a candidate for the boundary of the road using a box parameter surrounding a point cloud constituting the LiDAR cluster and a point parameter, and a learning-based classifier configured to apply a machine-learning scheme to the LiDAR cluster selected as the candidate for the boundary of the road by the rule-based classifier to determine the LiDAR cluster to be the boundary of the road or an object other than the boundary of the road.

Abstract: A method of treating a subject comprises administering a treatment to a subject identified as having a high probability of distant metastatic recurrence, wherein the probability of distant metastatic recurrence was determined by a process, comprising acquiring at least one image of a tissue sample comprising a plurality of cells, taken from a subject, classifying each of the plurality of cells into categories, dividing the at least one image into a plurality of patches, calculating values for a plurality of morphological features based on the patches, and calculating a distant metastatic recurrence probability based on the values. A computer-implemented method of training a neural network and a system for characterizing a cancer in a subject are also described.

Abstract: Techniques and apparatuses are described for deep neural network (DNN) processing for a user equipment-coordination set (UECS). A network entity selects (910) an end-to-end (E2E) machine-learning (ML) configuration that forms an E2E DNN for processing UECS communications. The network entity directs (915) each device of multiple devices participating in an UECS to form, using at least a portion of the E2E ML configuration, a respective sub-DNN of the E2E DNN that transfers the UECS communications through the E2E communication, where the multiple devices include at least one base station, a coordinating user equipment (UE), and at least one additional UE. The network entity receives (940) feedback associated with the UECS communications and identifies (945) an adjustment to the E2E ML configuration. The network entity then directs at least some of the multiple devices participating in an UECS to update the respective sub-DNN of the E2E DNN based on the adjustment.

Abstract: Described are systems and methods for detecting objects using calibrated imaging devices and obfuscating, in real-time or near real time, portions of the video data to protect the privacy of operators represented in the video data. For example, a position of an operator within a fulfillment center may be determined or tracked in video data and the pixels representative of that operator may be obfuscated using pixilation and/or other techniques so that a reviewing agent that is viewing the video data cannot determine the identity of the operator. Such obfuscation may be performed in real-time or near real-time using automated processing. In addition, only portions of the video data may be obfuscated so that events (e.g., item picks, item place) and/or other objects represented in the video data are still viewable to the reviewing agent.

Abstract: Disclosed is an apparatus for assisting the reading of a medical image based on a medical artificial neural network, the apparatus including a computing system. An apparatus according to an embodiment of the present invention includes a computing system, and the computing system includes at least one processor. The at least one processor acquires or receives a first analysis result obtained through the inference of a first artificial neural network for a first medical image, generates a first visualization format, which is a representative visualization format of the first medical image, based on the first analysis result, and visualizes the first medical image and the first analysis result based on the first visualization format.

Abstract: Selective training of neural networks using motion estimation, including: selecting, from a plurality of pixels in video data from a vehicle, based on motion relative to the vehicle, one or more pixels; and training a neural network based on the video data by zeroing out an error function applied to the selected one or more pixels.

Abstract: A method comprising the steps of: receiving an image from a dynamic vision sensor and an image from an image sensor, wherein the dynamic vision sensor and the image sensor capture at least an overlapping field of view; determining, from the image received from the dynamic vision sensor, an area of movement in the field of view of the image from the image sensor; applying a higher level of image compression to the areas of no movement compared with the area of movement in the image from the image sensor to produce a processed image; and outputting the processed image to a neural network.

Abstract: A method and instruction memory for processing acoustic images of a tubular to determine the surface of the tubular. The images may be acquired by an acoustic logging tool deployed into a pipe or well. A Machine Learning model is trained to recognize regions of the acoustic images that are internal to the tubular or not, in order to determine surface pixels. The surface pixels may be rendered to create visualizations of the tubular where noise and non-surface features are filtered out automatically.

