Abstract: A method for recognizing different object-categories within images based on texture classification of the different categories, which is implemented in an electronic device, includes extracting texture features from block images segmented from original images according to at least one Gabor filter; determining a grayscale level co-occurrence matrix of each block image according to the texture features; calculating texture feature statistics of each block image according to the grayscale level co-occurrence matrix; training and generating an object recognition model using the texture features and the texture feature statistics; and recognizing and classifying at least one object in original image according to the object recognition model.

Abstract: Systems and methods are provided for assembling an aircraft. The method includes receiving an upper half barrel section of fuselage that is in a crown up orientation and installing a crown module into the upper half barrel section.

Abstract: Embodiments of the disclosure provide a recognition method, apparatus, device, and storage medium and relates to the field of artificial intelligence technology. The method includes obtaining a second decision-making threshold of a feature-matching model in a target scenario by joint testing of the feature-matching model and auxiliary detection model. The method takes into full consideration the mutual influence between different algorithm models in a scenario when multiple algorithm models are used for facial recognition. Compared to manually setting a decision-making threshold for each algorithm model independently, the methods in the disclosure are more adaptable to changing scenarios and scenarios with multiple models used in facial recognition. This improves the accuracy and efficiency of the obtained decision-making thresholds, thereby enhancing the accuracy of multi-model facial recognition.

Abstract: A method for recognizing arteries and veins on a fundus image includes: executing a pre-process operation on the fundus image, so as to obtain a pre-processed fundus image; generating a fundus spectral reflection dataset associated with pixels of the pre-processed fundus image, based on the pre-processed fundus image, and a spectral transformation matrix; obtaining a plurality of principle component scores associated with the pixels of the pre-processed fundus image, respectively; and determining, for each of the pixels of the pre-processed fundus image that has been determined as a part of a blood vessel, whether the pixel belongs to a part of an artery or a part of a vein.

Abstract: The system determines whether content such as an image is suitable for content modification based on one or more criteria. The system includes decision engines or modules configured to evaluate one or more suitability metrics based on corresponding criteria such as publication status, restriction status, context, compatibility, and classification. If content is unsuitable for content modification because of entities or context depicted therein, privacy status, incompatibility with content modification, properties of the content file itself, or other aspects, the system generates a tag indicating the content is unsuitable for content modification. If content is suitable for content modification because of entities or context depicted therein, publication status, compatibility with content modification, properties of the content file itself, or other aspects, the system generates a content modification tag indicating the content is suitable for content modification.

Abstract: A video creation method of creating a video is provided. The method includes setting, in an imaging region of a reference video having a first angle of view, one or more regions having a second angle of view different from the first angle of view, determining a selection region that is the region in which a subject of a recording target is included, switching the selection region, displaying an insertion video in a period between a display period of a pre-switching video which is a video of the selection region before the switching and a display period of a post-switching video which is a video of the selection region after the switching, and executing, during a display period of the insertion video, a process to change a displayed video from one of the pre-switching video or the post-switching video to the other of the pre-switching video or the post-switching video.

Abstract: A teaching data extending device includes: a relationship acquiring unit that obtains a relationship between a plurality of features included in each of a plurality of teaching data; a feature selecting unit that selects any one or more of the plurality of features based on the relationship; and a teaching data extending unit that generates, for one or more teaching data, new teaching data in which a value of the feature selected by the feature selecting unit is replaced with a value of the feature in another teaching data classified in a same class.

Abstract: The present invention discloses a decoupling divide-and-conquer facial nerve segmentation method and device. As for the characteristics of a small facial nerve structure and a low contrast, a facial nerve segmentation model including a feature extraction module, a rough segmentation module, and a fine segmentation module is constructed. The feature extraction module is configured to extract a low-level feature and a plurality of different- and high-level features. The rough segmentation module is configured to globally search the different- and high-level features for facial-nerve features and fuse them. The fine segmentation module is configured to decouple a fused feature to obtain a central body feature. After the central body feature is combined with the low-level feature to obtain an edge-detail feature, a space attention mechanism is used to extract attention features from the central body feature and the edge-detail feature, to obtain a facial nerve segmentation image.

