Abstract: The present disclosure pertains to a system configured to manage and track intellectual property (IP). Some embodiments of this system may include: an application programming interface (API) having access to a web-accessible IP database; an artificial intelligence (AI) or learning engine coupled to the API and operating the API to query the web-accessible IP database for orphaned IP assets that have become abandoned, expired, and/or public domain assets; and a user-collaboration platform. Some embodiments of this platform may: receive information from the API concerning the orphaned IP assets found by the API; store the information concerning each of the orphaned IP assets on a blockchain data structure; provide access to the orphaned IP assets stored on the blockchain data structure to users via a network; and record user activity concerning user access, development, sharing, and/or modification of the orphaned IP assets on the blockchain data structure.

Abstract: Various embodiments are generally directed to providing a digital negotiation platform. An agent executing on a processor may programmatically negotiate parameters for an agreement based on rules and/or computing models. The agent may generate a smart contract comprising a transcript of communications and executable code operable to enforce the parameters. The smart contract may be stored in a blockchain.

Abstract: Digitally enabled solutions are provided using a centralized test management center. The digitally enabled solutions may be applied to computerized and paper-based tests. During actual testing events, real-time data is obtained and processed to the centralized test management center. When administering examinations, this aids proctors by: automating administration workflow; reducing/eliminating variations in the proctoring process across various locations; detecting problematic events while proctoring the exams in real-time; and identifying irregularities that may adversely impact the validity and integrity of the testing scores.

Abstract: A system includes a computer remote from a vehicle. The computer is programmed to store a label of an odor associated with a user, instruct the vehicle to pick up the user, receive data from an environmental sensor in the vehicle, and output a message upon determining that the data indicate that a level of the odor exceeds a threshold.

Abstract: Methods and apparatuses for watermark embedding and extracting are provided. A method for watermark embedding includes obtaining a carrier object and watermark information to be embedded in the carrier object; generating at least one encoding region including the watermark information according to the watermark information, the at least one encoding region including a plurality of template lattices; obtaining a watermark image according to the at least one encoding region; and embedding the watermark image in the carrier object.

Abstract: The present invention relates to an apparatus for adapting image processing based on a shape of a display device for a motor vehicle, the apparatus (1) comprising: a capture device (10), which is configured to capture a shape of a display device (200) and an extrinsic camera parameter of a camera, an intrinsic camera parameter of the camera, and at least one field of view of the camera; an arithmetic unit (20), which is configured to calculate a reprojection surface and a correspondence table based on the captured shape of the display device and based on the at least one extrinsic camera parameter, the at least one intrinsic camera parameter, and the at least one field of view; and an image processing device (30), which is configured to perform adapted image processing based on the calculated reprojection surface and the calculated correspondence table.

Abstract: Provided are a synthesis processing configured to that synthesize a first image acquired by a first imaging unit and a second image acquired by a second imaging unit of which an imaging range overlaps a part of an imaging range of the first imaging unit and generates a wide-angle image and an extraction unit configured to extract a region of interest within an imaging range in which the first imaging unit and the second imaging unit perform overlapping imaging on the basis of at least one of a third image, acquired by a third imaging unit, which includes at least a part of the imaging range in which the first imaging unit and the second imaging unit perform overlapping imaging in an imaging range thereof and a state of the third imaging unit when the third image is captured. The synthesis processing unit adjusts an amount of positional shift between the first image and the second image in the region of interest, and generates the wide-angle image.

Abstract: An imaging system includes an image sensor having an array of sensor pixels, a multi-chromatic illuminator adapted to independently generate a plurality of illumination colors each having a different finite spectral range, and a controller coupled to the image sensor and to the multi-chromatic illuminator. The controller includes logic to time-multiplexing the multi-chromatic illuminator between the illumination colors. Each of the illumination colors independently illuminates for at least a corresponding one of non-overlapping durations of illumination of the illumination colors. The controller includes further logic to acquire chromatic sub-images with the image sensor and combine the chromatic sub-images into a composite color image. Each of the chromatic sub-images is acquired during a different one of the non-overlapping durations of illumination.

