Abstract: To identify and track a plurality of objects. An object tracking device 1 includes a frame acquisition unit 31 for acquiring one set of frame data 11 included in moving image data that captures a space where a plurality of objects are present; a distance acquisition unit 32 for acquiring measurement data 12 from measurement of three-dimensional coordinates of points configuring the objects in the space where the objects are present; an object position detection unit 33 for detecting a two-dimensional position of each of the objects from the frame data 11 on the basis of comparison with template data of each of the objects; an object distance detection unit 34 for detecting a plurality of three-dimensional positions of the objects from the measurement data 12; and an associating unit 35 for associating each two-dimensional position from the object position detection unit 33 with the nearest three-dimensional position among the three-dimensional positions from the object distance detection unit 34.

Abstract: Various embodiments of present invention provide systems, methods, and computer-program products containing executable code for processing a package for shipping with a common carrier. Various embodiments include a wireless computing device that may be one of several types of devices such as smartphones, mobile telephones, mobile computers, portable digital assistants, laptop computers, gaming devices, electronic tablets, or other types of similar devices that may be used to facilitate the shipping and expedited tracking of objects having a tracking number via multimedia capture and processing.

Abstract: An image recognition system includes a cloud server that stores a cloud recognition model and a local server that stores a local recognition model. The local server recognizes an image through the local recognition model to generate a local recognition result. The image corresponds to a field, the image includes at least one object, and the local recognition result includes local category confidence scores of each object corresponding to each of a plurality of categories. For each object, the local server calculates a local entropy indicator according to the field and the corresponding local category confidence scores. The local server determines to transmit the image to the cloud server according to the local entropy indicators of the image. The cloud server recognizes the image through the cloud recognition model to generate a cloud recognition result, and updates the local recognition model according to the cloud recognition result.

Abstract: A particle velocimetry system and algorithm are provided for executing a particle reconstruction to determine three-dimensional positions of particles in a particle laden fluid flow from two-dimensional flow images generated by two-dimensional image sensors; generate a three-dimensional particle distribution from the three-dimensional position; and execute a recursive loop for performing further iterations of particle reconstruction and generation of three-dimensional particle distributions, with recursive iterations of particle reconstruction executed with the use of particle property data obtained from the prior executed iteration of particle reconstruction.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for object detection and identification. The method, computer program product and computer system may include computing device which may receive an image from a user device. The image may be a screenshot captured by the user device from a display. The computing device may classify the image based on features present in the image and detect a salient object contained within the image. The computing device may identify the object in the image and one or more sources of the object in the image.

Abstract: A multi-task hierarchical machine learning model is configured to perform both a decision task to predict a decision result and an explanation task to predict a plurality of semantic concepts for explainability associated with the decision task, wherein a semantic layer of the machine learning model associated with the explanation task is utilized as an input to a subsequent decision layer of the machine learning model associated with the decision task. Training data is received. The multi-task hierarchical machine learning model is trained using the received training data.

Abstract: A radiation image processing device (100) includes an image processor (6) configured to calculate an adjustment coefficient (Iideal/Ireal) for adjusting a signal reduction rate (Dscat) due to removal of a scattered component in a subject (S) of radiation transmitting through the subject (S) acquired based on a luminance value (Inogrid) of a radiation image (Xnogrid) captured with the radiation radiated to the subject (S) without a grid (8) being arranged, the luminance value (Inogrid) of the radiation image (Xnogrid) containing a signal of a scattered ray.

Abstract: The method includes: determining a reference sensor from at least two sensors and sampling moments of the reference sensor; when obtaining observation data of a sensor K at a moment t1, determining a type of the sensor K; if the sensor K is a first sensor of a first type, determining a sampling moment T1 that is closest to t1 and an interpolation coefficient ?1, and calculating first rotation data and first translation data that are at T1, and a first covariance matrix; if the sensor K is a second sensor of a second type, determining two sampling moments T2 and T3 that are closest to t1 and interpolation coefficients ?2 and ?3, and calculating second rotation data and second translation data that are of the second sensor from T2 to T3, and a second covariance matrix; merging rotation data, translation data, and covariance matrices that are at the sampling moments.

Abstract: A method for identifying a risk merchant comprises: determining, for each of at least one marked merchant cluster based on a user correlation degree of a merchant to be identified and the each marked merchant cluster, a degree of similarity between the merchant to be identified and the marked merchant cluster, wherein each marked merchant in the each marked merchant cluster is marked with a corresponding risk category by a trained classification model, and the user correlation degree of the merchant to be identified and the marked merchant cluster comprises a user correlation coefficient of the merchant to be identified and the marked merchant cluster and a number of same users of the merchant to be identified and the marked merchant cluster; and determining whether the merchant to be identified is a risk merchant based on the degree of similarity between the merchant to be identified and the each marked merchant cluster.

