Abstract: A method for processing image data, may include: receiving at least one image; segregating the at least one image into at least one region, based on a requested noise reduction level; and denoising the at least one image by varying at least one control feature of the segregated at least one region by a neural network to achieve the requested noise reduction level.

Abstract: Systems and methods for controlling a vehicle are provided. The systems and methods receive a frame of visible image data from at least one camera of the vehicle and a frame of invisible perception data from a perception sensor. The systems and methods fuse the frame of invisible perception data and the frame of visible image data to provide a fused frame of perception data. Object detection, classification and tracking are performed using a neural network based on the fused frame of perception data to provide object detection data. Steering, propulsion and/or braking of the vehicle are controlled based on the object detection data.

Abstract: An image processing method and system that generates output images. The system receives a first input image depicting a first set of products and determines the first set of products and corresponding first product categories. The system then receives, on a user interface of a requestor device, a second input image depicting other products selected as being of interest having corresponding second product categories for the other products. In response to a match between one of the first product categories and the second product categories: the system applies the first input image and the second input image to generative adversarial networks (GANs). Each GAN is trained using image dataset for corresponding ones of the first and second product categories, to generate an output image replacing at least a portion of first input image with the second input image, the replacement based on the match between the product categories.

Abstract: Techniques for detecting attributes and/or gestures associated with pedestrians in an environment are described herein. The techniques may include receiving sensor data associated with a pedestrian in an environment of a vehicle and inputting the sensor data into a machine-learned model that is configured to determine a gesture and/or an attribute of the pedestrian. Based on the input data, an output may be received from the machine-learned model that indicates the gesture and/or the attribute of the pedestrian and the vehicle may be controlled based at least in part on the gesture and/or the attribute of the pedestrian. The techniques may also include training the machine-learned model to detect the attribute and/or the gesture of the pedestrian.

Abstract: A surgical exercise performed with a surgical robotic system is sensed by depth cameras, generating 3D point cloud data. Robot system data associated with the surgical robotic system is logged. Object recognition is performed on image data produced by the one or more depth cameras, to recognized objects, including surgical equipment and people, in the operating room (OR). The surgical exercise is digitized by storing the 3D point cloud data of unrecognized objects, a position and orientation associated with the recognized objects, and c) the robot system data.

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: A method of identifying a false alarm of an optical motion sensor including a plurality of pixel units, includes: receiving pixel values of a testing set of pixel units selected from the plurality of pixel units, and a number of pixels of the testing set is smaller than a number of the plurality of pixel units; and performing a background adjustment operation according to the pixel values of the testing set of pixel units.

Abstract: A method for training an artificial neural network including classifiers for material characterization. The method includes obtaining functional data having phase and amplitude, registering functional data by phase-amplitude separation and statistical analysis on the phase-amplitude separated data with an elastic distance to produce aligned functional data, performing dimensional reduction on the aligned functional data to produce a dimensional representation of the functional space, performing, by a computer system, a training operation to train an artificial neural network based on the dimensional representation of the functional space. A method and system for material characterization is also disclosed.

Abstract: The present disclosure relates to systems and methods for object monitoring. The system may obtain at least one image associated with a predetermined region captured by a capture device. The system may obtain a predetermined traffic rule associated with the predetermined region, wherein the predetermined traffic rule is determined based on one or more predetermined images associated with the predetermined region. The system may identify at least one object in the at least one image. The system may monitor a traffic behavior of the at least one object based on the predetermined traffic rule.

Abstract: This application discloses a method for implementing recommendation information in a video performed at a computer device. The method includes: acquiring a target video; identifying a video segment within the target video according to a scene change status of video frames in the target video, the video segment including a target video frame; performing image recognition on the target video frame to obtain a region corresponding to an object in the target video frame that is compatible with the recommendation information; and implanting the recommendation information in the region of the target video frame. Image recognition processing is performed on the target video frame to obtain a video segment including the target video frame, so as to determine the implantation location in the target video frame on the basis of the object of the target type in the target video frame.

Abstract: A method and breast imaging system for processing breast tissue image data includes feeding image data of breast images to an image processor, identifying image portions depicting breast tissue and high density elements and executing different processing methods on input images. A first image processing method involves breast tissue enhancement and high density element suppression, whereas the second image processing method involves enhancing high density elements. Respective three-dimensional sets of image slices may be generated by respective image processing methods, and respective two-dimensional synthesized images are generated and combined to form a two-dimensional composite synthesized image which is presented through a display of the breast imaging system. First and second image processing may be executed on generated three-dimensional image sets or two-dimensional projection images acquired by an image acquisition component at respective angles relative to the patient's breast.

