Abstract: Embodiments of the present provide a method and a device for processing an image, server and storage medium. The method includes: determining, based on an object type of an object in an image to be processed, a feature expression of the object in the image to be processed; and determining an entity associated with the object in the image to be processed based on the feature expression of the object in the image to be processed and a feature expression of an entity in a knowledge graph.

Abstract: An automatic method of determining an image composition procedure that generates a new image visualization based on aggregations and variations of input images. A set of input images is received. Visual features are extracted from the input images. Context associated with input images is received. Based on the extracted visual features and the context associated with the input images, a composition procedure comprising a set of image operations to apply on the set of input images is learned. One or more image operations in the composition procedure are determined to present to a user. A difference visualization image associated with the input images may be generated by executing the one or more image operations.

Abstract: A system acquiring data for phenotyping on a farm field where crops are bred. The device includes: an information storage unit storing farm field information, an imaging condition, and aerial images of the farm field linked with imaging condition; a classification unit classifying the aerial images into a plurality of image groups having different imaging altitude ranges on the basis of an imaging altitude included in the imaging condition; an image processing unit creating image-processed data from at least one of the image groups and imaging condition and analyzes a trait of in the farm field from the image-processed data; a visualization unit visualizing image-processed data obtained in the image processing unit; a processed information storage unit storing, as processed information, data obtained in the image processing unit and visualization unit; and an output unit outputting data obtained in the image processing unit and the visualization unit.

Abstract: A system and method of animating an image of an object by extracting a plurality of three dimensional (3D) features from a first image, depicting a puppet object, and from the second image of a driver object, obtaining, a value of a first 3D identity-invariant feature of the puppet object, and a value of a second 3D identity-invariant feature, from the second image, calculating and applying, a mixing function, on the plurality of 3D features, generating a rendered image, based on the plurality of 3D features, the first 3D identity-invariant feature and the second 3D identity-invariant feature, and generating an output image based on the rendered image, the plurality of 3D features, the first image and the second image, wherein the output image depicting a target object comprising at least one 3D identity-invariant feature of the driver object.

Abstract: Methods and systems for managing vegetation include training a machine learning model based on an image of a training data region before a weather event, an image of the training data region after the weather event, and information regarding the weather event. A risk score is generated for a second region using the trained machine learning model based on an image of the second region and predicted weather information for the second region. The risk score is determined to indicate high-risk vegetation in the second region. A corrective action is performed to reduce the risk of vegetation in the second region.

Abstract: An exploration method starts from cuttings associated with sampling intervals and well data for a well in a subsurface formation. The cuttings are prepared and analyzed to extract textural and chemical/mineralogical data for plural fragments in each sample that is made of the cuttings in one sampling interval. The method then includes matching lithotypes of rock defined according to the textural and chemical/mineralogical data for each fragment with segments of the well data in the corresponding sampling interval to obtain correspondences between the lithotypes and depth ranges. The correspondences between the lithotypes and the depth ranges may be used as constraints for seismic data inversion.

Abstract: Methods for determining a trailer status are disclosed herein. An example method includes capturing a three-dimensional image and a two-dimensional image. The three-dimensional image may comprise three-dimensional image data, and the two-dimensional image may comprise two-dimensional image data. The example method may further include determining a first trailer status based on the three-dimensional image data, and determining a second trailer status based on the two-dimensional image data. The example method may further include comparing the first trailer status to the second trailer status to determine a final trailer status.

Abstract: A method for determining residue coverage within a field may include receiving, with one or more computing devices, first and second images of the field. The first image may depict a portion of the field at a first time during a crop-growing period and the second image may depict the portion of the field at a second time during the crop-growing period, with the first and second times being different. Furthermore, the method may include generating, with the one or more computing devices, an estimated residue coverage map for the field based on the received first and second images. Additionally, the method may include generating, with the one or more computing devices, a prescription map for the field based on the estimated residue coverage map.

Abstract: A method for determining management zones within an agricultural field, the method includes selecting a plurality of remotely sensed images of the agricultural field wherein the plurality of remotely sensed images represent a plurality of growing seasons, each of the plurality of remotely sensed images having a vegetation index associated therewith, generating a refined average image from the plurality of remotely sensed images of the agricultural field, and applying a classification method to define management zones associated with the refined average image.

Abstract: A simulator to simulate target detection and recognition is provided. The simulator includes a memory configured to store instructions and a processor disposed in communication with said memory. The processor upon execution of the instructions is configured to receive ground truth attributes defining one or more respective simulated physical objects and receive collection attributes defining one or more collections. Each collection is a simulation of automated target recognition applied to simulated sensor output associated with simulated sensing of the one or more simulated physical objects. The processor upon execution of the instructions is further configured to simulate detection of the one or more simulated physical objects and recognition based on the ground truth attributes and the collection attributes and output detection data about at least one detected object based on the simulation of target detection.