Abstract: A system for generating a high resolution (HR) computed tomography (CT) image from a low resolution (LR) CT image is described. The system includes a first generative adversarial network (GAN) and a second GAN. The first GAN includes a first generative neural network (G) configured to receive a training LR image dataset and to generate a corresponding estimated HR image dataset, and a first discriminative neural network (DY) configured to compare a training HR image dataset and the estimated HR image dataset. The second GAN includes a second generative neural network (F) configured to receive the training HR image dataset and to generate a corresponding estimated LR image dataset, and a second discriminative neural network (DX) configured to compare the training LR image dataset and the estimated LR image dataset.

Abstract: Described herein are motion smoothing techniques for a display system to account for motion of moving or animating objects in a way that mitigates judder. For example, first pixel data and second pixel data associated with two previously-rendered frames may be provided to a graphics processing unit (GPU) as input. The video encoder of the GPU can process the input pixel data to generate an array of motion vectors which is used to modify third pixel data of a re-projected frame. The modified third pixel data for the re-projected frame is “motion-smoothed” for rendering on a display, such as a head-mounted display (HMD), in a manner that mitigates judder of moving or animating objects.

Abstract: A vehicular vision system includes a camera disposed at an in-cabin surface of a vehicle windshield, which includes a blackout region and a light-transmitting window through the blackout region. The camera views through the windshield at the light-transmitting window. The field of view of the camera encompasses at least part of the blackout region around the light-transmitting window, such that some of the photosensing elements do not receive light that passes through the light-transmitting window. A control processes image data captured by the camera to provide a dewarped image and does not use some of the photosensing elements of the camera when providing the dewarped image. The light-transmitting window of the blackout region is sized and shaped such that the photosensing elements that are not used by the control in dewarping the image are the same photosensing elements that do not receive light that passes through the light-transmitting window.

Abstract: A method of generating an elemental map includes: acquiring a plurality of correction channel images by scanning a surface of a standard specimen having a uniform elemental concentration with a primary beam and generating a correction channel image for each channel; generating correction information for each pixel of each correction channel image among the plurality of correction channel images based on a brightness value of the pixel; acquiring a plurality of analysis channel images by scanning a surface of a specimen to be analyzed with the primary beam and generating an analysis channel image for each channel; correcting brightness values of pixels constituting an analysis channel image among the plurality of analysis channel images based on the correction information; and generating an elemental map of the specimen to be analyzed based on the plurality of analysis channel images having pixels with corrected brightness values.

Abstract: An image processing method configured to correct a captured image using a neural network includes a first step of determining an inversion axis for a partial image or a filter of the neural network according to a position in the captured image of the partial image that is part of the captured image, a second step of determining a positional relationship among pixels of color components in an image input to the neural network corresponding to the inversion axis, and a third step of generating a corrected image obtained by correcting an input image by processing, using the neural network, the input image generated from the captured image based on the positional relationship.

Abstract: An image inspection apparatus includes an image reader that reads an original image formed on a recording material based on a print job and generates a read image, and a hardware processor that analyzes the read image and performs an image inspection, wherein the hardware processor: acquires the read image from the image reader, detects an edge from the read image, and excludes a region near the edge from a target of the image inspection; performs a predetermined filter process on the read image after the exclusion process to generate a first reference image compares the read image after the exclusion process with the first reference image to generate a first comparison image; and binarizes the first comparison image using a predetermined threshold to detect points where a specific abnormality has occurred, and outputs a detection result.

Abstract: A biological sample system that efficiently images and registers codes corresponding to biological samples depicted in an image. The biological sample system can implement a neural network to detect the codes individually, and further translate each code into an item identifier. The item identifiers are correlated with network server accounts and a user interface can depict the biological samples with data indicating their registration state.

Abstract: Methods and systems for facilitating photo-based estimation are described. In an aspect, a server is configured to receive, via a communications module from a remote computing device, a signal comprising image data obtained by the remote computing device through activation of a submission application. The server may obtain verification data that includes at least one of policy data obtained from at least one of the stored profiles or sensor data received from the remote computing device. The server may evaluate the image data based on the verification data to determine whether the image data is valid. The server may, upon determining that the image data is not valid, generate an error.

