Abstract: A semiconductor apparatus examination method includes a step of detecting light from a plurality of positions in a semiconductor apparatus (D) and acquiring a waveform corresponding to each of the plurality of positions, a step of extracting a waveform corresponding to a specific timing from the waveform corresponding to each of the plurality of positions and generating an image corresponding to the specific timing based on the extracted waveform, and a step of extracting a feature point based on a brightness distribution correlation value in the image corresponding to the specific timing and identifying a position of a drive element in the semiconductor apparatus based on the feature point.

Abstract: A method of classifying a media includes receiving a media file and extracting therefrom first and second data streams including first and second media content, respectively, the media content being associated with the media item. First and second feature vectors describing the first and second media content, respectively, are generated. At least a first single feature vector representing the first sequence of first feature vectors and the second sequence of second feature vectors is generated, or at least a first single feature vector representing at least the first sequence of first feature vectors is generated. A second feature vector representing at least the second sequence of second feature vectors is generated. A probability vector from the first single feature vector, or from the first and second single feature vectors is generated. A user profile suitability class is assigned to the media item based on the probability vector.

Abstract: Systems and methods for detecting objects in a video are provided. A method can include inputting a video comprising a plurality of frames into an interleaved object detection model comprising a plurality of feature extractor networks and a shared memory layer. For each of one or more frames, the operations can include selecting one of the plurality of feature extractor networks to analyze the one or more frames, analyzing the one or more frames by the selected feature extractor network to determine one or more features of the one or more frames, determining an updated set of features based at least in part on the one or more features and one or more previously extracted features extracted from a previous frame stored in the shared memory layer, and detecting an object in the one or more frames based at least in part on the updated set of features.

Abstract: The technology described herein is directed to systems, methods, and software for indexing video. In an implementation, a method comprises identifying one or more regions of interest around target content in a frame of the video. Further, the method includes identifying, in a portion of the frame outside a region of interest, potentially empty regions adjacent to the region of interest. The method continues with identifying at least one empty region of the potentially empty regions that satisfies one or more criteria and classifying at least the one empty region as a negative sample of the target content. In some implementations, the negative sample of the target content in a set of negative samples of the target content, with which to train a machine learning model employed to identify instances of the target content.

Abstract: Methods and apparatuses to encode both high dynamic range images and low dynamic range images. The input video may also convert the high dynamic range image to an image of lower luminance dynamic range by applying either a scaling the high dynamic range image to a predetermined scale of the luma axis or by applying a sensitivity tone mapping which changes the brightnesses of pixel colors falling within a subrange or by applying a gamma function or by applying an arbitrary monotonically increasing function mapping the lumas.

Abstract: An intra prediction method and a device using the intra prediction method are provided. The intra prediction method includes the steps of: deriving a current prediction mode as a prediction mode of a current block; constructing neighboring samples of the current block with available reference samples; filtering the available reference samples; and generating predicted samples of the current block on the basis of the filtered available reference samples. The filtering step includes performing the filtering using the available reference sample located in the prediction direction of the current prediction mode and a predetermined number of available reference samples neighboring to the prediction direction of the current prediction mode.

Abstract: A dependency indication is signaled within the beginning of a packet, that is, within the adjacent of a slice header to be parsed or a parameter set. This is achieved, for example, by including the dependency indication at the beginning of the slice header, preferably after a syntax element identifying the parameter set and before the slice address, by including the dependency indication before the slice address, by providing the dependency indication to a NALU header using a separate message, or by using a special NALU type for NALUs carrying dependent slices.

Abstract: A currency information provision system (1) comprises: a database server (2) in which currency image information is stored as one type of currency information about multiple types of currency; and an information processing terminal (3) that acquires target image information (IA), which is image information for a search object, and transmits the target image information (IA) to the database server (2), the database server (2) comparing the target image information (IA) with the currency image information, and thereby searching a candidate currency estimated to be the same type as currency included in the search target, from among the multiple types of currency, and transmitting the currency information (IB) of the candidate currency to the information processing terminal (3), and the information processing terminal (3) providing currency information for the candidate currency.

Abstract: A computer-implemented image-based property condition determination system and method are provided. The method includes determining one or more element condition ratings from individual images of the property; and determining an overall property condition based on available element condition ratings of the property. An overall condition rating of a portfolio comprising a plurality of properties is determined using the condition ratings of properties in the portfolio.

Abstract: Systems and methods for navigating intersections autonomously or semi-autonomously can include, but are not limited to including, accessing data related to the geography and traffic management features of the intersection, executing autonomous actions to navigate the intersection, and coordinating with one or more processors and/or operators executing remote actions, if necessary. Traffic management features can be identified by using various types of images such as oblique images.

