Abstract: The present invention provides a display method, a display system, and a readable storage medium for plant disease diagnosis information. The method comprises: acquiring a plant image; using a disease location detection model to recognize and process the plant image, so as to determine whether the plant image has a suspected disease area; if a result recognized by the disease location detection model indicates that there is at least one suspected disease area, marking and displaying the suspected disease area according to a first preset mode; using a disease diagnosis model to recognize and process the plant image, so as to acquire species information and determine whether the plant image has a suspected disease; and if a result recognized by the disease diagnosis model indicates that there is at least one suspected disease, performing information display regarding the suspected disease according to a second preset mode.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a continuous kernel neural network that learns continuous reconstruction kernels to merge digital image samples in local neighborhoods and generate enhanced digital images from a plurality of burst digital images. For example, the disclosed systems can utilize an alignment model to align image samples from burst digital images to a common coordinate system (e.g., without resampling). In some embodiments, the disclosed systems generate localized latent vector representations of kernel neighborhoods and determines continuous displacement vectors between the image samples and output pixels of the enhanced digital image. The disclosed systems can utilize the continuous kernel network together with the latent vector representations and continuous displacement vectors to generated learned kernel weights for combining the image samples and generating an enhanced digital image.

Abstract: Apparatuses and methods train a model and then use the trained model to determine a global three dimensional (3D) position and orientation of a fiduciary marker. In the context of an apparatus for training a model, a wider field-of-view sensor is configured to acquire a static image of a space in which the fiducial marker is disposed and a narrower field-of-view sensor is configured to acquire a plurality of images of at least a portion of the fiducial marker. The apparatus also includes a pan-tilt unit configured to controllably alter pan and tilt angles of the narrower field-of-view sensor during image acquisition. The apparatus further includes a control system configured to determine a transformation of position and orientation information determined from the images acquired by the narrower field-of-view sensor to a coordinate system for the space for which the static image is acquired by the wider field-of-view sensor.

Abstract: The subject disclosure presents systems and methods for automatically selecting meaningful regions on a whole-slide image and performing quality control on the resulting collection of FOVs. Density maps may be generated quantifying the local density of detection results. The heat maps as well as combinations of maps (such as a local sum, ratio, etc.) may be provided as input into an automated FOV selection operation. The selection operation may select regions of each heat map that represent extreme and average representative regions, based on one or more rules. One or more rules may be defined in order to generate the list of candidate FOVs. The rules may generally be formulated such that FOVs chosen for quality control are the ones that require the most scrutiny and will benefit the most from an assessment by an expert observer.

Abstract: The disclosure herein describes providing signature data of an input document. Text data of the input document is obtained (e.g., OCR data generated from image data) and a first set of signature fields are identified using signature key-value pairs of the text data. A first subset of signed signature fields and a first subset of unsigned signature fields are determined based on mapping to a set of predicted values. A second set of signature fields are determined using a region prediction model applied to image data of the input document. Region images associated with the first subset of unsigned signature fields and with second set of signature fields are obtained and a second set of signed signature fields and a second set of unsigned signature fields are determined using a signature recognition model. Signature output data is provided including signed signature fields and/or unsigned signature fields.

Abstract: Methods for training a convolutional neural network to register images for electronic designs include inputting a first pair of images aligned in a first modality and a second pair of images aligned in a second modality. An affine transformation is generated with a convolutional neural network, using one image from the first pair of images and one image from the second pair of images. The one image from the first pair of images is in the first modality and the one image from the second pair of images is in the second modality. Methods for registering images for electronic designs include inputting a pair of images, wherein the pair of images comprises a computer aided design (CAD) image and a scanning electron microscope (SEM) image. The CAD image is registered to the SEM image, using a trained convolutional neural network. The trained convolutional neural network further comprises an affine transformation.

Abstract: A method is provided. The method includes: obtaining a picture to be processed, where the picture to be processed includes a plurality of pixels, and the plurality of pixels comprise first pixels for forming an image and second pixels for forming an image background; rotating the picture to be processed, where for each rotation angle, an intermediate picture is obtained; selecting at least two pictures from the picture to be processed and several intermediate pictures for calculating an area of a bounding box surrounding the image respectively; and removing second pixels outside the bounding box in a picture with the smallest area of bounding box to obtain a processed picture.

Abstract: Systems and techniques are described for performing supervised learning (e.g., semi-supervised learning, self-supervised learning, and/or mixed supervision learning) for optical flow estimation. For example, a method can include obtaining an image associated with a sequence of images and generating an occluded image. The occluded image can include at least one of the image with an occlusion applied to the image and a different image of the sequence of images with the occlusion applied. The method can include determining a matching map based at least on matching areas of the image and the occluded image and, based on the matching map, determining a loss term associated with an optical flow loss prediction associated with the image and the occluded image. The loss term may include a matched loss and/or other loss. Based on the loss term, the method can include training a network configured to determine an optical flow between images.

Abstract: A lung region can be appropriately divided with a region dividing device, method, and program for dividing a lung region included in a medical image. A bronchus extracting unit 30 of a region dividing unit 22 extracts a bronchus from a lung region included in a medical image acquired by imaging a subject including a lung. A first dividing unit 31 to a fourth dividing unit 34 identify a plurality of branch positions of the bronchus and divide, based on the branch positions, the lung region into a plurality of regions.