Abstract: An image processing apparatus includes a memory configured to record base image data and an index value of the base image data, and a processor. The processor is configured to calculate an index value based on input image data and correct answer information at a position where the input image data is captured, read an index value of base image data at the position where the input image data is captured, from the memory, and write the input image data and the calculated index value into the memory such that each of the base image data and the index value of the base image data is updated in a case where the calculated index value has evaluation higher than evaluation of the index value of the base image data.

Abstract: A method and an apparatus for generating vehicle damage information are provided. The method includes: acquiring a damage area image of a target vehicle; performing image segmentation on the damage area image to obtain a first suspected damage area; inputting the damage area image to a pre-trained detection model to obtain a second suspected damage area, the detection model being configured to detect a location of the suspected damage area in the image; determining a damage image feature based on the first suspected damage area and the second suspected damage area; and inputting the damage image feature to a pre-trained classification model to generate a damage type, the classification model being configured to characterize a corresponding relationship between the image feature and the damage type.

Abstract: A method is provided for model training to detect defective products. The method includes sampling training images of a product to (i) extract image portions therefrom made of a center patch and its context and (ii) black-out the center patch. The method further includes performing unsupervised back-propagation training of a Contextual Auto-Encoder (CAE) model using (i) the image portions with the blacked-out center patch as an input and, (ii) the center patch as a target output and, (iii) an image-based loss function, to obtain a trained CAE model. The method also includes sampling positive and negative center-patch-sized portions from the training images. The method additionally includes normalizing, using the trained CAE model, the positive and negative center-patch-sized portions.

Abstract: A method for applying AOI to copper coils thinned by laser etching includes placing a half-finished product under a scanning unit; scanning the half-finished product to generate an image; sending the image to an image analysis unit which is activated to analyze the image, identify cutting boundaries of the half-finished product, compare the cutting boundaries with an original processing path file, and identify defects of the half-finished product; activating the image analysis unit to find points around the half-finished product; activating the image analysis unit to simulate an optimum path; activating the image analysis unit to convert the optimum path into an optimum processing path file; activating the image analysis unit to send the optimum processing path file to a program unit; conveying the half-finished product to dispose under a laser processing unit; and activating the program unit to instruct the laser processing unit to process the half-finished product.

Abstract: A metrology system is disclosed. In one embodiment, the system includes a characterization sub-system configured to acquire one or more images of a specimen. In another embodiment, the system includes a controller configured to: receive one or more training images of a specimen from the characterization sub-system; receive one or more training region-of-interest (ROI) selections within the one or more training images; generate a machine learning classifier based on the one or more training images and the one or more training ROI selections; receive one or more product images of a specimen from the characterization sub-system; generate one or more classified regions of interest with the machine learning classifier; and determine one or more measurements of the specimen within the one or more classified regions of interest.

Abstract: A method for localizing an intracranial electrode in a subject's brain is provided. The intracranial electrode has at least one electrode contact. The method includes: acquiring a first brain image reconstructed from first image data acquired after electrode-implantation; acquiring a second brain image reconstructed from second image data acquired before the electrode-implantation; co-registering the first brain image and the second brain image to acquire spatial transformation parameters; extracting a first coordinate of the electrode contact from the first brain image; converting the first coordinate into a second coordinate in the second brain image by using the spatial transformation parameters; co-registering the second brain image and a universal brain atlas to define functional zones in the second brain image; and defining a corresponding functional zone where the second coordinate is located. Another alternative method and a system for localizing an intracranial electrode are also provided herein.

Abstract: A medical image processing apparatus according to present embodiments includes processing circuitry. The processing circuitry is configured to acquire medical images. The processing circuitry is configured to control the medical images based on an evaluation value corresponding to each of the medical images, thereby control a forwarding/reversing number or a forwarding/reversing speed of displayed images of the medical images for an operation amount.

Abstract: Apparatus and methods are described for use with a sample that includes a plurality of red blood cells. A series of images associated with a series of depth levels of the sample are acquired, by performing a depth scan of the sample with a microscope. One of the depth levels is identified as being an optimum focal plane for imaging one or more entities within the sample using the microscope, by identifying that a drop in image contrast occurs at the identified depth level. The sample is imaged using the microscope, by focusing the microscope at an investigative depth level that is based on the identified depth level. Other applications are also described.

