Abstract: A device, method and system for identifying objects in warped images from a fisheye camera is provided. A device converts warped images from a fisheye camera into dewarped images. The device determines that a first object identified in the dewarped images is: entering a blind-spot region of the dewarped images; or located at a first edge region of the dewarped images. The device determines that a second object is: exiting the blind-spot region of the dewarped images, or located at a second edge region of the dewarped images, the second edge region opposite the first edge region. The device, in response to the second object meeting one or more reidentification conditions associated with the first object, reidentifies the second object as the first object at the dewarped images.

Abstract: Disclosed are methods, systems, and apparatus for monitoring delivered packages using video. A method includes obtaining a notification of a delivery of a package at a property that includes a first image depicting the package; obtaining a second image captured by a camera at the property; determining, using the first image and the second image, that the package has been delivered to the property; generating a package model that represents an appearance of the package; and tracking a location of the package for use in determining whether to provide an alert to a device about the location of the package. Determining that the package has been delivered to the property comprises: comparing the first image to a model of a scene of the property; determining that the first image satisfies similarity criteria for matching the model of the scene; and determining that the second image likely depicts the package.

Abstract: Provided is a method for predicting a location of a fast-moving object. The method includes receiving event information from an event camera, the event information corresponding to an event detected by the event camera, generating a Binary Event History Image (BEHI) based on the event information, providing the BEHI as an input to an event-based neural network, obtaining, as an output of the event-based neural network, a first predicted location of the fast-moving object, a normal distribution indicating prediction uncertainty of the predicted location, and a predicted time-to-collision (TTC). The method further includes estimating a second predicted location of the fast-moving object based on the first predicted location, the normal distribution, and the predicted TTC output by the event-based neural network, and actuating a mechanical catching device to be at the second predicted location.

Abstract: Systems and methods for managing at least one medical image that includes image data and metadata. One method includes generating, at a processor, a number of copies of the at least one medical image, wherein the number of copies is equal to a number of independent reviewers associated with the at least one medical image. Each of the copies includes the image data and the metadata. The method also includes modifying the metadata of each of the copies to include a unique identifier, and storing the copies to at least one image storage device.

Abstract: Methods, systems and devices for use in semiconductor package manufacture of a package having a die and die substrate are provided. A method for use in semiconductor package manufacture includes steps of forming one or more wirebond interconnections between the die and the die substrate, capturing input data representative of wirebond interconnection features during inspection of the formed wirebond interconnections, and passing the captured input data to a machine learning (ML) engine. The method further includes processing the captured input data with the machine learning engine using a trained model to obtain an output array of data, evaluating the output array of data to determine a predicted radio-frequency (RF) performance rating, and outputting the predicted RF performance rating. Training data set processing may include applying image data and parameters to a multi-layer neural network to obtain a set of candidate ML models, and selecting an optimal ML model.

Abstract: Systems and methods for evaluating a set of bounding boxes in a blended image are described. A system can include an integrated circuit configured to obtain expected bounding box data. The expected bounding box data can be based on coordinates data of an image. The integrated circuit can determine target coordinates based on the expected bounding box data. The integrated circuit can receive a blended image including a set of objects and a set of bounding boxes. The integrated circuit can extract pixel values located at the target coordinates in the blended image. The integrated circuit can identify an error relating to the set of bounding boxes based on the extracted pixel values and the expected bounding box data.

Abstract: Systems and methods for tracking an object in video surveillance are provided. A method may include obtaining a video including a plurality of consecutive frames; obtaining a current frame from the plurality of consecutive frames, wherein an object of interest is identified in at least two previous frames of the current frame; obtaining at least two template frames from the at least two previous frames; and identifying a position related to the object of interest in the current frame based on the at least two template frames using a correlation filter.

Abstract: A method for determining a centerline of a blood vessel in an image associated with a subject is provided. The method includes obtaining a centerline model used for identifying a centerline of a blood vessel and identifying the centerline of the blood vessel based on the centerline model.

Abstract: A metal artifact reduction (MAR) method and system for reducing metal artifacts from X-ray images. The method is suitable for CT images generated with metallic elements exterior to a patient being scanned, for example, where external probes are applied to a patient, such as ultrasound probes. The method may be embodied in a computer algorithm for use, for example, in radiotherapy treatment planning and patient positioning procedures. In one application, the disclosed technique improves the dose delivery accuracy in ultrasound-guided cardiac radioablation, making this treatment modality a viable option for cardiac arrhythmias.

Abstract: A method of image processing includes: obtaining an original image and obtaining a to-be-processed image based on the original image; recognizing the to-be-processed image by using an image recognition model to obtain a silhouette array; generating a silhouette image based on the silhouette array; and generating a target image based on the original image and the silhouette image, where the target image corresponds to a region of interest of the original image.

Abstract: A detection unit detects a lesion candidate contained in display frame data (a tomographic image), using a machine learned detection model. An exclusion processing unit collates a feature vector of the detected lesion candidate with feature vectors registered in an exclusion database to determine whether the detected lesion candidate is an exclusion target. When the detected lesion candidate is an exclusion target, a mark display control unit restricts display of a mark for indicating a lesion candidate.

