Abstract: A matching position output system includes: a shooting means configured to shoot an image of an object; a generating means configured to generate information identifying the object from the shot image of the object; and an output means configured to, based on the generated information identifying the object, acquire information of a matching position of the object from a recording means configured to associate and record the information identifying the object and the information of the matching position of the object, and output the information of the matching position of the object.

Abstract: A robot including a medium storing instructions that when executed by a processor of the robot effectuates operations including: capturing images of a workspace as the robot moves within the workspace; identifying at least one characteristic of an object captured in the images of the workspace; determining an object type of the object based on an object dictionary of different types of objects, wherein the different object types comprise at least a cord, clothing garments, a shoe, earphones, and pet bodily waste; and instructing the robot to execute at least one action based on the object type of the object, wherein the at least one action comprises avoiding the object or cleaning around the object.

Abstract: Disclosed herein are methods and system for detecting road marking expressed using alternating infrared reflective tiles comprising high infrared reflective tiles and low infrared red reflective tiles painted on a road surface using paint material(s) characterized by: (1) reflecting light in visible light spectral range deviating less than a first value from the light reflected by the road surface and (2) reflecting light in an infrared spectral range deviating more than a second value from the light reflected by the road surface. Infrared image(s) and visible light image(s) of the road surface which are registered to each other may be analyzed to compute an infrared reflective value and a luminance value for each pixel respectively. A ratio may be computed between the infrared reflective value of and the luminance value of corresponding pixels to identify high and low infrared reflective tiles in pixels having a ratio exceeding a third value.

Abstract: Logo candidates for a specific ISO data can be identified from external resources based on the enriched merchant data. Low quality images of the logo candidates are filtered out with image analysis including entropy ratio evaluations of the logo candidates. Also, the logo candidates are processed with high quality filtering including classification of the logo candidates with a deep learning classifier for distinguishing logos from non-logos. A logo from the logo candidates is selected to associate with the ISO data packets. A display having the selected logo associated with a transaction of the ISO data packets can be generated for display to users.

Abstract: An information prompt method includes collecting an image of an ambient environment of a user to generate a real-scenario image, obtaining information about a point of interest from an electronic map, identifying the real-scenario image to determine a target object, determining a label of the target object and a display position of the label in the real-scenario image based on the information about the point of interest and position information of the electronic device, and displaying an interface including the label and the real-scenario image, where the label is displayed in an overlay mode at the display position in the real-scenario image.

Abstract: In some aspects, systems and methods for efficiently clustering a large-scale dataset for improving the construction and training of machine-learning models, such as neural network models, are provided. A dataset used for training a neural network model configured can be clustered into a first set of clusters and a second set of clusters. The neural network model can be constructed with a number of nodes in a hidden layer that is based on the number of clusters in the first set of clusters. The neural network can be trained based on training samples selected from the second set of clusters. In some aspects, the trained neural network model can be utilized to satisfy risk assessment queries to compute output risk indicators for target entities. The output risk indicator can be used to control access to one or more interactive computing environments by the target entities.

Abstract: Training a machine learning model (e.g., a neural network model such as a convolutional neural network (CNN) model) so that, when trained, the model can be utilized in processing vision data (e.g., from a vision component of a robot), that captures an object, to generate a rich object-centric embedding for the vision data. The generated embedding can enable differentiation of even subtle variations of attributes of the object captured by the vision data.

Abstract: A biometric sensor device system including a biometric sensor device, and a prediction computer. The biometric sensor device includes at least one camera, and a biometric sensor device processor configured to record a time synchronized communicative interaction between participants, by controlling at least one camera to record the participants over a time period, and transfer the recorded communicative interaction to the prediction computer. The prediction computer including a prediction computer processor configured to extract, from the recorded communicative interaction, a physical characteristic of each of the participants over the time period, compare the physical characteristic of at least one of the participants with the physical characteristic of at least another one of the participants over the time period, and classify or score at least one of the participants according to a predetermined classification or dimensional scoring scheme based on the comparison.

Abstract: Disclosed herein are methods and systems for painting driving assistance markings using one or more paint materials which are visible in a plurality of light spectral ranges, in particular, visible light and one or more infrared light spectral ranges. Further disclosed are methods and systems for analyzing images captured in multiple spectral ranges to identify the driving assistance markings and/or part thereof in a plurality of different spectral ranges and identify aggregated driving assistance markings by aggregating the driving assistance markings identified in the plurality of different spectral ranges. Also disclosed herein are methods and systems for presenting and detecting enhanced driving assistance markings on one or more elements under one or more paint materials which are highly transparent in one or more infrared spectral ranges while reflecting visible light conforming to a color of the element(s) surface thus not affecting appearance of the element(s) in the visible light spectrum.

Abstract: A method serves for MR-based reconstruction of images of a patient. Whether a value of a movement of the patient in at least one motion direction during an MR scan exceeds a respective threshold value is monitored. If this is not the case, an image reconstruction is performed by a Wave-CAIPI method on the basis of identical calibrated PSF subfunctions for all k-space lines. When this is the case, a number of bins are provided that correspond to sequential value ranges of the patient movement in at least one motion direction, the k-space lines are assigned to the bins based on a movement value determined during their respective acquisition, a calibration of PSF subfunctions is performed for at least two bins on the basis of the k-space lines assigned to said bins, and an image reconstruction is performed by a Wave-CAIPI method in such a way that the PSF subfunctions associated with the assigned bins are used for the respective k-space lines.

