Abstract: An approach is provided in which the approach receives an image that includes multiple image points and constructs a plane in the image based on a first subset of the plurality of image points. The approach identifies a second subset of the image points that belong to the plane and are not part of the first subset of image points, and removes the first subset of image points and the second subset of image points form the image points. The approach annotates the remaining subset of image points in the image.

Abstract: In an aspect for generating a 2-dimensional (2D) building mapping, a computer-implemented method may include one or more processors configured for receiving scanning data in a first file format corresponding to 3-dimensional (3D) aerial Light Detection and Ranging (LiDaR) images. Further, the method may include transforming the first file format to a second file format and transmitting the scanning data to a building semantic segmentation component. Further, the computer-implemented method may include generating semantic segmentation data corresponding to building structures defined by point clusters; generating instance segmentation data corresponding to top surfaces of the building structures; generating building boundary data for each one of the point clusters based on one or more of the semantic segmentation data and the instance segmentation data; and processing the building boundary data to generate a 2D building mapping comprising refined boundary lines for each of the point clusters.

Abstract: A system, method, and computer program product provides a way to separate connected or adhered adjacent characters of a digital image for license plate recognition. As a threshold processing, the method performs a recognition of character adhesion by obtaining character parameters using an image processor. The parameters include a horizontal max crossing and a ratio of width and height. A first rule-based module is used responsive to the character parameters to distinguish the adhered characters (character adhesions) that are easy to judge, leaving the uncertain part to a character adhesion classifier model for discrimination. Character adhesion data is obtained by data augmentation including the adding of a random distance between two single characters to create class like adhered characters. Then the character adhesion classifier model of single character and character adhesion data is trained. Any uncertain part can be distinguished by the trained character adhesion classifier model.

Abstract: Provided are a display panel, a preparation method thereof and a display device. The display panel includes a substrate; a driving circuit layer, where the driving circuit layer is disposed on a side of the substrate, and includes a plurality of driving circuits; and an LED element layer, where the LED element layer is disposed on a side of the driving circuit layer facing away from the substrate, and includes a plurality of micro-LED elements, where each of the plurality of micro-LED elements is electrically connected to a respective one of the plurality of driving circuits through a via hole.

Abstract: Aspects of the subject disclosure may include, for example, a device having a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations including receiving encrypted hypertext transport protocol (HTTPS) traffic including media content; separating the HTTPS traffic into audio segments and video segments; calculating a size for each audio segment in the HTTPS traffic; maintaining a sliding window of a plurality of sizes of consecutive audio segments to form a fingerprint; and identifying the media content by matching the fingerprint with a reference in a catalog. Other embodiments are disclosed.

Abstract: An LED module, an LED source substrate, a display panel, and a display apparatus are provided. The LED module includes N LED chips. N is an integer not smaller than 2. The N LED chips share one first electrode. At least two of the N LED chips each include a second electrode.

Abstract: A method for detecting and labeling a target object in a 2D image includes receiving a plurality of 2D images from a visual sensor, manually marking points of the target object on each of the 2D images, generating from the 2D images a 3D world coordinate system of the environment surrounding the target object, mapping each of the marked points on the 2D images to the 3D world coordinate system using a simultaneous localization and mapping (SLAM) engine, automatically generating a 3D bounding box covering all the marked points mapped to the 3D world coordinate system, mapping the 3D bounding box to each of the 2D images, generating a label for the target object on each of the 2D images using a machine learning object detection model, and training the machine learning object detection model based on the generated label for the target object.

Abstract: Facilitating pre-positioning of streaming content onto communication devices is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise facilitating an identification of a first multimedia content stream and a second multimedia content stream. The identification can be based on a usage preference of a defined entity associated with a communication device. The operations can also comprise, in response to a determination that an available network satisfies a defined network criterion, facilitating pre-positioning, at a data store of the communication device, the first multimedia content stream and the second multimedia content stream.

Abstract: A display panel, a display device and a method for driving a display panel are provided. The display panel includes N types of display areas which includes an i-th type display area and a j-th type display area. The display panel includes M display parts which include a first display part and a second display part. The first display part includes at least one i-th type display area, and the second display part includes at least one i-th type display area. At least one j-th type display area is arranged between the i-th type display area included in the first display part and the i-th type display area included in the second display part. Light-emitting time periods of the i-th type display area and the j-th type display area at least partially do not overlap, to reduce the number of sub-pixels driven at the same time period.

Abstract: A processor may receive an image of the collection of material having a plurality of objects. The processor may identify the anomaly from the plurality of objects. The processor may generate a bounding box for the anomaly. The processor may generate one or more picking points on the anomaly. The one or more picking points may be configured on at least one balance points of the anomaly. The processor may remove the anomaly from the collection of material via the one or more picking points.

