Abstract: A model and an application thereof, the model being used for detecting a benign and malignant degree of a tumor. The model grades a change of an imprinted gene in a tumor by calculating a deletion expression quantity of the imprinted gene and a copy number abnormal expression quantity of the imprinted gene. The detection model and device in the present invention presents the expression of loss of imprinting in a sample of a tumor patient in a direct way for the first time; based on an imprinted gene in situ labeling method, a change of an imprinted gene is detected objectively, directly, early, and precisely, and a quantitative model can be provided, to make a great contribution to molecular pathology diagnosis.

Abstract: Technology is disclosed for annotating and source linking the objects displayed in an image. An example method including detecting, by a processing device, an object in an image; associating the object in the image with a source indicator; annotating, by the processing device, the image to indicate the object is associated with the source indicator; receiving a user selection of the object in the image; and identifying a source based on the source indicator and on contextual data associated with the user selection, wherein the source comprises information about the object.

Abstract: An electronic device may be configured to detect a fault in imaging and vision hardware accelerators. The electronic device may include a controller configured to select a first golden input frame of multiple golden input frames to perform a first self-test, and retrieve a first reference image signature corresponding to the first golden input frame. The electronic device may include a hardware accelerator module configured to obtain the first golden input frame, and generate a first output frame based on the first golden input frame. The electronic device may include a signature generator configured to generate a first generated image signature based on the first output frame. The electronic device may include a signature comparison module configured to compare the first generated image signature to the first reference image signature in order to determine whether the hardware accelerator module includes a fault at a first time.

Abstract: A system for physiological motion measurement is provided. The system may acquire a reference image corresponding to a reference motion phase of an ROI and a target image of the ROI corresponding to a target motion phase, wherein the reference motion phase may be different from the target motion phase. The system may identify one or more feature points relating to the ROI from the reference image, and determine a motion field of the feature points from the reference motion phase to the target motion phase using a motion prediction model. An input of the motion prediction model may include at least the reference image and the target image. The system may further determine a physiological condition of the ROI based on the motion field.

Abstract: A security system can perform a systematic test to generate a visualization of the security camera coverage for an area. The test can be performed using a network of security cameras, a central system, and a mapping entity. The mapping entity may hold or be attached to a sign with a pattern. The mapping entity can move the sign from one location to another throughout a zone to be mapped. At each location, the mapping entity may send a message with the coordinate of its current position and optionally the pattern it is carrying to the central system.

Abstract: The present embodiments relate generally to a system and method for generating images from ultrasound imaging data. The system can include a data acquisition processor, and administrator processor, a user device, and a server. The method can include transmitting images to a server, applying an implicit misfit function to generate a first set of images, then, based on this first set, apply an explicit misfit function to generate a second set of images of higher accuracy.

Abstract: A distance information generation apparatus includes a generation unit configured to generate distance information using first and second image signals captured from different viewpoints, a detection unit configured to detect a known-shape subject using the image signals, an extraction unit configured to extract, from the distance information generated by the generation unit, distance information corresponding to the subject, a calculation unit configured to calculate, based on the distance information and the shape of the subject detected by the detection unit, a correction parameter for correcting the distance information extracted by extraction unit, and a correction unit configured to correct, using the correction parameter calculated by the calculation unit, the distance information generated by the generation unit.

Abstract: A hardware accelerator for computing integral image values of an image is provided that includes a plurality of row computation components configurable to operate in parallel to compute row sum values of respective rows of a row block of the image. The hardware accelerator is further configured to compute integral image values for the row block using the row sum values and block pivots.

Abstract: There is provided systems and methods for performing actions based on light signatures. An exemplary system includes a light source, a light detector, a non-transitory memory storing a plurality of light signatures and a hardware processor. The hardware processor executes an executable code to illuminate, using the light source, a target object with a first light, collect, using the light detector, a second light being a reflection of the first light by the target object, match the second light with one of the plurality of light signatures, and perform an action in response to matching the second light with the one of the plurality of light signatures.

Abstract: Methods, systems, and non-transitory computer readable media are disclosed for generating artistic images by applying an artistic-effect to one or more frames of a video stream or digital images. In one or more embodiments, the disclosed system captures a video stream utilizing a camera of a computing device. The disclosed system deploys a distilled artistic-effect neural network on the computing device to generate an artistic version of the captured video stream at a first resolution in real time. The disclosed system can provide the artistic video stream for display via the computing device. Based on an indication of a capture event, the disclosed system utilizes the distilled artistic-effect neural network to generate an artistic image at a higher resolution than the artistic video stream. Furthermore, the disclosed system tunes and utilizes an artistic-effect patch generative adversarial neural network to modify parameters for the distilled artistic-effect neural network.