Abstract: This provides a system and method for analyzing and diagnosing conditions, such as cancer, based upon stained tissue sample slides prepared by users from patient tissue taken, for example, in a biopsy procedure. A convolutional neural network (CNN) is generated at training time. Slide images are acquired/scanned, and individual cell nuclei from the images are annotated, using IHC. H&E images are acquired/scanned prior to a washout intermediate step from the same slides/tissue samples. IHC is performed on the same tissue layer, which was scanned and used to retrospectively annotate the H&E WSI. IHC is used to annotate a ground truth mask for machine learning. The resultant process achieves a high degree of spatial resolution in detecting individual IHC positive nuclei, yielding process that is almost entirely automated, employing materials that are commonly available in clinical practice. In runtime, users access the CNN to perform analysis on patient slides.

Abstract: The present disclosure relates to a high precision method, model, and apparatus for calibrating the intrinsic camera parameter values of one or more intrinsic parameters of a camera. The intrinsic parameters include focal lengths, skew, optical center, and radial distortion coefficients. The practical implementation of the solution consists of a camera capturing several images of a planar calibration pattern from different viewpoints, and a computer implemented processing pipeline. The solution does not require additional human input and is robust to misdetection of the calibration pattern in some of the images.

Abstract: A computing system receives an indication of an energy project. The computing system monitors satellite image data of the energy project. The computing system determines that a first activity has been initiated for the energy project based on a change in satellite image data. The computing system monitors telemetry data associated with a location corresponding to the energy project and the satellite image data of the energy project. The computing system converts the telemetry data associated with the location corresponding to the energy project to a telemetry activity index. The computing system converts the satellite image data associated with the energy project to a satellite activity index. The computing system determines that the first activity has ended for the energy project based on changes in at least one of the telemetry activity index and the pad activity index.

Abstract: A computerized method of providing automatic anatomy recognition (AAR) includes gathering image data from patient image sets, formulating precise definitions of each body region and organ and delineating them following the definitions, building hierarchical fuzzy anatomy models of organs for each body region, recognizing and locating organs in given images by employing the hierarchical models, and delineating the organs following the hierarchy. The method may be applied, for example, to body regions including the thorax, abdomen and neck regions to identify organs.

Abstract: A computer-implemented method, system, and computer-readable medium, for determining a level of hypertension of a person. Image data defining a retinal image of a retina of the person that has been captured by a retinal imaging system is acquired. Processing the image data is performed to identify a plurality of vessel segments present in the retinal image. Determining is performed of respective widths of vessel segments of at least a subset of the plurality of vessel segments present in the retinal image. Calculating, as a vascular summary metric, at least one average value or median value of the determined widths of the vessel segments present in the retinal image, is performed to determine a level of hypertension by which the person has been affected.

Abstract: Provided is a contour shape recognition method, including: sampling and extracting salient feature points of a contour of a shape sample; calculating a feature function of the shape sample at a semi-global scale by using three types of shape descriptors; dividing the scale with a single pixel as a spacing to acquire a shape feature function in a full-scale space; storing feature function values at various scales into a matrix to acquire three types of feature grayscale map representations of the shape sample in the full-scale space; synthesizing the three types of grayscale map representations of the shape sample, as three channels of RGB, into a color feature representation image; constructing a two-stream convolutional neural network by taking the shape sample and the feature representation image as inputs at the same time; and training the two-stream convolutional neural network, and inputting a test sample into a trained network model to achieve shape classification.

Abstract: A method and a device for detecting a placement of wafers in a wafer cassette are provided. The wafer cassette includes a plurality of receiving grooves. The receiving grooves hold and store the wafers. Light is emitted through the wafer cassette. The light passes through the gaps and is imaged, and the image is captured. Characteristic information is extracted from the image. The extracted characteristic information is compared with standard characteristic information of a preset image, and whether a placement of all the wafers in the wafer cassette is qualified and satisfactory is determined according to a compared result.

Abstract: An AI-based object classification method and apparatus, a computer-readable storage medium, and a computer device. The method includes: obtaining a target image to be processed, the target image including a target detection object; separating a target detection object image of the target detection object from the target image; inputting the target detection object image into a feature object prediction model to obtain a feature object segmentation image of a feature object in the target detection object image; obtaining quantitative feature information of the target detection object according to the target detection object image and the feature object segmentation image; and classifying the target detection object image according to the quantitative feature information to obtain category information of the target detection object in the target image.

Abstract: An abnormality detection apparatus (100) includes an acquisition unit (110) that acquires input data, an abnormality degree computation unit (120) that has a discriminative model for computing an abnormality degree of input data, inputs the acquired input data to the discriminative model, and thereby computes an abnormality degree of the input data, a normality degree computation unit (130) that has a normality model for computing a normality degree of input data, inputs the input data to the normality model, and thereby computes a normality degree of the input data, a determination unit (140) that has a determination model for performing determination relating to an abnormality level of input data, inputs the abnormality degree and the normality degree to the determination model, and thereby performs determination relating to an abnormality level of the input data, and an output unit (150) that outputs output information based on a result of the determination.