Abstract: The present disclosure relates to the classification of images, such as medical images using machine learning techniques. In certain aspects, the technique may employ a distance metric for the purpose of classification, where the distance metric determined for a given image with respect to a homogenous group or class of images is used to classify the image.

Abstract: A breast image analysis method, breast image analysis system and non-transitory computer-readable medium are provided in this disclosure. The breast image analysis method includes the following operations: inputting a first breast image and a second breast image; extracting a plurality of first feature points of the first breast image and a plurality of second feature points of the second breast image; calibrating the first breast image and the second breast image according to the first feature points and the second feature points to generate a first calibration image and a second calibration image; calculating a first difference image and calculating a second difference image; utilizing an image analysis model to respectively determine whether the first difference image and the second difference image are symmetric; and generating an analysis report representing a symmetry result or an asymmetry result.

Abstract: A chromosome abnormality detecting system includes an image capturing unit and a non-transitory machine readable medium. The image capturing unit is for obtaining a target metaphase chromosomes image of a subject. The non-transitory machine readable medium storing a program which, when executed by at least one processing unit, determines whether the subject has a chromosome abnormality when executed by a processing unit. The program includes a reference database obtaining module, a reference image transforming module, a reference preliminary classifying module, a reference feature selecting module, a training module, a target image transforming module, a target preliminary classifying module, a target feature selecting module and a comparing module.

Abstract: Methods of processing images, such as ultrasound images, to determine integrity of an implant are described. The method may include receiving an ultrasound image of an implant in a body of a subject; determining one or more characteristics of a surface of the implant based on an intensity of pixels of the ultrasound image; generating a predicted status of the implant based on the one or more characteristics by comparison of the one or more characteristics with a database of image data; and displaying the predicted status of the implant. The implant may be a breast implant, for example, wherein the method is useful for analyzing the presence or probability of extracapsular ruptures, contractures, and combinations thereof.

Abstract: Approaches for the automatic segmentation of magnetic resonance (MR) images. Machine learning models segment images to identify image features in consecutive frames at different levels of resolution. A neural network block is applied to groups of MR images to produce primary feature maps at two or more levels of resolution. The images in a given group of MR images may correspond to a cycle and have a temporal order. A second RNN block is applied to the primary feature maps to produce two or more output tensors at corresponding levels of resolution. A segmentation block is applied to the two or more output tensors to produce a probability map for the MR images. The first neural network block may be a convolutional neural network (CNN) block. The second neural network block may be a convolutional long short-term (LSTM) block.

Abstract: Provided are an image recognition apparatus, an image recognition method, and a program for enabling recognition of many kinds of objects with high precision. An overall recognition unit executes, for at least one given object, a process of recognizing the position of the object in an image. A partial image extraction unit extracts, from the image, a partial image which is a part of the image associated with the recognized position. A partial recognition unit executes a process of recognizing what is one or more objects represented by the partial image, the one or more objects including an object other than the given object the position of which is recognized.

Abstract: A relative atlas graph is generated to store mapping data used by an autonomous vehicle. The relative atlas graph may be generated for a geographical area based on observations collected from the geographical area, and may include element nodes corresponding to elements detected from the observations along with edges that connect pairs of element nodes and define relative poses between the elements for connected pairs of element nodes.

Abstract: An extraction section extracts, from each of a plurality of non-defective product images which show the appearance of the inspection target determined to be a non-defective product, the non-defective vector representing a feature of each non-defective product image. A generation section generates a transformation matrix by using a plurality of non-defective product vectors extracted by the extraction section. The transformation matrix is a matrix representing sequentially performing first mapping for mapping the feature vector to a feature space and second mapping for mapping a result of the first mapping to the whole space to which the feature vector belongs. An adjusting section adjusts each element of the transformation matrix generated by the generation section by using, as learning data, the feature vector extracted from a pseudo defect image.

