Abstract: Methods, systems, and devices for multi-device object tracking and localization are described. A device may transmit a request message associated with a target object to a set of devices within a target area. The request message may include an image of the target object, a feature of the target object, or at least a portion of a trained model associated with the target object. Subsequently, the device may receive response messages from the set of devices based on the request message. The response messages may include a portion of a captured image including the target object, location information of the devices, a pose of the devices, or temporal information of the target object detected within the target area by the devices. In some examples, the device may determine positional information with respect to the target object based on the one or more response messages.

Abstract: A computer model to identify a type of physical card is trained using simulated card images. The physical card may exist with various subtypes, some of which may not exist or be unavailable when the model is trained. To more robustly identify these subtypes, the training data set for the computer model includes simulated card images that are generated for the card type. The simulated card images are generated based on a semi-randomized background that varies in appearance, onto which an identifying marking of the card type is superimposed, such that the training data for the computer model includes additional randomized sample card images and ensure the model is robust to further variations in subtypes.

Abstract: A system for performing adaptive focusing of a microscopy device comprises a microscopy device configured to acquire microscopy images depicting cells and one or more processors executing instructions for performing a method that includes extracting pixels from the microscopy images. Each set of pixels corresponds to an independent cell. The method further includes using a trained classifier to assign one of a plurality of image quality labels to each set of pixels indicating the degree to which the independent cell is in focus. If the image quality labels corresponding to the sets of pixels indicate that the cells are out of focus, a focal length adjustment for adjusting focus of the microscopy device is determined using a trained machine learning model. Then, executable instructions are sent to the microscopy device to perform the focal length adjustment.

Abstract: A sensor is provided and includes a first control line; a first signal line; a first auxiliary line; a first detection electrode; a first detection switch connected to the first detection electrode, the first control line and the first signal line; and a first shielding electrode connected to the first auxiliary line, wherein the first shielding electrode is located to overlap the first signal line via an insulating film.

Abstract: A ball tracking and scoring system and also enables a game implementation system using this system.

Abstract: Systems and methods are disclosed for evaluating cardiovascular treatment options for a patient. One method includes creating a three-dimensional model representing a portion of the patient's heart based on patient-specific data regarding a geometry of the patient's heart or vasculature; and for a plurality of treatment options for the patient's heart or vasculature, modifying at least one of the three-dimensional model and a reduced order model based on the three-dimensional model. The method also includes determining, for each of the plurality of treatment options, a value of a blood flow characteristic, by solving at least one of the modified three-dimensional model and the modified reduced order model; and identifying one of the plurality of treatment options that solves a function of at least one of: the determined blood flow characteristics of the patient's heart or vasculature, and one or more costs of each of the plurality of treatment options.

Abstract: Aspects of the subject disclosure may include, for example, receiving a plurality of proposed machine learning solutions to a machine learning problem including receiving, for each respective proposed machine learning solution of the plurality of proposed machine learning solutions, one or more of a machine learning model, a dataset and a data pipeline output; automatically determining hybrid solutions to the machine learning problem, including combining, by the processing system, at least one of a first component from a first proposed machine learning solution with at least one of a second component from a second proposed machine learning solution; and ranking the hybrid solutions including determining a log loss score for each hybrid solution and sorting the hybrid solutions according to the log loss score for each hybrid solution. Other embodiments are disclosed.

Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for pre-processing image data before 3D object tracking includes, in at least one aspect, a method including: receiving, at a first computer, image frames from a camera; identifying, by the first computer, locations of interest in the image frames; finding sequences of the locations, wherein each of the sequences satisfies a motion criterion for locations identified in at least three image frames from the camera; and sending output data for the sequences of the locations to a second computer for processing the sequences in the output data by interpolating between specified 2D positions in specific image frames for the sequences, using timestamps of the specific image frames, to produce a virtual 2D position at a predetermined point in time, which is usable for constructing a 3D track of a ball in motion.

Abstract: A surgical instrument navigation system and method of use is provided that visually simulates a virtual volumetric scene of a body cavity of a patient from a point of view of a surgical instrument residing in the cavity of the patient, wherein the surgical instrument, as provided, may be a steerable surgical catheter with a biopsy device and/or a surgical catheter with a side-exiting medical instrument, among others. Additionally, systems, methods and devices are provided for forming a respiratory-gated point cloud of a patient's respiratory system and for placing a localization element in an organ of a patient.

Abstract: Systems and methods of automating the generation of a correction of an estimate of an elevation of a digital elevation model (DEM) of the bare earth under forest canopy. The disclosed embodiments facilitate generation of a more accurate DEM in areas of canopy coverage (where the input X-band DSM cannot see the ground) to estimate both the canopy height and the associated DEM. In some embodiments, the result of computationally correcting an estimate of an original DEM is a modified DEM. The method of correcting an estimate of an original DEM utilizes a pair of P-band radar images, an original DEM overlapping the same scene as the P-band radar images, at least one common, uniquely-identifiable point in the P-band radar images, and a definition of a geographical area surrounding the common, uniquely identifiable point over which the elevation correction is applicable.