Abstract: Various image diagnostic systems, and methods of operating thereof, are disclosed herein. Example embodiments relate to operating the image diagnostic system to identify one or more tissue types within an image patch according to a hierarchical histological taxonomy, identifying an image patch associated with normal tissue, generating a pixel-level segmented image patch for an image patch, generating an encoded image patch for an image patch of at least one tissue, searching for one or more histopathological images, and assigning an image patch to one or more pathological cases.

Abstract: The present invention relates to method and apparatus for training a neural network for object recognition. A training method which includes inputting a training image set containing an object to be recognized, dividing the image samples in the training image set into simple samples and hard samples, for each kind of the image sample and the variation image sample, performing, a transitive transfer, calculating a distillation loss of the transferred student feature of the image sample relative to a teacher feature extracted from corresponding image sample of the other kind, classifying, the image sample, and calculating a classification loss of the image sample, calculating a total loss related to the training image set, and updating parameters of the neural network according to the calculated total loss.

Abstract: The invention concerns a method for determining a minimum value of L.A.S.E.R. energy necessary to form, by using a LASER source, a gas bubble in an ocular tissue, the method comprising: acquisition (200) of a reference image, emission (210) at a sampling point, of a L.A.S.E.R. beam, acquisition (220) of one (or more) current image(s) of the ocular tissue, detection (230) of one (or more) gas bubble(s) by comparing the current image(s) and the reference image, determination (240, 250) of the minimum value based on the detected gas bubble(s).

Abstract: An image processing device and method for more easily performing a write operation on an image. Write information corresponding to a feature of an image of processing target is searched for based on relevant information that associates the write information with the feature of the image. For example, it is furthermore possible to search for write information corresponding to the feature of an image of the processing target based on the learning result of the user's behavior. The present invention can be applied to, for example, an information processing device, an image processing device, electronic equipment, an image processing method, a program, or the like.

Abstract: Systems, devices, methods, and computer processing products for automatically checking for errors in segmentation (contouring) using heuristic and/or statistical evaluation methods.

Abstract: Disclosed are a software for analyzing images of a fertilized egg, the software providing a means for executing a process including: (a) a step of measuring the difference in area between the female pronucleus and the male pronucleus from images of a fertilized egg obtained in a period of 1 to 10 hours before the time of occurrence of male and female pronuclear membrane breakdown as a reference; (b) a step of measuring the difference in are between the female pronucleus and the male pronucleus from images of the fertilized egg obtained immediately before the time of occurrence of male and female pronuclear membrane breakdown as the reference; and (c) a step of storing the measured values of the area difference obtained in the step (a) and the area difference obtained in the step (b), to be readable at any time as needed, and an apparatus incorporating this software.

Abstract: A method for analyzing a rock sample includes segmenting a digital image volume corresponding to an image of the rock sample, to associate voxels in the digital image volume with a plurality of rock fabrics of the rock sample. The method also includes identifying a set of digital planes through the digital image volume. The set of digital planes intersects with each of the plurality of rock fabrics. The method further includes machining the rock sample to expose physical faces that correspond to the identified digital planes, performing scanning electron microscope (SEM) imaging of the physical faces to generate two-dimensional (2D) SEM images of the physical faces, and performing image processing on the SEM images to determine a material property associated with each of the rock fabrics.

Abstract: A processing plant, in particular for making respective products, in particular defined by articles or packages of respective articles, the articles being preferably in the form of articles of the tissue industry, the plant including at least one unit for detecting products, in particular defective products, especially defective articles and/or packages, preferably for detecting defective rolls and/or packs.

Abstract: The present invention provides a task interaction network which can jointly perform, based on multi parametric-magnetic resonance imaging scan images, a segmentation task to locate prostate cancer areas and a classification task to access aggressiveness of lesions. The task interaction network comprises a backbone network, an auxiliary segmentation branch, a classification branch having a lesion awareness module, and a main segmentation branch having a category allocation module. The auxiliary segmentation branch is utilized to predict an initial lesion mask as location guidance information for the classification branch to perform the classification task. The lesion awareness module is configured to refine the initial lesion mask to make it more accurate. Moreover, weights used in classification branch can serve as the category prototypes for generating category guidance features via the category allocation module to assist the main segmentation branch to perform the segmentation task.

Abstract: Devices, systems, methods, and computer-readable media for registering an electromagnetic registration of a luminal network to a 3D model of the luminal network include accessing a 3D model of a luminal network based on computed tomographic (CT) images of the luminal network, selecting a plurality of reference points within the 3D model of the luminal network, obtaining a plurality of survey points within the luminal network, dividing the 3D model of the luminal network and the luminal network into a plurality of regions, assigning a plurality of weights to the plurality of regions, determining an alignment of the plurality of reference points with the plurality of survey points based on the plurality of weights, and generating a registration based on the alignment, the registration enabling conversion of the plurality of survey points within the luminal network to points within the 3D model of the luminal network.

Abstract: Disclosed is a fluorescence image analyzing apparatus including a light source that emits light to a sample including a plurality of cells labeled with a fluorescent dye at a target site, an imaging unit that captures a fluorescence image of each of the cells that emit fluorescence by being irradiated with the light, a fluorescence image of the cell, and a processing unit that processes the fluorescence image captured by the imaging unit to acquire a bright spot pattern of fluorescence in the fluorescence image. The processing unit selects a reference pattern corresponding to a measurement item of the sample from a plurality of reference patterns corresponding to a plurality of measurement items and generates information used for determination of the sample based on the bright spot pattern of fluorescence in the fluorescence image and the selected reference pattern.