Abstract: A method and an apparatus for video frame processing are provided. The method includes: obtaining a convolutional neural network (CNN) feature and a local feature of a target video frame; performing dimension reduction on the CNN feature to obtain a CNN feature with a reduced dimension of the target video frame; obtaining a first video frame from multiple sample video frames, a distance between a CNN feature with a reduced dimension of the first video frame and the CNN feature with the reduced dimension of the target video frame meets a first preset condition; obtaining a local feature of the first video frame; calculating a matching degree between the local feature of the first video frame and the local feature of the target video frame; determining the first video frame as a duplicate video frame of the target video frame if the matching degree meets a second preset condition.

Abstract: Techniques for performing a machine learning model based spatial-temporal adaptive shift for end-to-end text-video retrieval are described.

Abstract: A device configured to identify a first pixel location within a first plurality of pixels corresponding with an item in a first image and to apply a first homography to the first pixel location to determine a first (x,y) coordinate. The device is further configured to identify a second pixel location within a second plurality of pixels corresponding with the item in a second image and to apply a second homography to the second pixel location to determine a second (x,y) coordinate. The device is further configured to determine that the distance between the first (x,y) coordinate and the second (x,y) coordinate is less than or equal to the distance threshold value, to associate the first plurality of pixels and the second plurality of pixels with a cluster for the item, and to output the first plurality of pixels and the second plurality of pixels.

Abstract: A sensing data processing device is provided. The sensing data processing device may track keypoints within a full observable range of the sensing data processing device based on sensing data collected from a previous frame and a sensing data collected from a current frame through a reference sensor, the full observable range determined based on a combined field of view (FOV) of sensors, manage a new keypoint together with the keypoints in response to the new keypoint being detected, and perform localization of the device based on the managed keypoints.

Abstract: A pattern inspection method includes converting sample patterns of a sample image into training images, extracting feature values of the training patterns, setting feature vectors of the training patterns on the basis of the feature values, converting the feature vectors into Gaussian vectors, clustering the Gaussian vectors, thereby sorting the Gaussian vectors into clusters, selecting a select vector from each of the clusters, storing, as a pattern model, the training pattern corresponding to the select vector in a database, converting a target pattern of a target image into an inspection pattern on the basis of the pattern model, and inspecting the inspection pattern.

Abstract: In one aspect, an example method for generating a candidate image for use as backdrop imagery for a graphical user interface is disclosed. The method includes receiving a raw image and determining an edge image from the raw image using edge detection. The method also includes identifying a candidate region of interest (ROI) in the raw image based on the candidate ROI enclosing a portion of the edge image having edge densities exceeding a threshold edge density. The method also includes manipulating the raw image relative to a backdrop imagery canvas for a graphical user interface based on a location of the candidate ROI within the raw image. The method also includes generating, based on the manipulating, a set of candidate backdrop images in which at least a portion of the candidate ROI occupies a preselected area of the backdrop imagery canvas, and storing the set of candidate backdrop images.

Abstract: An device is disclosed. A first buffer to store a query data point, and a second buffer to store a matrix of candidate data points. A processing element may process the query data point and the matrix of candidate data points to identify candidate data points in the matrix of candidate data points that are nearest to the query data point.

Abstract: A camera parameter estimating device includes an obtaining unit which obtains time series frames including a plurality of subject images existing in a common field of vision shared by multiple cameras for each of the cameras, a point estimating unit which estimates the coordinates of a joint point determined in the subject image in the time-series frame for each of the subject images, a motion trajectory estimating unit which estimates the trajectory of the joint point between the time-series frames for each of the cameras, an inter-camera correspondence estimating unit which estimates a group of the trajectories between the frames obtained respectively from a pair of the cameras, and a parameter estimating unit which estimates a camera parameter for each of the cameras according to the group of trajectories.

