Abstract: Embodiments of the present disclosure provide a method and a device for positioning a vehicle, a device, and a computer readable storage medium. The method includes: determining first edge information of a vehicle from an image related to the vehicle and acquired by a sensing device; determining a contour model corresponding to the vehicle; determining second edge information that matches the first edge information from an edge information set associated with the contour model, in which each edge information in the edge information set corresponds to a position of the contour model relative to the sensing device; and determining a position of the vehicle relative to the sensing device based on a position corresponding to the second edge information.

Abstract: A system and a method determine the position of an elevator car of an elevator installation, which elevator car is arranged movably in an elevator shaft. The system has a computing unit and an image-capturing unit that are arranged on the elevator car. The image-capturing unit records images of shaft components or shaft equipment used for other functions and transmits the images to the computing unit. The computing unit compares a currently recorded image with at least one stored comparison image of the shaft components or shaft equipment in the direction of travel of the elevator car so as to determine the current position of the elevator car in the direction of travel. The computing unit compares the currently recorded image with the stored comparison image also transversely to the direction of travel so as to determine the current position of the elevator car in the direction of travel.

Abstract: Embodiments of the present application provide a license plate location method and apparatus. The method includes: acquiring a to-be-located license plate image; sending the to-be-located license plate image to a target network for license plate location; wherein, the target network comprises a feature extraction layer and a regression layer; extracting, by the feature extraction layer, a feature value of the to-be-located license plate image, and sending the feature value to the regression layer; acquiring a combination of a license plate frame area and a license plate character area, which is obtained by the regression layer based on the feature value; and determining a license plate area of the to-be-located license plate image based on a location area corresponding to the acquired combination.

Abstract: A method for extracting data from lineless tables includes storing an image including a table in a memory. A processor operably coupled to the memory identifies a plurality of text-based characters in the image, and defines multiple bounding boxes based on the characters. Each of the bounding boxes is uniquely associated with at least one of the text-based characters. A graph including multiple nodes and multiple edges is generated based on the bounding boxes, using a graph construction algorithm. At least one of the edges is identified for removal from the graph, and removed from the graph to produce a reduced graph. The reduced graph can be sent to a neural network to predict row labels and column labels for the table.

Abstract: Described is determining a local deviation of a geometry of an object from a target geometry of the object on the basis of a digital representation of the object that comprises image information items that each specify a value of a measurand for the object at a defined position of the object. This includes determining the object representation, determining a distance field from the image information items of the object representation that comprises distance values for a specific point of the distance field that specifies the shortest distance of the point from a closest material boundary of the geometry of the object, determining the target geometry of the object, and determining the local deviation of the geometry of the object from the target geometry of the object at a test point on a material boundary predefined by the target geometry.

Abstract: Technologies are described for providing a projected augmented reality system with pose tracking for directing manual processes. A projected augmented reality system includes a video projector, configured to project a dynamically-changing image onto a surface within a work area of an operator, a sensor, and a computer. The computer includes a memory including instructions that when executed cause the computer to obtain three-dimensional pose data using the sensor, determine an output graphical element based on a sequence of three-dimensional pose data over time, and on a current production state, and send an output image based on the output graphical element to the video projector for projection onto the surface.

Abstract: Techniques to determine document recognition errors are described. In one embodiment, an apparatus may comprise an image reception component operative to receive an incoming document image; a document recognition component operative to perform a document recognition process on the incoming document image to generate a recognized document; and collect document recognition process information for the document recognition process of the incoming document image; a document reconstruction component operative to generate a reconstructed document image based on the document recognition process information; and an image comparison component operative to compare the reconstructed document image to the incoming document image to generate document recognition performance information for the recognized document. Other embodiments are described and claimed.

Abstract: Provided is a medical imaging device capable of obtaining a medical image with high image quality in a short time by using a novel image reconstruction technique in which DL is applied to medical image processing and by significantly reducing a total imaging time including an image reconstruction time. The medical imaging device classifies a medical image into any of a predetermined plurality of classes, selects an optimal one or a plurality of restorers from a plurality of restorers respectively corresponding to the plurality of classes according to classification results, and reconstructs the medical image using the selected restorers. The medical image is divided into, for example, a plurality of patches, and is reconstructed for each patch and integrated. The restorer can include a CNN.

Abstract: A method disclosed herein uses a processor of a server to function as a processing unit to enhance accuracy of character recognition in a terminal connected to the server, using a communication apparatus of the server. The processing unit may be configured to acquire first data indicating a result of character recognition with respect to image data taken by the terminal. The processing unit can determine a character type of a character included in the image data when it is determined that misrecognition is included in the result of character recognition based on the first data. The processing unit controls the communication apparatus to transmit second data according to the character type to terminal and instructs the terminal to perform character recognition using the second data with respect to the image data in order to improve the accuracy of character recognition.

Abstract: The present specification discloses systems and methods for integrating manifest data for cargo and light vehicles with their X-ray images generated during scanning. Manifest data is automatically imported into the system for each shipment, and helps the security personnel to quickly determine the contents of cargo. In case of a mismatch between cargo contents shown by manifest data and the X-ray images, the cargo may be withheld for further inspection. In one embodiment, the process of analyzing the X-ray image of the cargo in conjunction with the manifest data is automated.