Abstract: The present disclosure provides a method for detecting illegal parking. The method includes: obtaining a detection frame having a category of vehicle from an image acquired by an acquisition module through a first neural network to determine coordinate information of the detection frame; determining whether an area corresponding to the detection frame in the image is an illegal parking area through a second neural network; and when determining that the area is the illegal parking area, determining whether the coordinate information of the detection frame is changed within a preset period of time to determine whether the vehicle is parked illegally.

Abstract: ADVANCED RESPONSE PROCESSING IN WEB DATA COLLECTION discloses processor-implemented apparatuses, methods, and systems of processing unstructured raw HTML responses collected in the context of a data collection service, the method comprising, in one embodiment, receiving raw unstructured HTML documents and extracting text data with associated meta information that may comprise style and formatting information. In some embodiments data field tags and values may be assigned to the text blocks extracted, classifying the data based on the processing of Machine Learning algorithms. Additionally, blocks of extracted data may be grouped and re-grouped together and presented as a single data point. In another embodiment the system may aggregate and present the text data with the associated meta information in a structured format. In certain embodiments the Machine Learning model may be a model trained on a pre-created training data set labeled manually or in an automatic fashion.

Abstract: Embodiments of the present invention provide a model parameter fusion method and apparatus, which relate to the field of machine learning and intend to reduce a data transmission amount and implement dynamical adjustment of computing resources during model parameter fusion. The method includes: dividing, by an ith node, a model parameter of the ith node into N blocks, where the ith node is any node of N nodes that participate in a fusion, and 1?i?N?M; receiving, by the ith node, ith model parameter blocks respectively sent by other nodes of the N nodes than the ith node; fusing, by the ith node, an ith model parameter block of the ith node and the ith model parameter blocks respectively sent by the other nodes, so as to obtain the ith general model parameter block; and distributing, by the ith node, the ith general model parameter block to the other nodes of the N nodes.

Abstract: The present disclosure describes method, apparatus, and storage medium for obtaining object information. The method includes obtaining a to-be-tracked image comprising at least one object and at least one reference image comprising a plurality of objects; extracting a to-be-tracked image block comprising a plurality of to-be-tracked points from the to-be-tracked image and extracting a reference image block comprising a plurality of reference points from a reference image of the at least one reference image; constructing a point transformation relationship between the to-be-tracked image block and the reference image block based on a position relationship between the plurality of to-be-tracked points and a position relationship between the plurality of reference points; and obtaining a position of a reference point in the reference image corresponding to a to-be-tracked point based on the point transformation relationship, to determine an object in the reference image corresponding to the at least one object.

Abstract: A method for determining landmarks in an environment of a vehicle is described that comprises capturing of initial environmental data, and determining a least a subset of the initial environmental data. The determined subset is assigned a category from a plurality of different categories. Depending on a landmark type, a category is determined, and landmark detection for the landmark type is carried out restricted to the subset of the determined category. A corresponding system for determining landmarks in an environment of a vehicle is also described.

Abstract: A computer-implemented method for generating a feature descriptor for a location in an image for use in performing descriptor matching in analysing the image, the method comprising determining a set of samples characterising a location in an image by sampling scale-space data representative of the image, the scale-space data comprising data representative of the image at a plurality of length scales; and generating a feature descriptor in dependence on the determined set of samples.

Abstract: One embodiment provides for a method of transmitting data between multiple compute nodes of a distributed compute system, the method comprising multi-dimensionally partitioning data of a feature map across multiple nodes for distributed training of a convolutional neural network; performing a parallel convolution operation on the multiple partitions to train weight data of the neural network; and exchanging data between nodes to enable computation of halo regions, the halo regions having dependencies on data processed by a different node.

Abstract: Embodiments of a system and method for sorting and delivering articles in a processing facility based on image data are described. Image processing results such as rotation notation information may be included in or with an image to facilitate downstream processing such as when the routing information cannot be extracted from the image using an unattended system and the image is passed to an attended image processing system. The rotation notation information may be used to dynamically adjust the image before presenting the image via the attended image processing system.

Abstract: A checking device for a label, with a processor for the detection of the label and a layout unit for the creation of a layout of the label from layout data, wherein the checking device comprises a data converter device configured in such a way that it generates test data from layout data in memory in order to control the processor. A data converter device for use in a checking device, wherein the data converter device is configured in such a way that it generates test data from layout data in memory for checking the label.

Abstract: Systems and methods for estimating a height of an object from a monocular image are described herein. Objects are detected in the image, each object being indicated by a region of interest. The image is then cropped for each region of interest and the cropped image scaled to a predetermined size. The cropped and scaled image is then input into a convolutional neural network (CNN), the output of which is an estimated height for the object. The height may be represented by a mean of a probability distribution of possible sizes, a standard deviation, as well as a level of confidence. A location of the object may be determined based on the estimated height and region of interest. A ground truth dataset may be generated for training the CNN by simultaneously capturing a LIDAR sequence with a monocular image sequence.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for object detection and identification. The method, computer program product and computer system may include computing device which may receive an image from a user device. The image may be a screenshot captured by the user device from a display. The computing device may classify the image based on features present in the image and detect a salient object contained within the image. The computing device may identify the object in the image and one or more sources of the object in the image.