Abstract: An image processing method including obtaining image data. The image data includes a plurality of image data values. The image processing method also includes processing the image data, thereby generating output data. Processing the image data includes applying a convolution operation to the plurality of image data values using a kernel including a plurality of coefficients. Applying the convolution operation includes obtaining a sum of image data values of the plurality of image data values that correspond respectively to coefficients of the plurality of coefficients that each have a common coefficient value. Applying the convolution operation also includes multiplying the sum by the common coefficient value.

Abstract: A method for image segmentation comprises receiving volumetric image data for an anatomical region and generating a first volumetric patch from the volumetric image data. The method also comprises generating a second volumetric patch from the first volumetric patch by weighting at least one of a plurality of volumetric units in the first volumetric patch and receiving the second volumetric patch as an input to a convolutional neural network. The weighting that at least one of the plurality of volumetric units includes applying a weight based on a foreground structure classification. The method also comprises conducting a down-sampling filter process and conducting an up-sampling filter process within the convolutional neural network.

Abstract: A method of training an action selection neural network to perform a demonstrated task using a supervised learning technique. The action selection neural network is configured to receive demonstration data comprising actions to perform the task and rewards received for performing the actions. The action selection neural network has auxiliary prediction task neural networks on one or more of its intermediate outputs. The action selection policy neural network is trained using multiple combined losses, concurrently with the auxiliary prediction task neural networks.

Abstract: Disclosed herein are methods and systems for detection and discrimination of optical signals from a densely packed substrate. These have broad applications for biomolecule detection near or below the diffraction limit of optical systems, including in improving the efficiency and accuracy of polynucleotide sequencing applications.

Abstract: Described herein is a method for identifying an interference pigment in a coating. Also described herein is a corresponding device and a computer-readable medium.

Abstract: A method for training a pattern recognizer to identify regions of interest in unstained images of tissue samples is provided. Pairs of images of tissue samples are obtained, each pair including an unstained image of a given tissue sample and a stained image of the given tissue sample. An annotation (e.g., drawing operation) is then performed on the stained image to indicate a region of interest. The annotation information, in the form of a mask surrounding the region of interest, is then applied to the corresponding unstained image. The unstained image and mask are then supplied to train a pattern recognizer. The trained pattern recognizer can then be used to identify regions of interest within novel unstained images.

Abstract: Embodiments of the present techniques provide apparatus and methods for analysing intracoronary images, for example to predict the likelihood of a disease, disease presentation or event, and/or to track performance of a drug or other treatment. The method may comprise: for each image in the set of images of a coronary artery: classifying the image, using a first neural network, for the presence or absence of diseased tissue; when the image is classified as having diseased tissue present, classifying the image, using a second neural network, for the presence or absence of an artefact; determining whether to analyse the image based on the classifying steps; when the image is to be analysed, analysing the image by identifying, using a third neural network, one or more features of interest in a coronary artery tissue; and measuring each identified feature of interest.

Abstract: Methods and apparatus to monitor environments are disclosed. Example audience measurement devices disclosed herein execute, in connection with a first frame of data, a three-dimensional recognition analysis on an object detected in an environment within a threshold distance from a sensor. Disclosed example audience measurement devices also detect that the object has moved outside the threshold distance from the sensor in a second frame subsequent to the first frame. Disclosed example audience measurement devices further execute a two-dimensional recognition analysis on the object in the second frame.

Abstract: The disclosure provides a bill identification method, device, electronic device and a computer-readable storage medium. The method includes: obtaining an image of a bill to be identified; using a pre-trained area identification model to identify a final payment area of the bill in the image; using a pre-trained character identification model to identify the final payment amount in the final payment area. By applying the solution provided by the present disclosure, it is possible to realize automatic identification of the payment amount on the bill, and the efficiency of bill processing is improved.

Abstract: Systems and methods to train a cell object detector are described.

Abstract: Provided are an image fusion method and apparatus, and a portable terminal. The method comprises: obtaining several aligned images; respectively calculating gradient information of each image; setting a mask image of each image, and generating a target gradient image; performing a gradient operation on the target gradient image, and obtaining a target Laplacian image; performing a deconvolution transform on the Laplacian image, and generating a fused panoramic image. The technical solution generates a Laplacian image by performing gradient information calculations on several images, and then performs a deconvolution transform to generate a fused panoramic image, thereby eliminating image stitching color differences, and implementing a better image fusion effect.