Abstract: The present disclosure relates to a system and computer-implemented method for character recognition in a payment card. The method includes receiving an image of a payment card and one or more details associated with the payment card. Further, a derivative of the image is determined based on the one or more details and a horizontal sum of pixel values is determined for a plurality of rows in the image. Furthermore, one or more Regions of Interest (ROIs) are identified in the image by comparing the horizontal sum of pixel values with a predefined first threshold. Subsequently, one or more characters in the one or more ROIs are extracted using one or more peak values in a histogram of the one or more ROIs. Finally, each of the one or more characters extracted from the one or more ROIs is recognized using a trained Artificial Intelligence technique.

Abstract: A method for the clustering and identification of animals in acquired images based on physical traits is provided, where a trait feature is a scalar or vector quantity that is a property of a trait and trait distance is a measure of discrepancy between the same trait features of two animals, and also introduced are several different ways of implementing clustering using trait features.

Abstract: An optical recognition based positioning method and a positioning system, which can replace or compensate conventional satellite-based GPS, is provided. A position is determined utilizing a digital image, which captured a set of optical marks of known geographic coordinates. The distances in pixel coordinates of the optical marks within the digital image are converted into an angle at the camera between the actual geographical coordinate of the optical marks. A first group of points having the same angle as the first and second optical marks are identified. A second group of points having the same angle as the second and third optical marks are identified. Also, a third group of points having the same angle as the first and third optical marks are identified. Then, a point that co-exist in the first, second and the third group is identified as the geographical location of the positioning module.

Abstract: A question answering bot that digests non-relational data tables is provided. A processor receives a question regarding a non-relational data table. A processor extracts at least one feature of the question using a natural language processing (NLP) model. A processor extracts at least one similar feature of the non-relational data table to the extracted at least one feature of the question. A processor determines at least one relevant cell in the non-relational data table based on the at least one feature of the question and the at least one similar feature of the non-relational data table. A processor provides an answer to the question, where the answer is based on the at least one relevant cell.

Abstract: A method for synchronizing an action of a target object with source audio is provided. Facial parameter conversion is performed on an audio parameter of the source audio at different time periods to obtain source parameter information of the source audio at the respective time periods. Parameter extraction is performed on a target video that includes the target object to obtain target parameter information of the target video. Image reconstruction is performed on the target object in the target video based on the source parameter information of the source audio and the target parameter information of the target video, to obtain a reconstructed image. Further, a synthetic video is generated based on the reconstructed image, the synthetic video including the target object, and the action of the target object being synchronized with the source audio.

Abstract: An imaging apparatus includes: an imaging unit that has a pixel region in which a plurality of pixels is arranged and that reads and outputs a pixel signal from the pixels included in the pixel region; a unit-of-readout controller that controls a unit of readout set as a part of the pixel region: a recognition unit that has learned training data for each of units of readout; a first output unit that outputs a recognition result of recognition performed by the recognition unit for each of the units of readout; a second output unit that outputs the pixel signal to a subsequent stage; and a trigger generator that generates a trigger signal for controlling a first timing at which the first output unit outputs the recognition result and a second timing at which the second output unit outputs the pixel signal.

Abstract: A method for generating an object model includes: obtaining an initial morphable model; obtaining a plurality of initial images of an object, and depth images corresponding to the plurality of initial images; obtaining a plurality of target topological images by processing the plurality of initial images based on the depth images; obtaining a plurality of models to be synthesized by processing the initial morphable model based on the plurality of target topological images; and generating a target object model based on the plurality of models to be synthesized.

Abstract: An apparatus includes one or more processors and logic encoded in one or more non-transitory media for execution by the one or more processors and when executed operable to receive a first image of an object, wherein the first image was captured by a first camera in a set of cameras. The logic is further operable to determine first keypoints and a first pose for the object based on the first image. The logic is further operable to receive a second image from a second camera in the set of cameras. The logic is further operable to determine an overlap of the first image and the second image based on locations of the first keypoints, the second keypoints, the first pose, and the second pose. The logic is further operable to determine a synchronization error between the first image and the second image based on the overlap.

Abstract: The present disclosure generally relates to methods and user interfaces for managing live communication between multiple participants using a computer system in accordance with some embodiments.