Abstract: The present disclosure relates to training a machine learning model to classify images. An example method generally includes receiving a training data set including images in a first category and images in a second category. A convolutional neural network (CNN) is trained using the training data set, and a feature map is generated from layers of the CNN based on features of images in the training data set. A first area in the feature map including images in the first category and a second area in the feature map where images in the first category overlap with images in the second category are identified. The first category is split into a first subcategory corresponding to the first area and a second subcategory corresponding to the second area. The CNN is retrained based on the images in the first subcategory, images in the second subcategory, and images in the second category.

Abstract: Facilitating pre-positioning of streaming content onto communication devices is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise facilitating an identification of a first multimedia content stream and a second multimedia content stream. The identification can be based on a usage preference of a defined entity associated with a communication device. The operations can also comprise, in response to a determination that an available network satisfies a defined network criterion, facilitating pre-positioning, at a data store of the communication device, the first multimedia content stream and the second multimedia content stream.

Abstract: A method, a device and a computer program product for image processing are proposed. In the method, a first training image and region information are obtained. The region information indicates a region of a defect in the first training image. A second training image with the defect at least partially removed is generated using an image generator based on the first training image and the region information. The image generator is trained to recover the first training image by replacing pixels included in the region indicated by the region information. The image generator is updated based on the second training image. In this way, the image including the defect can be accurately and efficiently recovered.

Abstract: A plurality of different images of a same region of interest in an object are input into a set of neural networks, wherein each image of the region has been captured under a different value of a variable condition. A classification for each image is generated by the set of neural networks, wherein each classification includes a confidence score in a prediction of whether a feature is present in the region. The image classifications are ensembled to generate a final classification for the region. By applying a loss function, a loss is computed based on comparing the final classification to a ground truth of whether the feature is present in the region. The parameters of the set of neural networks are adjusted based on the computed loss.

Abstract: A method for providing a service based on a user scenario, a terminal device, and a system. The terminal device perceives the user scenario with low power consumption, and a cloud-side service open platform determines a corresponding scenario package based on the user scenario, where the scenario package includes a plurality of pre-orchestrated services for the user scenario. The service open platform pushes data related to the determined scenario package to the terminal device, and the terminal device displays entry interfaces of the plurality of services, and provides a corresponding service based on an operation performed by a user on the entry interface.

Abstract: Chip package structure and chip package method are provided. The chip package structure includes an encapsulating layer, a redistribution layer, a soldering pad group, and bare chips. Connecting posts is formed on a side of the bare chips. The encapsulating layer covers the bare chips and the connecting posts, while exposes a side of the connecting posts away from the bare chips. The redistribution layer on the connecting posts includes a first redistribution wire, a second redistribution wire, and a third redistribution wire. The first redistribution wire and the second redistribution wire are electrically connected to at least one connecting post respectively, and the third redistribution layer is electrically connected to remaining connecting posts. The soldering pad group on the redistribution layer includes an input soldering pad electrically connected to the first redistribution wire and an output soldering pad electrically connected to the second redistribution wire.

Abstract: A method for providing a service based on a user scenario, a terminal device, and a system. The terminal device perceives the user scenario with low power consumption, and a cloud-side service open platform determines a corresponding scenario package based on the user scenario, where the scenario package includes a plurality of pre-orchestrated services for the user scenario. The service open platform pushes data related to the determined scenario package to the terminal device, and the terminal device displays entry interfaces of the plurality of services, and provides a corresponding service based on an operation performed by a user on the entry interface.

Abstract: Provided are a display panel, a preparation method thereof and a display device. The display panel includes a substrate; a driving circuit layer, where the driving circuit layer is disposed on a side of the substrate, and includes a plurality of driving circuits; and an LED element layer, where the LED element layer is disposed on a side of the driving circuit layer facing away from the substrate, and includes a plurality of micro-LED elements, where each of the plurality of micro-LED elements is electrically connected to a respective one of the plurality of driving circuits through a via hole.

Abstract: Embodiments of the present disclosure relate to object defect detection. In an embodiment, a computer-implemented method is disclosed. According to the method, for a test image of at least one part of a target object, a reference image is generated by repeating a periodic pattern detected in the test image, the target object consisting of elements. A differential image is determined by comparing the test image and the reference image. The differential image is superimposed on a predefined grid image to obtain a superimposed image. The grid image comprises grids corresponding to elements of a reference object associated with the target object. The number of defective elements is determined in the at least one part of the target object based on the superimposed image. In other embodiments, a system and a computer program product are disclosed.

Abstract: A method, a device and a computer program product for image processing are proposed. In the method, whether a first image indicates a defect associated with a target object is determined. In response to determining that the first image indicates the defect, a second image absent from the defect is obtained based on the first image. The defect is identified by comparing the first image with the second image. In this way, the defect associated with the target object in the image can be accurately and efficiently identified or segmented.