Abstract: A system for detecting synthetic videos may include a server, a plurality of weak classifiers, and a strong classifier. The server may be configured to receive a prediction result from each of a plurality of weak classifiers; and send the prediction results from each of the plurality of weak classifiers to a strong classifier. The weak classifiers may be trained on real videos and known synthetic videos to analyze a distinct characteristic of a video file; detect irregularities of the distinct characteristic; generate a prediction result associated with the distinct characteristic, the prediction result being a prediction on whether the video file is synthetic; and output the prediction result to the server. The strong classifier may be trained to receive the prediction results of the plurality of weak classifiers from the server; analyze the prediction results; and determine if the video file is synthetic based on the prediction results.

Abstract: A face recognition system including: a reading unit that reads identification information from a medium carried by an authentication subject; an image capturing unit that acquires an image; a face detection unit that detects, as a detected face image, a face image from the image acquired; a face matching unit that, when a registered face image associated with the identification information read by the reading unit is present, matches the detected face image against the registered face image and matches, against the registered face image, the detected face image captured before the reading unit reads the identification information; and a registration unit that, when the registered face image associated with the identification information read is not present, registers, as the registered face image, the detected face image before the reading unit reads the identification information.

Abstract: The invention includes a system and method for obtaining high-resolution 3D images of objects. The system includes three cameras and three light sources that have different wavelengths (e.g. a red light source, a blue light source and a green light source). Each camera simultaneously captures a color image of the object. A processor separates each of the red light images, the blue light images and the green light images into separate monochrome images using each of the red light source, blue light source and green light source. The quality of the images are not subject to limited resolution of conventional RBG images. Because three different wavelengths of light are used, the surface can be accurately imaged, regardless of its characteristics (e.g. reflectivity and transparency). The system is well suited for industrial uses that require a high volume of objects, particularly those of mixed material, to be rapidly inspected for defects as small as a few microns.

Abstract: The present invention belongs to the technical field of petroleum exploitation engineering, and discloses a 3D modeling method for pore-filling hydrate sediment based on a CT image. Indoor remolding rock cores or in situ site rock cores without hydrate can be scanned by CT; a sediment matrix image stack and a pore image stack are obtained by gray threshold segmentation; then, a series of pore-filling hydrate image stacks with different saturations are constructed through image morphological processing of the pore image stack such as erosion, dilation and image subtraction operation; and a series of digital rock core image stacks of the pore-filling hydrate sediment with different saturations are formed through image subtraction operation and splicing operation to provide a relatively real 3D model for the numerical simulation work of the basic physical properties of a reservoir of natural gas hydrate.

Abstract: The present disclosure provides a method for detecting a lane line, a vehicle and a computing device. The method includes: generating an optical flow image in accordance with a series of event data from a dynamic vision sensor coupled to a vehicle; determining an initial search region including a start point of the lane line in accordance with the optical flow image; determining a center of gravity of the initial search region; determining a new search region through an offsetting operation on the center of gravity; determining a center of gravity of the new search region; repeating the steps of determining a new search region and determining a center of gravity of the new search region iteratively to acquire centers of gravity of a plurality of search regions; and determining the lane line in accordance with the centers of gravity of the plurality of search regions.

Abstract: A medical image processing apparatus includes: an obtaining unit configured to obtain a first image that is a motion contrast en-face image of an eye to be examined; and an image quality improving unit configured to generate a second image with at least one of lower noise and higher contrast than the obtained first image using the obtained first image as input data that is input into an image quality improving engine, wherein the image quality improving engine includes a machine learning engine that has been obtained by using training data including a second image with at least one of lower noise and higher contrast than a first image that is a motion contrast en-face image of an eye to be examined.

Abstract: An image processing method includes obtaining a sample image, a category label of the sample image, and a label value of the category label. The method further includes calling a preset image processing model to perform segmentation processing on the sample image to obtain at least two sub-regions. The method further includes calling the preset image processing model to perform category prediction on the sample image to obtain a predicted value of the category label. The method further includes updating a network parameter of the preset image processing model according to center coordinates of the sub-regions, the label value of the category label, and the predicted value of the category label. The method further includes performing iterative training on the preset image processing model according to the updated network parameter, to obtain a target image processing model.

Abstract: A computer-implemented method for processing images to determine EI site status is provided. The method includes image processing of an aerial image by two EI feature recognition models. A first EI feature recognition model recognizes a first EI feature and a second EI feature recognition model recognizes a second EI feature. The results of each model are further used to determine a composite indication of EI site status.

Abstract: A method of substitutional neural residual compression is performed by at least one processor and includes estimating motion vectors, based on a current image frame and a previous reconstructed image frame, obtaining a predicted image frame, based on the estimated motion vectors and the previous reconstructed image frame, and subtracting the obtained predicted image frame from the current image frame to obtain a substitutional residual. The method further includes encoding the obtained substitutional residual, using a first neural network, to obtain an encoded representation, and compressing the encoded representation.