Abstract: Methods and apparatus for generating improved image data from received input image data comprising first input image data associated with a first exposure level and second input image data associated with a second, different, exposure level. Motion detection data is generated from the received input image data by applying a noise model and improved image data is generated by combining data from the first and second input data in dependence on the motion detection data.

Abstract: Object tracking using image segmentation is disclosed. A captured image of a specimen is obtained. A segmented image is generated based on the captured image. The segmented image indicates segments corresponding to objects of interest. One or more target objects are identified from the objects of interest in the segmented image. Objects of interest most similar, in position and/or shape, to target objects shown in a previous image may be identified. Alternatively, objects of interest that are associated with connecting vectors most similar to the connecting vectors that connect the target objects in a previous image may be identified. A movement vector is drawn from a target object position in the previous image to a target object position in the segmented image. A field of view of the microscope is moved, with respect to the specimen, according to the movement vector to capture another image of the specimen.

Abstract: The invention relates to computation of optical flow using event-based vision sensors.

Abstract: There is provided a projection indication device that generates a projection image to be projected on parcel based on sensing information of the parcel, the device including: a processor; and a memory, in which by cooperating with the memory, the processor specifies parcel based on a distance image of the parcel included in sensing information, tracks the parcel based on a color image of the parcel included in the sensing information, and tracks the parcel based on the distance image of the parcel in a case where the color image of the parcel on which a projection image is projected includes a white region expressed in white which is not an original gradation.

Abstract: A system and method for pipeline data acquisition may include a software program that can autonomously review new and legacy videos collected by camera-equipped robotic systems from inside the pipelines, and automatically detect and categorize different features. Three-dimensional (3-D) point clouds may also be generated using software algorithms that stitch together like features in different video frames.

Abstract: Provided is an information processing device including an acquisition unit that acquires a first captured image, a second captured image, and a distance to a subject, and a derivation unit that derives an imaging position distance which is a distance between the first imaging position and the second imaging position, on the basis of a plurality of pixel coordinates for specifying a plurality of pixels of more than three pixels which are present in the same planar region as an emission position irradiated with the directional light beam on the real space and correspond to the position on the real space in each of the first captured image and the second captured image which are acquired by the acquisition unit, emission position coordinates which are derived on the basis of the distance acquired by the acquisition unit, a focal length of an imaging lens, and dimensions of imaging pixels.

Abstract: A gyroscope-based system and method is disclosed. Image information data is collected; a mechanical arm working environment image is modeled; a heat source supply apparatus and a gyroscope are provided at the mechanical arm proximal end; the position of the proximal end is accurately measured by accurately tracking the heat source at the proximal end; the relative position of the mechanical arm distal end is accurately calculated by using high-precision angle information measured by the high-precision gyroscope in combination with a number-theoretic formula. The disclosure provides separately determining the position of the distal end, or for assisting other algorithms or apparatuses that track the position of the mechanical arm distal end in error correction and calibration of the position of the mechanical arm distal end. The position of the mechanical arm distal end can be continuously and dynamically tracked in real time, and virtualized in the corresponding image system.

Abstract: In one of the exemplary embodiments, the disclosure is directed to a depth estimation apparatus including a first type of sensor for generating a first sensor data; a second type of sensor for generating a second sensor data; and a processor coupled to the first type of sensor and the second type of sensor and configured at least for: processing the first sensor data by using two stage segmentation algorithms to generate a first segmentation result and a second segmentation result; synchronizing parameters of the first segmentation result and parameters of the second sensor data to generate a synchronized second sensor data; fusing the first segmentation result, the synchronized second sensor data, and the second segmentation result by using two stage depth estimation algorithms to generate a first depth result and a second depth result.

Abstract: Methods, systems, and devices for object detection are described. A device may extract features from an image and identify a region within the image for object detection. The device may apply an object model to a first set of features corresponding to positions within the region and one or more channels of the image. The first set of features may be selected so that the object model can detect a first orientation of an object. The device may also apply the object model to a second set of features, from the region, that are different from the first set of features. The second set of features may be selected so that the object model can detect a second orientation of the object (e.g., a flipped or rotated orientation of the object with respect to the first orientation).