Abstract: Systems and methods are provided for generating labeled image data for improved training of an image classifier, such as a multi-layered machine learning model configured to identify target image objects in image data. When the initially trained classifier is unable to identify a particular object in input image data, such as an object that did not appear in initial training data, feature information determined by the classifier for the given image data may be provided to a clustering model. The clustering model may group image data having similar features into different clusters or groups, which may in turn be labeled at the group level by an annotator. The image data assigned to the different clusters, along with the associated labels, may subsequently be used as training data for training a classifier to identify the labeled objects in images.

Abstract: The invention provides a method for generating a compound ultrasound image. The method includes acquiring and beamforming channel data. Using the beamformed channel data a plurality of images, each image comprising a plurality of pixels, of a region of interest are obtained and an image information metric, wherein the image metric is associated with a pixel of the plurality of pixels, is assessed. The acquiring of the plurality of images and the assessment of the image metric are performed in parallel. For each image of the plurality of images: a per-pixel weighting for each pixel of the plurality of pixels based on the assessment of the image information metric is determined and applied to each pixel of the plurality of pixels. Finally a compound ultrasound image is generated based on the plurality of weighted pixels of the plurality of images.

Abstract: Among other things, one or more systems and/or techniques for classifying an item disposed within an object are provided herein. A three-dimensional image of the object (e.g., a bag) is segmented into a set of item representations (e.g., laptop, thermos, etc.). An item is identified from the set of item representations based upon item features of the item, such as the laptop that could be used to conceal an item of interest such as an explosive. A region comprising a three-dimensional image of the item is divided into a set of sub-regions (e.g., a first sub-region encompassing a screen, a second sub-region encompassing a motherboard, etc.). The item is classified as a potential first type of item (e.g., an explosive laptop) when any sub-region has a number of voxels, with computed tomography (CT) values within a range of known CT values for a first type of item, exceeding a threshold.

Abstract: In various examples, frames of a video may include a first visual object that may appear relative to a second visual object within a region of the frames. Once a relationship between the first visual object and the region is known, one or more operations may be performed on the relative region. For example, optical character recognition may be performed on the relative region where the relative region is known to contain textual information. As a result, the identification of the first visual object may serve as an anchor for determining the location of the relative region including the second visual object—thereby increasing accuracy and efficiency of the system while reducing run-time.

Abstract: An image analysis (“IA”) computer system for analyzing images of hail damage includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) store a damage prediction model associated with a rooftop, wherein the damage prediction model utilizes an artificial intelligence algorithm; (ii) display, to a user, an image of a rooftop; (iii) receive, from the user, a request to analyze damage to the rooftop; (iv) apply, by the at least one processor, the damage prediction model to the image, the damage prediction model outputting a plurality of damage prediction locations of the rooftop in relation to the image; and/or (v) display, by the at least one processor, an overlay box at each of the plurality of damage prediction locations, the overlay box being a virtual object overlaid onto the image for labeling the damage prediction locations.

Abstract: A method, apparatus, and computer-readable medium, for assessing image quality of an image produced by a scanning imaging system. The method comprises acquiring (S10) image data of an image produced by the scanning imaging system and calculating (S20 to S40), for each section of the image: a respective first value measuring at least one of sharpness or contrast of at least a part of the section, the measuring depending on noise, a respective second value measuring noise in at least a part of the section, and a respective third value indicating image quality, by combining the first and second values. The combining is such that calculated third values have a weaker dependency on the noise than the first values. The method further comprises determining (S50) a quality score that is indicative of image quality of the image based on a variation of the calculated third values among the sections.

Abstract: For processing a medical image, medical image data representing a medical image of at least a portion of a vertebral column is received. The medical image data is processed to determine a plurality of positions within the image. Each of the plurality of positions corresponds to a position relating to a vertebral bone within the vertebral column. Data representing the plurality of positions is processed to determine a degree of deformity of at least one vertebral bone within the vertebral column.

Abstract: Disclosed are systems, methods, and computer-readable media for a hybrid cloud structure for machine-learning based object recognition. In one aspect, a system includes one or more video-capable access points; and one or more processors configured to receive image data from the one or more video-capable access points; perform, at a first processor of the one or more processors, a first process to detect one or more objects of interest in the image data; generate vector IDs for one or more objects detected in the image data; perform, at a second processor of the one or more processors, a second process to identify the one or more objects in the vector IDs; and generate at least one offline trail for the one or more objects based on statistics associated with the one or more objects identified.

Abstract: Systems and methods are provided for screening histopathology tissue samples. An anomaly detection system is trained on a plurality of training images. Each of the plurality of training images represents a tissue sample that is substantially free of abnormalities. A test image, representing a tissue sample, is provided to the anomaly detection system. A deviation from normal score is generated for at least a portion of the test image. The deviation from normal score represents a degree of abnormality in the tissue sample represented by the test image.

Abstract: Methods and systems of registering a model of one or more anatomic passageways of a patient to a patient space are provided herein. An exemplary method may include accessing a set of model points of the model of the passageways, the model points being associated with a model space, collecting measured points along a length of a catheter inserted into the passageways of the patient, the measured points determined by a shape of the catheter, and assigning points of the first set to a plurality of subsets. The exemplary method may further include registering each of the subsets with the model points to produce a plurality of registration candidates, comparing the candidates to identify an optimal subset associated with an optimal registration of the plurality of candidates that translates the set of model points and at least one set of the sets of measured points into a common space.