Abstract: In a measurement device, a controller causes an imaging unit to generate a plurality of first pictures based on a first image and a plurality of second pictures based on a second image. The controller determines whether or not there is blurring on the basis of at least two of the plurality of first pictures. The controller determines whether or not there is blurring on the basis of at least two of the plurality of second pictures. The controller causes a measurement unit to perform measurement when the controller determines that there is no blurring in first picture blurring determination and second picture blurring determination.

Abstract: The present disclosure provides a method for training a machine learning software component. In a computing system, a plurality of images and a plurality of location tuples are received. Each of the location tuples includes a subject identifier and a temporal identifier. For each of the location tuples, the subject identifier is associated with an image of the plurality of images using the temporal identifier to form a training data set. The machine learning software component is trained with the training data set.

Abstract: A method of detecting a target includes determining a quality type of a target image captured using a camera, determining a convolutional neural network of a quality type corresponding to the quality type of the target image in a database comprising convolutional neural networks, determining a detection value of the target image based on the convolutional neural network of the corresponding quality type, and determining whether a target in the target image is a true target based on the detection value of the target image.

Abstract: An embodiment method includes: obtaining, by a first device, at least one parameter used to determine transmit power; determining, by the first device, the transmit power according to the at least one parameter; and sending, by the first device, data by using the determined transmit power. The at least one parameter includes at least one of the following parameters: a resource pool used for data transmission, a type of a to-be-sent message or service, a transmit interval or a transmit frequency of a to-be-sent message or service, a size of a message packet, a priority of a to-be-sent message or service, an ID of a to-be-sent message or service.

Abstract: A method for determining a spatial position of an object includes obtaining an image with at least one camera. The object is identified in the image. At least one of a pixel size of the object in the image and a pixel offset of the object from a center of an image plane of the at least one camera is determined. A distance between the object and the camera image plane is determined using either the pixel size and the pixel offset. The spatial position is determined using the distance and at least one known distance between the object and another element of the image.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for identifying and presenting information of a target user are presented. A live video stream comprising a target user may be displayed on a display of an augmented reality computing device. Data associated with one or more images of the target user may be sent to a facial recognition service, which may determine that a social network account matches the target user based on facial feature recognition. Information associated with the matched social network account may be received, and the live video stream on the augmented reality computing device may be augmented with a display of the received information associated with the matched social network account. In some examples, the augmented reality computing device may be augmented with one or more social network actions that are executable based on matching a target user to a social network account.

Abstract: A first image to be processed is identified, where the first image includes one or more interference factors. The one or more interference factors are removed from the first image using a plurality of different interference factor removal techniques to obtain a plurality of sample images, where each of the plurality of sample images is associated with a particular interference factor removal technique. Each sample image of the plurality of sample images is segmented into a plurality of sample sub-images based on a segmentation rule, where each sample sub-image is associated with an attribute. A plurality of target sub-images is determined from the plurality of sample sub-images, where each target sub-image comprises a combination of sample sub-images associated with a common attribute, and where each target sub-image is associated with a different attribute. The plurality of target sub-images associated with different attributes is combined into a target image.

Abstract: An embodiment of a mobile communication system includes a plurality of mobile units operating within a defined operating area, each of the mobile units having a processor, a memory for storing a mobile unit file structure, an application running on the processor for operating on the mobile unit file structure, and a receiver for receiving on a common receive communication channel data. The mobile communication system further includes a plurality of geolocation markers disposed within the defined operating area, each having a memory for storing geolocation information to define a relative position within the defined operating area, and a geolocation transmitter for transmitting the defined geolocation information on the common receive communication channel, the geolocation transmitter having a geolocation transmit range less than the defined operating area.

Abstract: Provided is a calibration device for an optical device including a two-dimensional image conversion element having a plurality of pixels and including an optical system that forms an image-forming relationship between the image conversion element and a three-dimensional world coordinate space, the calibration device including: a computer, wherein the computer is configured to: obtain calibration data representing the correspondence between two-dimensional pixel coordinates of the image conversion element and three-dimensional world coordinates of the world coordinate space; and fit a camera model representing the direction of a principal ray in the world coordinate space, corresponding to the pixel coordinates, as a function of the pixel coordinates, to the calibration data obtained, thereby calculating parameters of the camera model.

Abstract: Briefly, in accordance with one or more embodiments, a user equipment (UE) may enter into an E-UTRAN Routing Area Paging Channel state, and is configured with an E-UTRAN Routing Area and an Anchor identifier to identify an anchor evolved Node B (eNB) for the UE. The UE selects to a new cell without performing a handover procedure, and performs a cell update procedure. The UE also may enter into a Cell Update Connected state, and is configured with an Anchor identifier. The UE selects to a new cell, performs a cell update procedure, performs a buffer request procedure, and performs a cell update procedure to download buffered data and to perform data transmission with the new cell.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for facial recognition. A specific embodiment of the method includes: acquiring a to-be-recognized image; inputting the to-be-recognized image into a pre-trained first convolutional neural network to obtain complete facial feature information and partial facial feature information, the first convolutional neural network being used to extract a complete facial feature and a partial facial feature; and inputting the complete facial feature information and the partial facial feature information into a pre-trained second convolutional neural network to obtain a facial recognition result, the second convolutional neural network being used to represent a correlation between the facial recognition result, and the complete facial feature information and the partial facial feature information. This embodiment improves the accuracy of the recognition result in a situation where a face is partially covered.