Abstract: Examples provide a self-supervised language model for document-to-document similarity scoring and ranking long documents of arbitrary length in an absence of similarity labels. In a first stage of a two-staged hierarchical scoring, a sentence similarity matrix is created for each paragraph in the candidate document. A sentence similarity score is calculated based on the sentence similarity matrix. In the second stage, a paragraph similarity matrix is constructed based on aggregated sentence similarity scores associated with the first candidate document. A total similarity score for the document is calculated based on the normalize the paragraph similarity matrix for each candidate document in a collection of documents. The model is trained using a masked language model and intra-and-inter document sampling. The documents are ranked based on the similarity scores for the documents.

Abstract: The present disclosure provides a visual perception method and apparatus, a perception network training method and apparatus, a device and a storage medium. The visual perception method recognizes the acquired image to be perceived with a perception network to determine a perceived target and a pose of the perceived target, and finally determines a control command according to a preset control algorithm and the pose, so as to enable an object to be controlled to determine a processing strategy for the perceived target according to the control command. According to the perception network training method, acquire image data and model data, then generate an edited image with a preset editing algorithm according to a 2D image and a 3D model, and finally train the perception network to be trained according to the edited image and the label.

Abstract: Systems and methods are disclosed for processing an electronic image corresponding to a specimen. One method for processing the electronic image includes: receiving a target electronic image of a slide corresponding to a target specimen, the target specimen including a tissue sample from a patient, applying a machine learning system to the target electronic image to determine deficiencies associated with the target specimen, the machine learning system having been generated by processing a plurality of training images to predict stain deficiencies and/or predict a needed recut, the training images including images of human tissue and/or images that are algorithmically generated; and based on the deficiencies associated with the target specimen, determining to automatically order an additional slide to be prepared.

Abstract: Provided is an information processing apparatus including: an output control unit that controls display of information regarding an erroneous recognition target different from a predetermined recognition target, the erroneous recognition target being specified as having a possibility of erroneous recognition on the basis of a result of recognition processing on at least one piece of specifying data by a recognizer generated for recognizing the predetermined recognition target and a result of clustering processing on a target recognized by the recognition processing. The recognizer is generated by learning based on at least one piece of learning data. The at least one piece of learning data includes the predetermined recognition target and is data in substantially the same context. The specifying data is data in substantially the same context as the context of the at least one piece of learning data.

Abstract: This invention provides a system and method for finding multiple line features in an image. Two related steps are used to identify line features. First, the process computes x and y-components of the gradient field at each image location, projects the gradient field over a plurality subregions, and detects a plurality of gradient extrema, yielding a plurality of edge points with position and gradient. Next, the process iteratively chooses two edge points, fits a model line to them, and if edge point gradients are consistent with the model, computes the full set of inlier points whose position and gradient are consistent with that model. The candidate line with greatest inlier count is retained and the set of remaining outlier points is derived. The process then repeatedly applies the line fitting operation on this and subsequent outlier sets to find a plurality of line results. The process can be exhaustive RANSAC-based.

Abstract: A system uses range and Doppler velocity measurements from a lidar system and images from a video system to estimate a six degree-of-freedom trajectory (6DOF) of a target. The 6DOF transformation parameters are used to transform multiple images to the frame time of a selected image, thus obtaining multiple images at the same frame time. These multiple images may be used to increase a resolution of the image at each frame time, obtaining the collection of the superresolution images.

Abstract: In a data synchronization device (20), a first estimation unit (51) estimates as a first data range, a data range in which a vehicle is turning right or left, in moving image data (33). A second estimation unit (52) estimates as a second data range, a data range in which the vehicle is turning right or left, in acceleration data (34). A synchronization unit (53) performs a time-synchronization between the moving image data (33) and the acceleration data (34) based on a time range corresponding to the first data range estimated by the first estimation unit (51) and a time range corresponding to the second data range estimated by the second estimation unit (52).

Abstract: Techniques are provided for using one or more machine learning systems to process input data including image data. The input data including the image data can be obtained, and at least one machine learning system can be applied to at least a portion of the image data to determine at least one color component value for one or more pixels of at least the portion of the image data. Based on application of the at least one machine learning system to at least the portion of the image data, output image data for a frame of output image data can be generated. The output image data includes at least one color component value for one or more pixels of the frame of output image data. Application of the at least one machine learning system causes the output image data to have a reduced dimensionality relative to the input data.

Abstract: A product defect detection method, device and system are disclosed.

Abstract: A computing entity is described that obtains at least one inspection image of an at least partially fabricated product and causes the at least one inspection image to be processed by a product inspection engine. The product inspection engine includes a machine learning-trained model. The computing entity obtains an inspection result determined based on the processing of the at least one inspection image by the product inspection engine; identifies one or more training images stored in an image database based at least in part on the at least one inspection image; associates automatically generated labeling data with the one or more training images based at least in part on the inspection result determined by the processing of the at least one inspection image; and causes training of the product inspection engine using the one or more training images and the associated labeling data.