Abstract: Disclosed subject matter relates to method and system for securing data of objects in a laboratory environment. An image capturing device configured in a laboratory instrument may capture images of plurality of objects in the laboratory environment. A processor in the laboratory instrument may identify one or more objects from the images matching with predefined target objects. The processor may apply virtual masking object on or around the identified objects to prevent exposure of data associated with the identified objects and thus provides data privacy.

Abstract: A method for training a model for face recognition is provided. The method forward trains a training batch of samples to form a face recognition model w(t), and calculates sample weights for the batch. The method obtains a training batch gradient with respect to model weights thereof and updates, using the gradient, the model w(t) to a face recognition model what(t). The method forwards a validation batch of samples to the face recognition model what(t). The method obtains a validation batch gradient, and updates, using the validation batch gradient and what(t), a sample-level importance weight of samples in the training batch to obtain an updated sample-level importance weight. The method obtains a training batch upgraded gradient based on the updated sample-level importance weight of the training batch samples, and updates, using the upgraded gradient, the model w(t) to a trained model w(t+1) corresponding to a next iteration.

Abstract: Computer-implemented methods, systems, apparatus, and computer-readable media (transitory or non-transitory) are described herein for detecting eye tracking calibration errors. In various examples, respective poses of first and second eyes of a wearer of a head-mounted display (“HMD”) may be determined. A portion of a first image rendered on a first display of the HMD may be selected based on the pose of the first eye of the wearer. The selected portion of the first image may be matched to a portion of a second image rendered on a second display of the HMD. An eye tracking calibration error may be determined based on the pose of the second eye of the wearer and a location of the portion of the second image.

Abstract: A system for analyzing medical images to detect and classify a medical condition, the system includes an input for receiving a medical image, a convolutional neural network coupled to the input and configured to analyze the medical image to generate a prediction including a probability of the presence of the medical condition in the medical image and an atlas creation module coupled to the convolutional neural network and configured to generate an atlas comprising a set of image features and a set of training images. Each image feature is assigned with at least one training image associated with the medical condition. The system further includes a prediction basis selection module coupled to the convolutional neural network and the atlas and configured to create a prediction basis for the prediction generated by the convolutional neural network.

Abstract: A system includes a reconstructor (314) configured to reconstruct cone beam projection data to generate cone beam artifact corrected short scan cone beam volumetric image data. A method includes reconstructing, with a reconstructor, cone beam projection data to generate cone beam artifact corrected short scan cone beam volumetric image data. A computer-readable storage medium storing computer executable instructions which when executed by a processor of a computer cause the processor to: reconstruct cone beam projection data to generate cone beam artifact corrected short scan cone beam volumetric image data.

Abstract: A method for creating generated formatted data, that includes receiving, by a sequential data generator, raw data, where the raw data includes formation data at a drilling environment, processing the raw data to obtain generated recommendation data, where the generated recommendation data includes a proposed drilling location, and creating the generated formatted data, where the generated formatted data includes the generated recommendation data.

Abstract: A vehicular trailer angle detection system includes a camera disposed at a rear portion of a vehicle. The system determines, via processing of frames of image data captured by the camera, features of a trailer present rearward of the vehicle and hitched to the vehicle by determining features that have similar position changes between a current frame of image data captured by the camera and a previous frame of image data captured by the camera. Responsive to movement of the trailer relative to the vehicle, and via processing of captured frames of image data, the system tracks determined features over multiple captured frames of image data for different positions of the trailer relative to the vehicle. The system determines angle of the trailer relative to the vehicle based at least in part on tracking of determined features of the trailer over multiple captured frames of image data.

Abstract: An Artificial Intelligence (AI) based document processing and validation system identifies anomalies such as errors, fraud, and duplicates of received documents and enables automatic actions for valid documents using machine learning (ML) techniques. The received documents are processed for determining probabilities for errors, fraud, and duplicates. A validation worklist is generated with the documents arranged in descending order of the probabilities and invalid documents with higher probabilities are flagged for review while the valid documents with lower probabilities are further processed for the execution of automatic actions. The feedback from the invalid document review is used to further train the models in determining the probabilities.