Abstract: Described herein are means for generation of self-taught generic models, named Models Genesis, without requiring any manual labeling, in which the Models Genesis are then utilized for the processing of medical imaging. For instance, an exemplary system is specially configured for learning general-purpose image representations by recovering original sub-volumes of 3D input images from transformed 3D images. Such a system operates by cropping a sub-volume from each 3D input image; performing image transformations upon each of the sub-volumes cropped from the 3D input images to generate transformed sub-volumes; and training an encoder-decoder architecture with skip connections to learn a common image representation by restoring the original sub-volumes cropped from the 3D input images from the transformed sub-volumes generated via the image transformations.

Abstract: A method for training a system for automatically detecting a defect of a blade of a turbomachine including a platform intended to receive a blade, at least one image-acquiring device, at least one light source the luminous intensity of which may vary, at least one moving device configured to move the platform with respect to the at least one image-acquiring device or to move the at least one image-acquiring device with respect to the platform, and a processing unit configured to receive acquired images and associate them with the items of information relating to the luminous intensity of the at least one light source and to the relative position of the platform with respect to the at least one image-acquiring device during the image acquisition.

Abstract: A method of training an image recognition model includes: generating a virtual dynamic vision sensor (DVS) image using a virtual simulator; generating label information including information about a correct answer to a result of recognition of the DVS image by the image recognition model, with respect to the DVS image; and training the image recognition model by modifying the image recognition model so that a difference between the result of recognition of the DVS image by the image recognition model and the label information is reduced.

Abstract: A method of determining one or more damage states of one or more auxiliary parts of a damaged vehicle, the vehicle comprising a plurality of normalized parts and at least some of the normalized parts further comprising one or more auxiliary parts. The method includes receiving one or more images of the vehicle, using a plurality of classifiers, each determining at least one classification of damage to the vehicle, each said classification being determined for each of a plurality of normalized parts of the vehicle, determining one or more classifications for the plurality of auxiliary parts using one or more trained models, wherein each classification comprises at least one indication of damage to at least one auxiliary part and outputting the determined damage states of the one or more auxiliary parts.

Abstract: In one aspect, an example method includes a processor (1) applying a feature map network to an image to create a feature map comprising a grid of vectors characterizing at least one feature in the image and (2) applying a probability map network to the feature map to create a probability map assigning a probability to the at least one feature in the image, where the assigned probability corresponds to a likelihood that the at least one feature is an overlay. The method further includes the processor determining that the probability exceeds a threshold, and responsive to the processor determining that the probability exceeds the threshold, performing a processing action associated with the at least one feature.

Abstract: This application discloses a map display method and apparatus, where the method includes: obtaining an initial image, where a first road is included in the initial image. The initial image is divided into a first sub-image and a second sub-image, where the first road is not included in the first sub-image, and the first road is included in the second sub-image. The first sub-image is mapped to a first plane of a transition model, and the second sub-image is mapped to a second plane of the transition model, where there is an included angle between the first plane and the second plane, and the first plane is parallel to a screen of a display device. The second plane is controlled to move by means of the transition model, until the included angle is reduced from a first angle to a second angle, and a three-dimensional map display animation is obtained.

Abstract: An image recognition method and apparatus, a training method, an electronic device, and a storage medium are provided. The image recognition method includes: acquiring an image to be recognized, the image to be recognized including a target text; and determining text content of the target text based on knowledge information and image information of the image to be recognized.

Abstract: The presently disclosed inventive concepts are directed to systems, computer program products, and methods for intelligent screen automation. According to one embodiment, a method includes: determining one or more logical relationships between textual elements and non-textual elements of one or more images of a user interface; building a hierarchy comprising some or all of the non-textual elements and some or all of the textual elements in order to form a data structure representing functionality of the user interface; and outputting the data structure to a memory.

Abstract: The present invention relates to an image encoding and decoding technique, and more particularly, to an image encoder and decoder using unidirectional prediction. The image encoder includes a dividing unit to divide a macro block into a plurality of sub-blocks, a unidirectional application determining unit to determine whether an identical prediction mode is applied to each of the plurality of sub-blocks, and a prediction mode determining unit to determine a prediction mode with respect to each of the plurality of sub-blocks based on a determined result of the unidirectional application determining unit.

Abstract: A predictive contrastive representation method for multivariate time-series data processing includes: mapping temporal coding information at a current moment and future situational information by using a logarithmic bilinear model to obtain a similarity; training the similarity according to a noise contrastive estimation method and prediction situational label data, and constructing, based on a training result, a predictive contrastive loss function of the temporal coding information at the current moment and the future situational information; sampling the prediction situational label data based on a corresponding optimal loss in the predictive contrastive loss function, optimizing the predictive contrastive loss function by using a direct proportion property between the sampling probability and the similarity, constructing mutual information between the temporal coding information at the current moment and the future situational information based on the optimized predictive contrastive loss function, and pe