Abstract: Techniques are described for tracking a trailer, including obtaining a sequence of images from a camera positioned in rear of a vehicle, detecting the trailer and a tow-ball in the first image of the sequence of images using a trained learning model based on the first image and at least one scaled version of the first image, estimating an area of interest using a tracking algorithm and locations of the detected trailer and the tow-ball in the first image, estimating a distance between the vehicle and the trailer based on the detected location of the trailer in the first image, and upon determining that the distance between the vehicle and the trailer is less than a second threshold, continuously detecting locations of the trailer, tow-ball and a coupler in each image of the sequence of images using the learning model and the estimated area of interest, and updating the estimated area of interest to correspond to an area of interest in the following image, and actuating an action of the vehicle based on the de

Abstract: The present embodiments relate to efficient localization of a vehicle within a lane region. An imaging device onboard the vehicle may capture an image stream depicting an environment surrounding the vehicle. An image may be inspected to identify pixel bands indicative of lane markings of a road depicted in the image. Based on the identified pixel bands, a lane region indicating a lane of a roadway can be extracted. A lane drift metric may be generated that indicates an offset of the vehicle relative to a center of the lane region. An output action may be initiated based on the offset indicated in the lane drift metric. The lane region can be translated from a first frame to a second frame providing a top-down perspective of the vehicle within the lane region using a transformation matrix to assist in efficiently deriving the lane drift metric.

Abstract: A method to simulate target detection and recognition is provided. The method includes receiving ground truth attributes defining one or more respective simulated physical objects and receiving collection attributes defining one or more collections. Each collection is a simulation of automated target recognition applied to simulated sensor output associated with simulated sensing of the one or more simulated physical objects. The method further includes simulating detection of the one or more simulated physical objects and recognition based on the ground truth attributes and the collection attributes, generating detection data about at least one detected object based on the simulated target detection, and outputting the detection data.

Abstract: An information processing apparatus according to an aspect of the present invention displays a plurality of items corresponding to a plurality of kinds of predefined targets on a display device, manages a plurality of images, each in association with information regarding a position and a time at which each of the plurality of images is captured, chronologically captured by an imaging apparatus mounted on a moving object, and acquires an image containing a target corresponding to a selected item among the plurality images based on information regarding a position of the moving object or the imaging apparatus when an instruction based on a selection of any of the plurality of displayed items is input, information regarding a time at which the instruction is input, and the selected item.

Abstract: A method and an apparatus for visual positioning based on mobile edge computing are provided. In the method, the mobile edge computing node receives an environment image captured by a to-be-positioned device in an area covered by the mobile edge computing node; the mobile edge computing node determines a target image matching the environment image from multiple candidate images, and calculates the position and pose information of the to-be-positioned device based on the position and pose information of the device for capturing the target image; and the mobile edge computing node sends the position and pose information of the to-be-positioned device to the to-be-positioned device, so that the to-be-positioned device determines the positioning information in an electronic map according to the position and pose information.

Abstract: A computerized method of deep model matching for image transformation includes inputting pilot data and pre-trained deep model library into computer memories; performing a model matching scoring using the pilot data and the pre-trained deep model library to generate model matching score; and performing a model matching decision using the model matching score to generate a model matching decision output. Additional pilot data may be used to perform the model matching scoring and the model matching decision iteratively to obtain improved model matching decision output. Alternatively, the pre-trained deep model library may be pre-trained deep adversarial model library in the method.

Abstract: A farming machine includes one or more image sensors for capturing an image as the farming machine moves through the field. A control system accesses an image captured by the one or more sensors and identifies a distance value associated with each pixel of the image. The distance value corresponds to a distance between a point and an object that the pixel represents. The control system classifies pixels in the image as crop, plant, ground, etc. based on the visual information in the pixels. The control system generates a labelled point cloud using the labels and depth information, and identifies features about the crops, plants, ground, etc. in the point cloud. The control system generates treatment actions based on any of the depth information, visual information, point cloud, and feature values. The control system actuates a treatment mechanism based on the classified pixels.

Abstract: A security document may include a laserizable first layer including a grayscale image formed by laserizing; a color pattern that is in alignment with the grayscale image; and a second layer arranged between the first layer and the pattern, such that the first layer is above the second layer, and the pattern is below the second layer. The second layer may be more opaque than the first layer, such that when observing the security document from the top, the grayscale image appears to be colored by the color pattern only when the bottom of the security document is being illuminated.

Abstract: A method for characterization of coronary plaque tissue data and perivascular tissue data using image data gathered from a computed tomography (CT) scan along a blood vessel, the image information including radiodensity values of coronary plaque and perivascular tissue located adjacent to the coronary plaque, the method comprising quantifying radiodensity in regions of coronary plaque, quantifying, radiodensity in at least one region of corresponding perivascular tissue adjacent to the coronary plaque, determining gradients of the quantified radiodensity values within the coronary plaque and the quantified radiodensity values within the corresponding perivascular tissue, and determining a ratio of the quantified radiodensity values within the coronary plaque and the corresponding perivascular tissue; and characterizing the coronary plaque by analyzing a gradient of the quantified radiodensity values in the coronary plaque and the corresponding perivascular, and/or the ratio of the coronary plaque radiodensity

Abstract: In a crop identification method, multi-temporal sample remote sensing images labeled with first planting blocks of a specific crop are acquired. NDVI data of the sample remote sensing images are calculated. Noise of the NDVI data is reduced. A first multivariate Gaussian model is fitted based on de-noised NDVI data of the sample remote sensing image. Multi-temporal target remote sensing images are acquired. An NDVI time series of each pixel in the target remote sensing image is constructed. The NDVI time series is input to the first multivariate Gaussian model to obtain a likelihood value of each pixel displaying the specific crop in the remote sensing images. Second planting blocks of the specific crop in the target remote sensing images are determined accordingly. An accurate and robust identification result is thereby achieved.