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.

Abstract: A method and system for tracking movements of objects in a sports activity are provided. The method includes matching video captured by at least one camera with sensory data captured by each of a plurality of tags, wherein each of the at least one camera is deployed in proximity to a monitored area, wherein each of the plurality of tags is disposed on an object of a plurality of monitored objects moving within the monitored area; and determining, based on the video and sensory data, at least one performance profile for each of the monitored objects, wherein each performance profile is determined based on positions of the respective monitored object moving within the monitored area.

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices associated with object association and tracking are provided. Input data can be obtained. The input data can be indicative of a detected object within a surrounding environment of an autonomous vehicle and an initial object classification of the detected object at an initial time interval and object tracks at time intervals preceding the initial time interval. Association data can be generated based on the input data and a machine-learned model. The association data can indicate whether the detected object is associated with at least one of the object tracks. An object classification probability distribution can be determined based on the association data. The object classification probability distribution can indicate a probability that the detected object is associated with each respective object classification. The association data and the object classification probability distribution for the detected object can be outputted.

Abstract: This method for encoding a 3D point cloud comprises: —segmenting the point cloud into first patches (2); —determining inverse projection parameters of the patches (4); —projecting the first patches to planes using the inverse projection parameters (10); —encoding the planes (12); —determining sparse points in the point cloud not belonging to any patch (HC, 18); and—encoding the sparse points using an octree-decomposition (20).

Abstract: According to one embodiment, a medical image diagnostic apparatus includes processing circuitry. The processing circuitry inputs a noise correlation map and a medical image or an intermediate image to a learned model that is functioned to generate a denoise image, in which noise of the medical image or noise of the intermediate image is reduced, based on the medical image generated based on data collected with respect to a subject or the intermediate image at a front stage for generating the medical image and the noise correlation map correlated with the noise included in the medical image or the intermediate image, and generates the denoise image, in which the noise of the medical image or the noise of the intermediate image is reduced.

Abstract: Disclosed in the embodiments of the present application are method and apparatus for identifying damaged part of vehicle, server, client and system. The method comprises: pre-establishing identifiable vehicle feature library including vehicle parts and feature correspondence library including relative positional relationships of these parts. During the loss assessment image capturing procedure, the user manually delineates the damage location on the client. The identifiable vehicle features in the image can be identified by the server, the relative positions of the identifiable features and the mark delineated by user are determined according to these identifiable features.

Abstract: This disclosure provides an image recommendation method, a client, a server, a computer system and a medium. The method includes acquiring an image, sending the acquired image to a server, such that the server extracts a scale invariant feature of the acquired image and performs retrieval of identical images according to the scale invariant feature of the acquired image, and such that the server extracts a multi-scale feature of the acquired image and performs retrieval of similar images according to the multi-scale feature of the acquired image, and receiving at least one recommended image. The at least one recommended image is based on result of the identical image retrieval and result of the similar image retrieval.

Abstract: An enhanced imaging system for a motor vehicle includes a vision processing module that generates a data signal, in response to the camera capturing at least one of the video and the image. A graphics processor is configured to tone map the video or image to generate an RGB histogram including an overall tonal range. The processor is further configured to compare the overall tonal range to a tonal threshold. The processor detects one or more objects having a boundary that separates an interior region having an object tonal value from an exterior region having an exterior tonal value. The processor generates an optimized signal for increasing a difference between the object tonal value and the exterior tonal value, in response to the overall tonal range being above the tonal threshold. The system can further include a display device for displaying an optimized video or image.

Abstract: System and methods are provided for improved visual search systems that can use local features of images. The search system provides a user interface that enables a user to select areas or portions of an image to search on the selected features and the overall appearance of the image. The search system further provides customized user interfaces to exclude certain portions of images from the search algorithms. The search system can be used to detect potential intellectual property risks associated with items in an electronic catalog.