Abstract: Disclosed herein is digital object generator that makes uses a one-way function to generate unique digital objects based on the user specific input. Features of the input are first extracted via a few-shot convolutional neural network model, then evaluated weight and integrated fit. The resulting digital object includes a user decipherable output such as a visual representation, an audio representation, or a multimedia representation that includes recognizable elements from the user specific input.

Abstract: There is a method of computing a stylized, animatable representation of a subject from a family of stylized animatable representations. The method comprises: accessing a realistic representation of the subject; computing a mesh mapping using a model that was formed using a data set of training examples which pair realistic representations of other subjects with instances of the family. The method also comprises applying the mesh mapping to the realistic representation of the subject to produce a target mesh; and selecting the stylized animatable representation from the family, by assessing closeness of the target mesh with instances of the family.

Abstract: A method for operating a processing unit of a vehicle for processing sensor data of several different sensors with an artificial neural network, wherein a set of volume data cells is provided as a volumetric representation of different volume elements of an environment, and when sensor data is generated by the sensors the sensor data is transferred to the respective volume data cells using an inverse mapping function, wherein each inverse mapping function is a mapping of a respective sensor coordinate system of the sensor to an internal volumetric coordinate system corresponding to the world coordinate system, and by the transfer of the sensor data each volume data cell receives the sensor data that are associated with this volume data cell according to the inverse mapping function from each sensor, wherein the received sensor data from each sensor are accumulated in the respective volume data cell as combined data.

Abstract: An image signal processor and an image sensor including the same are disclosed. An image sensor includes a pixel array configured to convert received optical signals into electrical signals, a readout circuit configured to convert the electrical signals into image data and output the image data, and an image signal processor configured to perform deep learning-based image processing on the image data based on training data selected from among first training data and second training data based on a noise level of the image data.

Abstract: A modulated segmentation system can use a modulator network to emphasize spatial prior data of an object to track the object across multiple images. The modulated segmentation system can use a segmentation network that receives spatial prior data as intermediate data that improves segmentation accuracy. The segmentation network can further receive visual guide information from a visual guide network to increase tracking accuracy via segmentation.

Abstract: Example freight management systems and methods are described. In one implementation, techniques receive at least one wide angle camera image from a sensor tower, where the sensor tower is located proximate a loading dock and the wide angle camera image is associated with at least a portion of the loading dock. The techniques also receive multiple high precision camera images from the sensor tower, where the plurality of high precision camera images are associated with at least a portion of the loading dock. The techniques process the wide angle image using a first convolutional neural network (CNN) and process the multiple high precision images using a second CNN. The techniques identify a freight item proximate the loading dock based on the processed high precision images.

Abstract: The embodiments relate to a self-moving device, moving and working in a working area, including: an image detection module, detecting an environment around the self-moving device to generate an environmental image; a first recognition module, recognizing a specific object in the image based on the environmental image to generate a first recognition signal; a first communication module, selectively sending the environmental image and/or the first recognition signal to a server and receiving a second recognition signal corresponding to the environmental image; and a control module, controlling an action of the self-moving device according to the first recognition signal and/or the second recognition signal. Beneficial effects of embodiments are as follows: It is ensured that the self-moving device can obtain an accurate recognition result in time and performs an action accordingly.

Abstract: A terminal apparatus according to an embodiment includes a solid-state imaging element and a first communication unit. The solid-state imaging element includes an image sensor, an image processing unit, and an output interface. The image sensor converts received light into an image. The image processing unit detects a specific object in the image. The output interface outputs the image. The first communication unit transmits the image output from the output interface. The information processing apparatus includes a second communication unit and a processor. The processor receives an image transmitted from the terminal apparatus with the second communication unit. When the image received with the second communication unit is an image in which the image processing unit has detected a specific object, the processor performs mask processing of masking the specific object.

Abstract: An information processing device according to the present disclosure includes: an acquisition unit that acquires, from a terminal device, input information input to the terminal device by a user; a generation unit that generates content of displaying an input item of each of functions including a corresponding input item that is an input item corresponding to the input information; and a transmission unit that transmits the content generated by the generation unit to the terminal device.