Abstract: Provided is an image analysis unit that receives an image to be a down-conversion processing target as an input and generate adjacent pixel difference information used to select a pixel to be output to a down-converted image, and an output image generation unit that selects a pixel pair to be an adjacent pixel used for the down-converted image on the basis of the adjacent pixel difference information and generate the down-converted image by executing an adjustment of the selected pixel pair. The image analysis unit calculates a difference of an adjacent pixel pair of a processing block constituent pixel, generates a sorting result of arranging the differences in descending order, and the output image generation unit selects the pixel pair to be used for the down-converted image in descending order of difference values on the basis of the sorting result.

Abstract: An adaptive multimodal system is provided for managing safety in an industrial environment. The system comprises: a computer vision component for generating a computer vision output data; a real-time locating component for generating location data about an object within the industrial environment; a LIDAR component for generating 3D point cloud data of the industrial environment; and one or more processors coupled to the computer vision component, the real-time locating component and the LIDAR component and configured to: (i) obtain an identity of the object and the location data, and (ii) adjust, based at least in part on the identity and the location data, a pixel distribution for acquiring the 3D point cloud data, the process for generating the computer vision output data, or one or more parameters for acquiring an image data by the computer vision component.

Abstract: A method for depth sensing from an image of a projected pattern is performed at an electronic device with one or more processors and memory. The method includes receiving an image of a projection of an illumination pattern; for a portion of the image, selecting a candidate image of a plurality of candidate images by comparing the portion of the image with a plurality of candidate images; and determining a depth for the portion of the image based on depth information associated with the selected candidate image. Related electronic devices and computer readable storage medium are also disclosed.

Abstract: Systems, methods, and computer readable media for performing task assignment, completion, and management within a crowdsourced surveillance platform. A remote server may identify targets for image capture and may assign capture tasks to users based on travel plans of the user. Users may be assigned task to capture image of target locations lying along a travel path. The remote server may aggregate data related to the captured images and use it to update a map and log changes to the target location over time.

Abstract: The present embodiments include a system and method for processing multi-dimensional images in real time through the use of third-party servers and cloud computing. The system includes a data acquisition processor, a data storage unit, an administrator processor, and a server. The server can be a cloud-based server. The method includes receiving multi-dimensional imaging data, compressing and blending the image data, transmitting the image data to a server, decompressing and deblending the data, generating multi-dimensional images, and transmitting the imaging data back to the administrator processor.

Abstract: System, methods, and other embodiments described herein relate to determining depths of a scene from a monocular image. In one embodiment, a method includes generating depth features from sensor data according to whether the sensor data includes sparse depth data. The method includes selectively injecting the depth features into a depth model. The method includes generating a depth map from at least a monocular image using the depth model that is guided by the depth features when injected. The method includes providing the depth map as depth estimates of objects represented in the monocular image.

Abstract: An individual identification system includes: a storing unit for storing an image capture parameter in association with data characterizing a surface of a reference object; an acquiring unit that, when data characterizing a surface of an object to be matched is input, calculates an approximation degree between the input data and each data stored in the storing unit, and acquires the image capture parameter applied to the object to be matched from the storing unit based on the calculated approximation degree; a condition setting unit that sets an image capture condition determined by the acquired image capture parameter; an image capturing unit that acquires an image of the surface of the object to be matched under the set image capture condition; an extracting unit that extracts a feature value from the acquired image; and a matching unit that matches the extracted feature value against a registered feature value.

Abstract: A method for generating a synthetic dataset from an original dataset includes encoding categorical features of the original dataset, embedding the encoded dataset in a low-dimensional space, selecting a seed record from the embedded dataset, identifying a plurality of nearest neighbor records to the seed record, generating a new record by randomly selecting features from the plurality of nearest neighbor records, and concatenating the new record into the synthetic dataset. For a synthetic dataset that contains N records, which may be the same as or different from the number of records in the original dataset, the selecting, identifying, generating, and concatenating operations operate a total of N times on the records in the embedded dataset.

Abstract: A method of image processing converts images of different brightness to a common brightness range, the images representing at least partly the same scene. One of images including a local block with movement is selected for forming a composed image from the images. One or more corresponding blocks corresponding to the local block are determined in the images. Each of said one or more corresponding blocks is weighted with at least one of the following: similarity with respect to the local block, a distance from a location of the local block, saturation of the one or more corresponding blocks, and noise of the one or more corresponding blocks. The local block and at least one of the one or more corresponding blocks are combined, or the local block is replaced by at least one of the one or more corresponding blocks based on the weighting for forming the composed image from the images.

Abstract: A method for classifying measuring points of a point cloud ascertained by at least one sensor, in particular, a point cloud ascertained from a LIDAR sensor, a radar sensor and/or a camera sensor, via a control unit. Local surface vectors to adjacent measuring points are ascertained for each measuring point of the point cloud. For each local surface vector, respectively one angle is calculated between the local surface vectors with respect to a gravity vector. A maximal surface vector having a maximal angle with respect to the gravity vector and a standardized surface vector are ascertained for each measuring point of the point cloud based on the calculated angles. Each measuring point of the point cloud includes a standardized surface vector and/or includes a maximal surface vector having an angle with respect to the gravity vector above a limiting value being classified as a non-ground point.