Abstract: Systems and methods for selecting a prior comparison study. One system includes an electronic processor configured to, for a medical image study associated with a patient, select a prior comparison image study. The electronic processor is also configured to automatically determine, based on monitored user interaction with the selected prior comparison image study, a usefulness of the selected prior comparison image study. The electronic processor is also configured to automatically update a selection model based on the usefulness of the prior comparison image study to a user.

Abstract: An integrated circuit included in a device for performing a neural network operation includes a buffer configured to store feature map data in units of cells each including at least one feature, wherein the feature map data is for use in the neural network operation; and a multiplexing circuit configured to receive the feature map data from the buffer, and output extracted data by extracting feature data of one of features that are included within a plurality of cells in the received feature map data, the features each corresponding to an identical coordinate value.

Abstract: Provided are an image fusion method, a storage medium and an electronic device are to-be-fused are acquired. Luminance and chrominance separation is performed on the visible light image to extract a luminance component and a chrominance component. Luminance fusion is performed on the luminance component of the visible light image and the infrared image to obtain a luminance fusion result. Image reconstruction is performed according to the luminance fusion result and the chrominance component of the visible light image to obtain a fused image.

Abstract: A method for recognizing an object includes reading in a first image signal that represents a first camera image recorded by a mono camera, and a second image signal that represents a second camera image recorded by the mono camera. First pixels situated on an image line of the first camera image are selected from the first image signal. Second pixels are identified from the second image signal, the second pixels corresponding to the first pixels. A flux signal is formed using the first pixels and the second pixels, the flux signal representing an optical flux profile for the first pixels situated along the image line. The flux profile represented by the flux signal is segmented into a plurality of segments, each of which represents a plane in the vehicle surroundings. An object signal that represents a recognized object is determined, using the plurality of segments.

Abstract: The system and method for pneumonia detection using image processing and object detection in chest X-ray images utilizes an ensemble of deep learning models, including GoogLeNet, ResNet-18, and DenseNet-121. The process begins with the acquisition and pre-processing of medical images to standardize and partition them into training, validation, and testing subsets. The convolutional neural networks (CNNs) are trained and fine-tuned on the training subset, and their performance is evaluated using the validation subset with metrics such as accuracy, precision, recall, and F1-score. The system computes an ensembled probability score to make the final prediction, ensuring high accuracy. This method also integrates clinical data for improved diagnostic precision.

Abstract: Systems and methods are disclosed for verifying slide and block quality for testing. The method may comprise receiving a collection of one or more digital images at a digital storage device. The collection may be associated with a tissue block and corresponding to an instance. The method may comprise applying a machine learning model to the collection to identify a presence or an absence of an attribute, determining an amount or a percentage of tissue with the attribute from a digital image in the collection that indicates the presence of the attribute, and outputting a quality score corresponding to the determined amount or percentage.

Abstract: An indicia-reading module is capable of integration into the smallest face of thin-profile smart device. The module employs chip-on-board packaging and a customized sensor enclosure to eliminate the stack-up height found in conventional packaging. The module also employs a customized frame to reduce volume by integrating circuit subassembly circuit boards into a unique architecture and by serving as the lenses for the illuminator and the aimer, thereby eliminating the need for any extra lenses or holders.

Abstract: A radio frequency (RF) switch arrangement that improves the voltage handling capacity of a stack of switching elements (e.g., field-effect transistors (FETs)). The RF switch arrangement can include a ground plane and a stack arranged in relation to the ground plane, the stack including a plurality of switching elements coupled in series with one another. The RF switch arrangement can also include a plurality of capacitive elements, each of the plurality of capacitive elements providing a capacitive path across respective terminals of a corresponding one of the plurality of switching elements.

Abstract: A non-transitory computer-readable recording medium has stored therein a program that causes a computer to execute a process, the process including acquiring movie data including a plurality of consecutive frames calculating first likelihood of a class of the movie data by inputting the acquired movie data to a trained model, calculating an optical flow indicating movement of an area included in the movie data, based on the movie data generating occluded movie data by setting an occluded area in each of the frames included in the movie data, based on the optical flow, calculating second likelihood of a class of the occluded movie data by inputting the occluded movie data to the model identifying an area that affects identification of the class among areas in the movie data, based on the first likelihood and the second likelihood and displaying the identified area that affects identification of the class.