Abstract: In some aspects, the disclosure is directed to methods and systems for machine learning-based identification of sepsis in patients, such as those with severe burn injuries. A set of biomarker and vital sign measurements of a population with a known clinical diagnosis may be collected and normalized. A first subset of the modified set of biomarker and vital sign measurements may be used to train and test the algorithm (e.g. a neural network), and a second subset of the modified set of biomarker and vital sign measurements may be used for secondary validation studies.

Abstract: A method for generating a plurality of sets of training image data for training a machine learning model includes: (a) acquiring object image data representing an object image; (b) dividing the object image into T number of partial object images by dividing a region of the object image into T number of partial regions corresponding to respective ones of T number of partial color ranges; (c) generating a plurality of sets of color-modified object image data representing respective ones of a plurality of color-modified object images by performing an adjustment process on the object image data, the adjustment process including a color modification process to modify colors of at least one of the T number of partial object images; and (d) generating the plurality of sets of training image data using one or more sets of background image data and the plurality of sets of color-modified object image data.

Abstract: An ophthalmologic information processing apparatus includes an acquisition unit and a determination unit. The acquisition unit is configured to acquire a captured image of a subject's eye. The determination unit is configured to determine whether or not the captured image acquired by the acquisition unit is an analysis error image including a predetermined analysis error factor.

Abstract: The present technology relates to an imaging device, an imaging method, and a program that make it possible to easily perform imaging focusing on a subject or a specific part of the subject. The imaging device causes a preliminary-notice frame that gives notice of a specific area to be focused to be displayed on an image obtained by an imaging unit according to a type of the subject. The present technology can be applied to an imaging device.

Abstract: An image extraction device, an image extraction system, an image extraction method, and an image extraction program are capable of extracting an appropriate reference image from a database by using a pathological image as a query. The image extraction device includes: an acquisition unit which acquires a pathological image; a first calculation unit which calculates a local feature amount of the pathological image; a second calculation unit which calculates a non-local feature amount based on a correlation with the local feature amount; a third calculation unit which calculates a degree of similarity between the non-local feature amount and a feature amount of each of a plurality of reference images stored in a database; and an extraction unit which extracts one or more reference images based on the degree of similarity.

Abstract: Embodiments described herein provide a system and method for extracting factual information. The system transforms a query into a natural language prompt in a format of a query subject and a queried relation. The system encodes, via an embedding layer of a pre-trained language model, the natural language prompt into a first embedding. The system encodes, via the adapter model, the first embedding into a second embedding based on a probability that the second embedding returns the factual information when the second embedding is fed the first attention layer of the pre-trained language model. The system decodes, by the first attention layer of the pre-trained language mode, the second embedding into a response to the query. The system extracts the factual information from the decoded response to the query.

Abstract: A controlling method of an electronic apparatus is provided. The controlling method of the electronic apparatus according to an embodiment includes displaying cooking description information for one cooking step from among a plurality of cooking steps included in recipe information, receiving other cooking description information that can replace the displayed cooking description information from an external device, classifying the received other cooking description information according to a predetermined criterion, displaying the classified other cooking description information, and based on one of the displayed other cooking description information being selected, updating the recipe information by replacing the displayed cooking description information with the selected other cooking description information.

Abstract: The present disclosure relates to systems and methods for processing real-time video and detecting objects in the video. In one implementation, a system is provided that includes an input port for receiving real-time video obtained from a medical image device, a first bus for transferring the received real-time video, and at least one processor configured to receive the real-time video from the first bus, perform object detection by applying a trained neural network on frames of the received real-time video, and overlay a border indicating a location of at least one detected object in the frames. The system also includes a second bus for receiving the video with the overlaid border, an output port for outputting the video with the overlaid border from the second bus to an external display, and a third bus for directly transmitting the received real-time video to the output port.