Abstract: A diagnostic tool for deep learning similarity models and image classifiers provides valuable insight into neural network decision-making. A disclosed solution generates a saliency map by: receiving a baseline image and a test image; determining, with a convolutional neural network (CNN), a first similarity between the baseline image and the test image; based on at least determining the first similarity, determining, for the test image, a first activation map for at least one CNN layer; based on at least determining the first similarity, determining, for the test image, a first gradient map for the at least one CNN layer; and generating a first saliency map as an element-wise function of the first activation map and the first gradient map. Some examples further determine a region of interest (ROI) in the first saliency map, cropping the test image to an area corresponding to the ROI, and determine a refined similarity score.

Abstract: A method and system for performing semi or fully automatic data imbalance detection and correction in training a machine-learning (ML) model includes receiving a request to train the ML model, receiving access to a dataset for use in training the ML model, identifying a feature of the dataset for which data imbalance detection is to be performed, examining the dataset to determine a distribution of the feature across the dataset, determining if the distribution of the feature across the dataset indicates data imbalance, upon determining that the distribution of the feature across the dataset indicates data imbalance, identifying a desired distribution for the identified feature, selecting a subset of the dataset that corresponds with the selected feature and the desired distribution, and using the subset to train the ML model.

Abstract: A method for shadow and cloud masking for remote sensing images of an agricultural field using multi-layer perceptrons includes electronically receiving an observed image, performing using at least one processor an image segmentation of the observed image to divide the observed image into a plurality of image segments or superpixels, extracting features for each of the image segments using the at least one processor, and determining by a cloud mask generation module executing on the at least one processor a classification for each of the image segments using the features extracted for each of the image segments, wherein the cloud mask generation module applies a classification model including an ensemble of multilayer perceptrons to generate a cloud mask for the observed image such that each pixel within the observed image has a corresponding classification.

Abstract: A method, device, and computer readable medium for displaying a two-dimensional image of a viewed object simultaneously with an image depicting a three-dimensional geometry of the viewed object using a video inspection device is disclosed. The video inspection device displays a two-dimensional image of the object surface of a viewed object, and determines the three-dimensional coordinates of a plurality of surface points. At least one rendered image of the three-dimensional geometry of the viewed object is displayed simultaneously with the two-dimensional image. As measurement cursors are placed and moved on the two-dimensional image, the rendered image of the three-dimensional geometry of the viewed object is automatically updated.

Abstract: Techniques for implementing an efficient machine learning (ML) model for classification are provided. In one set of embodiments, a computer system can receive a query data instance to be classified. The computer system can then generate a first classification result for the query data instance using a first (i.e., primary) ML model, where the first classification result includes a predicted class for the query data instance and a confidence level indicating a likelihood that the predicted class is correct, and compare the confidence level with a classification confidence threshold. If the confidence level is greater than or equal to the classification confidence threshold, the computer system can output the first classification result as a final classification result for the query data instance. However, if the confidence level is less than the classification confidence threshold, the computer system can forward the query data instance to one of a plurality of second (i.e.

Abstract: A processor-implemented neural network method includes: determining a respective probability density function (PDF) of normalizing a statistical distribution of parameter values, for each channel of each of a plurality of feature maps of a pre-trained neural network; determining, for each channel, a corresponding first quantization range for performing quantization of corresponding parameter values, based on a quantization error and a quantization noise of the respective determined PDF; determining, for each channel, a corresponding second quantization range, based on a signal-to-quantization noise ratio (SQNR) of the respective determined PDF; correcting, for each channel, the corresponding first quantization range based on the corresponding second quantization range; and generating a quantized neural network, based on the corrected first quantization range corresponding for each channel.

Abstract: A method for estimating tightly enclosing bounding boxes by a computing system includes: controlling a scanning system including one or more depth cameras to capture visual information of the scene including one or more objects; detecting the one or more objects of the scene based on the visual information; singulating each the one or more objects from the frame of the scene to generate one or more 3D models corresponding to the one or more objects, the one or more 3D models including a partial 3D model of a corresponding one of the one or more objects; extrapolating a more complete 3D model of the corresponding one of the one or more objects based on the partial 3D model; and estimating a tightly enclosing bounding box of the corresponding one of the one or more objects based on the more complete 3D model.

Abstract: According to an aspect of an embodiment, a method may include obtaining a data set that includes categories (or features), and a target criteria. The method may further include obtaining a first decision tree model using the data set. The method may further include ranking the categories based on the first decision tree model and removing low-ranking categories from the data set. The method may further include generating a second decision tree model using the data set. The second decision tree model may include branch nodes. Each of branch nodes may represent a branch criteria. The method may further include pruning a branch node. The method may further include designating a remaining branch nodes as a rule node. The method may further include generating a rule based on the branch criteria of the rule node and presenting the rule in a graphical user interface.

Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for an end-to-end solution to auto-identifying features. In one embodiment, a novel architecture is presented that enables the identification of optimal features and feature processes for use by a machine learning model. The novel architecture introduces a feature orchestrator for managing, routing, and retrieving the data and features associated with analytical job request. The novel architecture also introduces a feature store designed to identify, rank, and store the features and data used in the analysis. To aid in identifying the optimal features and feature processes, a training system may also be included in the solution which can perform some of the training and scoring of the features.

Abstract: An optical imaging device for imaging a biometric input object is provided. The optical imaging device includes an optical sensor having an array of sensing elements. The optical sensor is configured to first read a first subset of sensing elements in the array of sensing elements; analyze the first read of the first subset of sensing elements to determine an ambient light condition; first alter an operating point of the optical imaging device based on the ambient light condition; and image the input object.