Abstract: An evaluator 21 that evaluates states of cells included in each region of interest of a cell image, and a predictor 22 that performs, in advance, machine learning of a relationship between evaluation results for a specific region of interest within a first cell image obtained by imaging cells before a staining process and regions around the specific region of interest and staining states of cells of the specific region of interest within a second cell image obtained by imaging the same imaging targets as the cells of the first cell image after the staining process are provided. The predictor 22 predicts staining states of cells of a specific region of interest based on evaluation results for the specific region of interest and regions around the specific region of interest among the evaluation results for the third cell image of the cells before the staining process.

Abstract: An individual identifying device includes a conversion unit and an alignment unit. The conversion unit performs frequency conversion on an image obtained by imaging an object. The alignment unit performs alignment of an image for extracting a feature amount for identifying an individual of the object, based on a first subregion in the image after the frequency conversion.

Abstract: A method including selecting, in a server, a first image portion from an image is provided. The method also includes identifying one or more known similar images associated with the first image portion, and determining a first score for enhancing the first image portion based on the known similar image(s). The method includes increasing a pixel resolution in the first image portion according to the scale to form an enhanced image portion. The method also includes identifying a synthetic value for the enhanced image portion and storing the enhanced image portion in a database when the synthetic value is below a tolerance value.

Abstract: A method for training an obfuscation network capable of performing distinct concealing processes for distinct regions of an original image is provided. The method includes steps of: a learning device (a) inputting a training image into the obfuscation network to generate an obfuscated training image by performing a 1-st to an n-th concealing process respectively on a 1-st to an n-th training region of the training image; (b) inputting the obfuscated training image into a 1-st to an n-th discriminator to respectively generate a 1-st to an n-th obfuscated image score on determining whether the obfuscated training image is real or fake, and inputting the obfuscated training image into an image recognition network to apply learning operation on the obfuscated training image to generate feature information for training; and (c) training the obfuscation network such that an accumulated loss is maximized, and an accuracy loss is minimized.

Abstract: System, methods, and other embodiments described herein relate to detecting a lane change of a target vehicle ahead of an ego vehicle. In one approach, a method includes receiving communications from a target vehicle specifying state information about the target vehicle. The method includes determining a location of a first decision point at a first distance ahead of an ego vehicle and acquiring sensor data about the target vehicle. The method includes determining motion information about the target vehicle based on the sensor data. The motion information includes positions that the target vehicle occupied relative to the ego vehicle. The method includes storing the state information and motion information for a first amount of time, and determining whether the target vehicle has changed lanes based on the state information and the motion information that has been stored once the ego vehicle has reached the first decision position.

Abstract: System and method for classifying data objects occurring in an unstructured dataset, comprising: extracting feature vectors from the unstructured dataset, each feature vector representing an occurrence of a data object in the unstructured dataset; classifying the feature vectors into feature vector sets that each correspond to a respective object class from a plurality of object classes; for each feature vector set: performing multiple iterations of a clustering operation, each iteration including clustering feature vectors from the feature vector set into clusters of similar feature vectors and identifying outlier feature vectors, wherein for at least one iteration after a first iteration of the clustering operation, outlier feature vectors identified in a previous iteration are excluded from the clustering operation; and outputting a key cluster for the feature vector set from a final iteration of the multiple iterations, the key cluster including a greater number of similar feature vectors than any of the

Abstract: A method, system, and article are directed to haze reduction for image processing.

Abstract: The present invention relates to medical instrument tracking. In order to facilitate tracking a medical instrument, a system (100) is provided for tracking a medical instrument. The system comprises an instrument marker (14), a tracking arrangement (16), and a processing unit (18). The instrument marker is attached to the medical instrument on a marker position (20). The tracking system is configured to detect line segments (22) in the 5 field of interest and to detect the attached instrument marker. The processing unit is configured to identify a line segment (24) on which the attached instrument marker is detected as the medical instrument, and to determine an offset (26) between a position of a medical instrument tip (28) and the marker (14) by touching a reference marker (34) on the subject with the medical instrument (10).

Abstract: Disclosed is a method and system for certifying product authenticity using physical feature information including digitized surface fingerprint and blockchain, each of which manages certification information, packaged by binding together unique product information and physical feature information including surface feature of a product as a unique product feature, based on a distributed ledger of a blockchain to reliably determine whether the product is authentic or not in various ways. The authenticity certification information is packaged as a unique product feature by binding a serial number of the product for which the certification information is generated, together with physical feature information of the product, including image-based surface fingerprint. The authenticity certification information is stored in the blockchain.

Abstract: A system for converting an image of an unstructured table into a structured table is provided. The system may comprise a memory storing machine readable instructions. The system may include a processor to receive an image of a unstructured table and convert the image of the unstructured table into a structured table. Converting the image of the unstructured table into the structured table may include providing cell mapping and low confidence determination to highlight potentially misconverted content. The low confidence determination may be based on a first input and a second input. The processor may export the structured table, upon validation, to an application that supports structured tables.