Abstract: This document describes systems and techniques for clustering track pairs for multi-sensor track association. Many track-association algorithms use pattern-matching processes that can be computationally complex. Clustering tracks derived from different sensors present on a vehicle may reduce the computational complexity by reducing the pattern-matching problem into groups of subproblems. The weakest connection between two sets of tracks is identified based on both the perspective from each track derived from a first sensor and the perspective of each track derived from a second sensor. By identifying and pruning the weakest connection between two sets of tracks, a large cluster of tracks may be split into smaller clusters. The smaller clusters may require fewer computations by limiting the quantity of candidate track pairs to be evaluated. Fewer computations result in processing the sensor information more efficiently that, in turn, may increase the safety and reliability of an automobile.

Abstract: A system for detects and manages recurring transactions incurred by members of a client organization. The system includes a computing server that is configured to classify named entities into a category of transacting named entities. For example, the computing server classifies a list of named entities that offer subscription services and hence, create recurring transactions with the client organization. The computing server receives transaction data and determines that a target transacting named entity extracted from the received data belongs to the category (e.g., there is a named entity in the received transaction data that has sold a service to the client organization more than once). The computing server may then determine the presence and frequency of a subscription transaction series in the received transaction data, and cause a predicted a timing of an upcoming transaction in the subscription transaction series to be displayed in a graphical user interface (GUI).

Abstract: A method, apparatus and computer program product identify objects of interest within images. In a method, the method receives one or more images generated by an image capture device that is configured to be relocated. The method also receives information regarding an estimated location of the image capture device. Based upon a representation of a respective image of the one or more images and respective representations of a plurality of reference images, the method identifies one or more reference images that are associated with the estimated location. The method also identifies an object of interest within the respective image generated by the image capture device based upon respective digital signatures of one or more objects of interest and at least some context associated with the one or more objects of interest that are depicted by the one or more reference images that have been identified.

Abstract: Embodiments are disclosed for identifying and generating symmetrical repeat edits to similar objects in an image. A selection of a first object and an edit to the first object in an image is received. The image is searched for a plurality of candidate objects that have a similar shape to the first object and the plurality of candidate objects are filtered to include one or more objects that are symmetrical with the first object. A symmetric object is selected from the plurality of candidate objects. An axis of symmetry is computed between the symmetric object and the first object. The edit is applied to the symmetric object and to the first object.

Abstract: An image processing apparatus according to the present invention includes: an extracting unit configured to extract a candidate image, which is an image of a candidate region specified in accordance with a preset criterion, from a target image to be a target for an annotation process, and also extract a corresponding image, which is an image of a corresponding region corresponding to the candidate region, from a reference image that is an image corresponding to the target image; a displaying unit configured to display the candidate image and the corresponding image so as to be able to compare the images with each other; and an input accepting unit configured to accept input of input information for the annotation process for the candidate image.

Abstract: An imaging system includes first camera; second camera, second field of view of second camera being wider than first field of view of first camera, wherein first field of view overlaps with portion of second field of view; and processor(s) configured to: capture first images and second images, wherein overlapping image segment and non-overlapping image segment of second image correspond to said portion and remaining portion of second field of view; determine blurred region(s) (B1, B2) of first image; and generate output image in manner that: inner image segment of output image is generated from: region(s) of overlapping image segment that corresponds to blurred region(s) of first image, and remaining region of first image that is not blurred, and peripheral image segment of output image is generated from non-overlapping image segment.

Abstract: A computer-implemented prediction method, system, and computer program product including detecting an anomaly between at least two images in a social media timeline, analyzing at least one of contextual data and image data associated with the at least two images that cause the anomaly, and predicting an actual time of the at least two images in the social media timeline based on the analyzed at least one of contextual data and image data.

Abstract: Technologies are provided herein for determining if a face image is of sufficient quality for facial recognition processing. An image that contains a face image is received and analyzed to identify facial features of the face image. Distances between the facial features are determined and dimensions of regions for the facial features are calculated based on those distances. A plurality of regions in the face image are defined based on the calculated dimensions. A combined pixel contrast quality of the face image is then calculated based on a combination of separate pixel contrast qualities for the plurality of regions. The face image is determined to be of sufficient quality for facial recognition processing in response to the combined pixel contrast quality satisfying one or more criteria.