Abstract: Systems and methods for image based location estimation are described. In one example embodiment, a first positioning system is used to generate a first position estimate. A set of structure façade data describing one or more structure façades associated with the first position estimate is then accessed. A first image of an environment is captured, and a portion of the image is matched to part of the structure façade data. A second position is then estimated based on a comparison of the structure façade data with the portion of the image matched to the structure façade data.

Abstract: An automatic target recognizer system including: a database that stores target recognition data including multiple reference features associated with each of multiple reference targets; a pre-selector that selects a portion of the target recognition data based on a reference gating feature of the multiple reference features; a preprocessor that processes an image received from an image acquisition system which is associated with an acquired target and determines an acquired gating feature of the acquired target; a feature extractor and processor that discriminates the acquired gating feature with the reference gating feature and, if there is a match, extracts multiple segments of the image and detects the presence, absence, probability or likelihood of one of multiple features of each of the multiple reference targets; a classifier that generates a classification decision report based on a determined classification of the acquired target; and a user interface that displays the classification decision report.

Abstract: This document describes systems and processes manufacturing and distributing stone slabs, such as including distributing a stone slab and a slab image file associated with the stone slab. The slab image file may include an image and associated information to facilitate one or more operations related to the stone slab.

Abstract: Methods, devices and non-transitory computer-readable storage medium for processing a sequence of respective top view images of a same terrestrial location are provided. One of the method may comprise choosing one image, called reference image, among the respective top view images, estimating for each respective top view image a respective geometric deformation between the respective top view image and the reference image, computing by the respective geometric deformations respective subpixel positions of the respective top view images relative to one high-resolution coordinate system, interpolating at the respective subpixel positions to sample at least part of at least some of the respective top view images on a prescribed grid to obtain a high-resolution image.

Abstract: A LUS robotic surgical system is trainable by a surgeon to automatically move a LUS probe in a desired fashion upon command so that the surgeon does not have to do so manually during a minimally invasive surgical procedure. A sequence of 2D ultrasound image slices captured by the LUS probe according to stored instructions are processable into a 3D ultrasound computer model of an anatomic structure, which may be displayed as a 3D or 2D overlay to a camera view or in a PIP as selected by the surgeon or programmed to assist the surgeon in inspecting an anatomic structure for abnormalities. Virtual fixtures are definable so as to assist the surgeon in accurately guiding a tool to a target on the displayed ultrasound image.

Abstract: An information processing system configured to perform predetermined processing based on out-of-focus information corresponding to a first area of an image, the out-of-focus information corresponding to a difference in focus between the first area of the image and a second area of the image that is different from the first area of the image.

Abstract: A method and device for behavior control of a virtual image based on a text, and a medium are disclosed. The method includes inserting a symbol in a text, and generating a plurality of input vectors corresponding to the symbol and elements in the text; inputting the plurality of input vectors to a first encoder network, and determining a behavior trigger position in the text based on an attention vector of a network node corresponding to the symbol; determining behavior content based on a first encoded vector that is outputted from the first encoder network and corresponds to the symbol; and playing an audio corresponding to the text, and controlling the virtual image to present the behavior content when the audio is played to the behavior trigger position.

Abstract: A system for detecting a ball or strike in baseball.

Abstract: Texture extraction is disclosed.

Abstract: Systems and methods for detecting complex networks in MRI image data in accordance with embodiments of the invention are illustrated. One embodiment includes an image processing system, including a processor, a display device connected to the processor, an image capture device connected to the processor, and a memory connected to the processor, the memory containing an image processing application, wherein the image processing application directs the processor to obtain a time-series sequence of image data from the image capture device, identify complex networks within the time-series sequence of image data, and provide the identified complex networks using the display device.

Abstract: There are described herein methods and system for generating an inspection image of an object from radiographic imaging. The method comprises obtaining a plurality of digital images of the object positioned between a radiation source and a photon beam detector, the digital images taken at different object-detector distances or source-detector distances to create unique grain diffraction patterns in each one of the digital images, and forming the inspection image from image features common to the digital images at a common scale and removing the unique grain diffraction patterns.