Abstract: Some aspects of the subject matter disclosed herein include a system implemented on one or more data processing apparatuses. The system can include an interface configured to obtain, from one or more sensor subsystems, sensor data describing an environment of a vehicle, and to generate, using the sensor data, (i) one or more first neural network inputs representing sensor measurements for a particular object in the environment and (ii) a second neural network input representing sensor measurements for at least a portion of the environment that encompasses the particular object and additional portions of the environment that are not represented by the one or more first neural network inputs; and a convolutional neural network configured to process the second neural network input to generate an output, the output including a plurality of feature vectors that each correspond to a different one a plurality of regions of the environment.

Abstract: In one embodiment, a computing system accesses a number of features extracted from one or more first images. The extracted features are associated with at least one object captured in the first images. The first images are captured by a camera associated with a vehicle. The computing system identifies, in a reference map, reference features matching one or more of the features extracted from the first images. The reference features are associated with the at least one object captured in the first images. The computing system generates, for the camera, a calibration model by comparing the identified reference features in the reference map and the features that match the one or more reference features. The calibration model is used to calibrate second images captured by the camera associated with the vehicle.

Abstract: The invention relates to a method, a device (20, 30) and a computer-readable storage medium with instructions for determining the lateral position of a vehicle relative to the lanes of a road. An image processing unit (22) detects (10) road markings. A position-determining unit (23) determines (11) a starting position for the vehicle. A processing unit (24) determines (12) points of intersection of a line which is orthogonal to the vehicle movement and the detected road markings. The processing unit (24) determines (13) in addition points of intersection of the orthogonal line and road markings from a lane geometry map for the starting position determined for the vehicle, the points of intersection being filtered. An evaluation unit (25) finally determines (14) the lateral position of the vehicle by comparing the determined points of intersection.

Abstract: The present disclosure provides a method and apparatus for detecting significance of promotional information, a device and a computer storage medium. The method comprises: extracting each frame of image in a video segment, taking the image as input of a significance detecting module, and obtaining an output result of the significance detecting model; based on the output result, determining a significance score of promotional information corresponding to said each frame of image; obtaining the significance of promotional information corresponding to the video segment, based on the significance score of promotional information corresponding to each frame of image.

Abstract: This disclosure relates to a method for non-destructive inspection of a fruit (1) having an axis of rotational symmetry (10). The method comprises the step of positioning the fruit (1) in such a way that its axis of rotational symmetry (10) has an orientation that is substantially parallel to a predetermined plane (20) and the step of radiographing the fruit (1), where the direction of emission of X-rays is substantially perpendicular to said predetermined plane (20) and an X-ray image obtained (41) lies on said predetermined plane (20). The X-ray image obtained (41) is processed to calculate, at corresponding points of the X-ray image (41), respective values of attenuation of the X-ray signal through the fruit (1). The X-ray image (41) is divided into a plurality of sections (415) which are perpendicular to the projection (410) of the axis of rotational symmetry (10) on the predetermined plane (20).

Abstract: Deep learning-based clothing style evaluation method and system are provided.

Abstract: Technologies for interacting with a user to identify an unrecognizable object include an autonomous device that is configured to obtain environmental data indicative of an environment, detect an object in the environment using the environmental data, determine, in response to detecting the object, whether the object is recognizable, activate, in response to determining that the object is not recognizable, a light source, indicate the object using the light source, query a user using the light source and a natural language query output with an audio device, receive a user response indicative of an object identity, and update a recognizable object database in response to a receipt of the user response to associate the object identity with the object.

Abstract: A system for aerial image detection and classification is provided herein. The system comprising an aerial image database storing one or more aerial images electronically received from one or more image providers, and an object detection pre-processing engine in electronic communication with the aerial image database, the object detection pre-processing engine detecting and classifying objects using a disparity mapping generation sub-process to automatically process the one or more aerial images to generate a disparity map providing elevation information, a segmentation sub-process to automatically apply a pre-defined elevation threshold to the disparity map, the pre-defined elevation threshold adjustable by a user, and a classification sub-process to automatically detect and classify objects in the one or more stereoscopic pairs of aerial images by applying one or more automated detectors based on classification parameters and the pre-defined elevation threshold.

Abstract: Cameras capture time-stamped images of predefined areas. The predefined areas are assigned to one or more zones. The images are processed to recognize items and persons within the zones. Custom metrics with respect to each zone relative to at least time durations, items, and persons are logged in a data store. An interface is provided to custom define queries that are processed against the data store. In an embodiment, the data store is mined to discover relationships between the metrics for each zone, and real-time actions are processed when predefined conditions are discovered from the metrics.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for receiving a set of images at a social media system, wherein each image includes one or more recognized features associated with one or more lightness values; indexing each image using the one or more recognized features and the associated range of lightness values; receiving a query; determining a first group of images that is responsive to the query; determining that the query triggers a lightness filter to be displayed on the user device; providing the first group of images for display on a user interface with one or more lightness filter indicators; and in response to a user selection of one of the one or more lightness filter indicators: filtering the first group of images to determine a filtered group of images, and updating the images provided for display according to the filtered group of images.