Abstract: An orientation detection apparatus includes an input/output interface and a processor. The input/output interface acquires a captured image of a wheel of a vehicle. The processor detects the orientation of the vehicle on the basis of the position of the wheel in the captured image.

Abstract: A method of detecting objects on a support structure includes: obtaining a point cloud of the support structure supporting an object; obtaining a position of a back of the support structure in the point cloud; discarding a portion of the point cloud based on the position of the back of the support structure; generating a three-dimensional occupancy grid from the point cloud, the occupancy grid having a plurality of cells each indicating whether the cell is one of occupied and unoccupied; identifying a contiguous region of occupied cells in the occupancy grid corresponding to the object; and generating a detected object position based on the contiguous region.

Abstract: A parallax image in which a parallax is associated with each pixel is acquired from an image captured by a stereo camera. The parallax is voted to a two-dimensional matrix. A line is extracted from a coordinate system in which an X-axis indicates the parallax and a Y-axis indicates the pixel in the vertical direction. In the case where a first line and a second line are detected, an X-coordinate of the start point coordinates of the second line is larger than an X-coordinate of end point coordinates of the first line, and the difference between the slope of the first line and the slope of the second line falls within a permissible range, a determination section determines that a low position is present at a position farther from the movable body than a position in real space corresponding to a Y-coordinate of start point coordinates.

Abstract: A rearward lane display system includes an image sensor configured to capture one or more images including lane markers forward of a vehicle, one or more display mirrors, and a processor in communication with the image sensor and the display mirrors, where the processor is configured to process the one or more images to predict where the lane markers will appear rearward of the vehicle, and display representations of the predicted lane markers in the one or more display mirrors.

Abstract: The primary purpose of the present invention is to enable devices/machines/systems to perform an optimized video analytics on images and videos. The present invention focuses on detecting, tracking, and classifying objects in a scene. Here, the tracking is performed by comparing objects across at least two frames and then associating the objects based on a cost matrix. Some examples of the objects include, but are not limited to, persons, animals, vehicles, or any other articles or items.

Abstract: A head-mounted device (“HMD”) is configured to perform intrinsic and/or extrinsic calibration of the HMD's camera system by exploiting a displayed electronic image rendered on a separate display screen. A series of images are captured using one or more of the HMD's cameras. The displayed image is a known image that includes markers with known characteristics to the HMD. The known characteristics include known marker shapes and a number of coded or un-coded markers. Each image in the series captures the displayed image at a different angle or distance relative to another image in the series. The HMD then identifies, from within the series of images, two-dimensional image positions of the markers. The HMD uses the two-dimensional image positions and a determined three-dimensional position of the markers to perform a bundle adjustment used to subsequently determine a position and angular alignment of the separate display screen relative to the HMD.

Abstract: Techniques related to indirect sparse simultaneous localization and mapping (SLAM) are discussed. Such techniques include adaptively positioning a virtual camera relative to an estimated position of a physical camera within an environment to be mapped, projecting a depth error to an image plane corresponding to the adaptive camera position, and using the projected depth error to update a mapping of the environment.

Abstract: A dual-view angle image calibration method includes: performing feature matching on a first image pair to obtain a first feature point pair set, the first image pair including two images respectively photographed corresponding to two different view angles of a same scene; obtaining multiple different first fundamental matrices of the first image pair at least according to the first feature point pair set, and obtaining first image deformation information indicating relative deformation between the first image pair after mapping transformation is performed through the first fundamental matrices and the first image pair before the mapping transformation is performed; determining a first optimization fundamental matrix from the multiple first fundamental matrices at least according to the first image deformation information; and calibrating the first image pair according to the first optimization fundamental matrix.

Abstract: An information processing apparatus comprises a control unit configured to set a shift amount based on a bit width of data, for each layer of a network including a plurality of layers, a plurality of MAC (multiply-accumulate) units configured to execute MAC operations on a plurality of data and a plurality of filter coefficients of the layer, a plurality of shift operation units configured to shift a plurality of MAC operation results obtained by the plurality of MAC units based on the shift amount, and an adding unit configured to calculate a total sum of the plurality of MAC operation results shifted by the plurality of shift operation units.