Abstract: The technology disclosed present systems and methods to produce an enhanced resolution image from images of a target using structured illumination microscopy (SIM). The method includes transforming at least three images of the target captured by a sensor in a spatial domain into a Fourier domain to produce at least three frequency domain matrices that each include first blocks of complex coefficients and redundant second blocks of complex coefficients that are conjugates to the first blocks. The method includes reducing computing resources required to produce the enhanced resolution image by using first blocks of complex coefficients to produce at least three phase-separated half-matrices in the Fourier domain. The method includes performing one or more intermediate transformation on the phase-separated half-matrices to produce realigned shifted half-matrices. The method includes calculating complex coefficients of second blocks in the Fourier domain to produce full matrices from half-matrices.

Abstract: A computer-program product storing instructions which, when executed by a computer, cause the computer to receive an input data from a sensor, wherein the input data includes data indicative of an image, wherein the sensor includes a video, radar, LiDAR, sound, sonar, ultrasonic, motion, or thermal imaging sensor, generate an adversarial version of the input data, utilizing a generator, in response to the input data, create a training data set utilizing the input data and the adversarial version of the input data, determine an update direction of a meta model utilizing stochastic gradient respect with respect to an adversarial loss, and determine a cross-entropy based classification loss in response to the input data and classification utilizing a classifier, and update the meta model and the classifier in response to the cross-entropy classification loss utilizing the training data set.

Abstract: A computer completes a data image analysis task. The computer receives a machine learning (ML) model trained for use with image data content characterized by first context. The computer receives an evaluation image dataset having evaluation image data content characterized by a second context. The computer receives a request to complete an image data analysis task for the evaluation image dataset using the ML model. The computer compares the contexts to determine whether the contexts are similar and whether the evaluation image dataset is compatible with the ML model. If the evaluation dataset is incompatible with the ML model, the computer uses the generative model to generate a ML model compatible synthetic image dataset based on the evaluation dataset. The computer applies the ML model to the synthetic image dataset to provide an answer for the data image analysis task; the computer delivers the answer to a user interface.

Abstract: The present disclosure relates generally to evaluating the surfaces of a building. The present disclosure relates more particularly to a method of characterizing a surface texture of a building surface. The method includes illuminating a first area of the building surface from a single direction and capturing an image of the first area using a camera while the first area is illuminated. The first image includes a first group of digital pixel values. The method further includes calculating a first set of values that characterize a first surface texture of the first area based on a first group of digital pixel values of the image, and comparing the first set of values to a second set of values that characterize a second surface texture, so as to produce a comparator value.

Abstract: A system trains a model to infer an intent of an entity. The model includes one or more sensors to obtain frames of data, one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to perform steps. A first step includes determining, in each frame of the frames, one or more bounding regions, each of the bounding regions enclosing an entity. A second step includes identifying a common entity, the common entity being present in bounding regions corresponding to a plurality of the frames. A third step includes associating the common entity across the frames. A fourth step includes training a model to infer an intent of the common entity based on data outside of the bounding regions.

Abstract: Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.

Abstract: A method for detecting a field navigation line after ridge sealing of crops includes the following steps. A field crop image is acquired. Image color space transformation, image binaryzation, longitudinal integration, neighborhood setting and region integration calculation are sequentially performed on the field crop image to obtain a crop row image. Detections of an initial middle ridge, a left ridge and a right ridge are performed on the crop row image to obtain center lines of the initial middle ridge, left ridge and right ridge. Center lines of a left (right) crop row are established by using an area 1 between the center lines of the left (right) ridge and the initial middle ridge. A center line model of a middle ridge is established by using an area 0 between the center lines of the left and right crop rows, namely a navigation line of a field operation machine.

Abstract: A computer-implemented method, a computer program product, and a system for reducing labeled sample quantities required to update test sets. The computer-implemented method includes inputting a portion of unlabeled production data into a base model and generating labeled output relating to the unlabeled production data. The computer-implemented method also includes inputting the labeled output into a performance predictor. The performance predictor is a meta model of the base model that is trained with another portion of the unlabeled production data, a training set used to train the base model, and a test set portioned from the training set. The computer-implemented method further includes outputting, by the performance predictor, a performance metric relating to the labeled output produced by the trained base model. The performance metric can be any metric capable of measuring the output performance of the base model.

Abstract: Computer-readable media, methods, and systems are disclosed for converting a diagram into a digital diagram format. A diagram is received as an image file and a plurality of recognition stages produce a final diagram based on predicted information from one or more of the recognition stages. The plurality of recognition stages including a shape detection stage for detecting a plurality of shapes and at least one arrow detection stage in which arrows are detected as relations between pairs of shapes. The final diagram is generated based on the predicted information and is converted into the digital diagram format compatible with a diagram modeling language.