Abstract: A method for identifying regions of interest (ROIs) includes receiving, by a processor from a video camera, a video image and computing, by the processor, an optical flow image, based on the video image. The method also includes computing, by the processor, a magnitude of optical flow image based on the video image and computing a histogram of optical flow magnitudes (HOFM) image for the video image based on the magnitude of optical flow image. Additionally, the method includes generating, by the processor, a mask indicating ROIs of the video image, based on the HOFM.

Abstract: Multi-radio antenna apparatuses and stations for wireless networks including multiple radios coupled to a single transmit/receive antenna, in which the antenna is highly synchronized by an external (e.g., GPS) signal. These multi-radio antenna systems may provide highly resilient links. Synchronization may allow these apparatuses to organically scale the transmission throughput while preventing data loss. The single transmit/receive antenna may have a single dish or a compound (e.g., a single pair of separate transmitting and receiving dishes) and connections for two or more radios.

Abstract: To provide a fabric type identifying apparatus capable of photographing an identifier of fabric with high resolution to correctly recognize the identifier. A fabric type identifying apparatus A for identifying a type of a fabric F provided with an identifier I includes a camera 51 for photographing the fabric F, a traversing device 53 for moving the camera 51 crosswise in a direction orthogonal to a conveying direction of the fabric F, and an image processing apparatus 52 for receiving an image input and photographed by the camera 51. The image processing apparatus 52 identifies the type of the fabric F on a basis of the identifier I included in the input image of the fabric F. The position of the camera 51 is adjustable so that the identifier I passes through a photographing area, thereby enabling to bring the camera 51 closer to the fabric F to photograph the small identifier I with high resolution, resulting in recognizing the identifier I correctly.

Abstract: A method for inspecting a container and an inspection device are disclosed. X-ray scanning is performed on the inspected container to obtain a scanned image. The scanned image is processed to obtain a region of interest. Features of texture units included in the region of interest are calculated. Local descriptions of the texture units are formed based on the features of the texture units. Distinction of each local point is calculated from a local description of each of the texture units so as to obtain a local distinct map of the region of interest. It is determined whether there is an article which is secretly carried in the inspected container using the local distinct map.

Abstract: The present disclosure provides an automatically updating vehicle classification system and method. The system comprises a processor which extracts from a vehicle image at least one of: a unique vehicle identifier from the vehicle image and visual features of the vehicle in the vehicle image. If the visual features are below a probability threshold for matching a vehicle class in a local database, the processor looks up the unique vehicle identifier in a registration database. The registration database stores vehicle registration information including unique vehicle identifiers and associated vehicle class information. If the vehicle class information associated with the vehicle identifier is not a class recognized by the processor, the processor creates a new vehicle class associated with the visual features of the vehicle.

Abstract: The invention relates to an apparatus (100) for processing a medical image (IM) of a structure of interest (SOI). The apparatus comprises a first unit (101) configured for decomposing the medical image (IM) into at least one band pass image (Bdl) and a low pass image (L); a user interface (102) arranged for enabling a user to specify an enhancement curve (EC) for the radiographic image (IM) based on at least one of (i) a metric structure length (l), (ii) a structure selectivity (s) and (iii) a structure enhancement strength (a); a second unit (103) configured for applying the enhancement curve (EC) to the at least one band pass image (Bdl) for generating at least one enhanced band pass image EBdl; and a third unit (105) configured for composing an enhanced medical image (EIM) based on the at least one enhanced band pass image (EBdl) and the low pass image (L). The invention also relates to a corresponding method of processing a medical image (IM).

Abstract: The present disclosure relates to apparatus and method for training a learning system to detect event. The present disclosure provides apparatus and method for training a learning system that includes an event related keyword collecting unit that collects an event related keyword inputted by a user, a related keyword collecting unit that collects at least one related keyword from a word database by transmitting an event related keyword to the word database, an event related image collecting unit that collects at least one event related image that is related to a search formula from an image database by transmitting the search formula to the image database, and a training unit that trains a learning system by communicating with the learning system with a use of an event related image as training data.

Abstract: A system, computer-readable medium, and method for improving semantic mapping and traffic participant detection for an autonomous vehicle are provided. The methods and systems may include obtain a two-dimensional image, obtain a three-dimensional point cloud comprising a plurality of points, perform semantic segmentation on the image to map objects with a discrete pixel color, and overlaying the semantic segmentation on the image to generate a updated image, generate superpixel clusters from the semantic segmentation to group like pixels together, project the point cloud onto the updated image comprising the superpixel clusters, and remove points determined to be noise/errors from the point cloud based on determining noisy points within each superpixel cluster.

Abstract: Disclosed are a substrate inspection apparatus and a method for displaying a component in a three-dimensional inspection of a substrate. The substrate inspection apparatus measures a substrate or an inspection target region of interest of the substrate and displays an image of components positioned within the measured region on a display unit. The image of the components displayed on the display unit may be displayed in a predetermined reference direction. The difference between the reference direction and a direction in which the actual component is disposed on the substrate is displayed in the form of a numerical value or a figure. Alternatively, the image of the component in the reference direction and the image of the actually disposed component are simultaneously displayed on a screen, and a user may convert a display method of the image by using a toggle button.