Abstract: A monitoring device includes an acquisition unit configured to acquire a captured image of a display panel of a control device configured to control an analyzer, an image storage unit configured to store the captured image, and a state determination unit configured to determine a state of the analyzer based on the captured image.

Abstract: A perception system comprises a segmenting neural network and an uncertainty detector. The segmenting neural network is configured and trained for segmentation of an input image pixel matrix to generate a segment composed of elements that correspond the pixels of the input image pixel matrix. Each element of the segment map is assigned to one of a plurality of object classes the segmenting neural network is trained for by way of class prediction. Elements being assigned to the same object class form a segment of the segment map. The uncertainty detector is configured to generate an uncertainty score map that is composed of elements that correspond the pixels of the input image pixel matrix. Each element of the uncertainty map has an uncertainty score that is determined by the uncertainty detector and reflects an amount of uncertainty involved in a class prediction for a corresponding element in the segment map.

Abstract: A method for identifying a jet regulator (1) with improved precision and robustness is provided using a captured photo of a hole pattern (4) of an outlet structure (3) of the jet regulator (1). The captured photo is first compared with different stored hole pattern types (15) in a computer-implemented manner in a first step, and based on an evaluation of the respective degree of a match between the captured photo and each hole pattern type (15), the hole pattern type (15) on which the captured hole pattern (4) is based is ascertained. Subsequently, in another step, the ascertained hole pattern type (15) and the captured hole pattern (4) are examined for deviations in a computer-implemented manner, and identification information which uniquely identifies the jet regulator (1) is ascertained in a computer-implement manner using detected or undetected deviations and is optionally output.

Abstract: Provided is a method of performing registration between a plurality of images of different dimensions. The method may be performed by one or more processors of a computing device, and may include primarily positioning a three-dimensional (3D) image by identifying a first landmark in the 3D image, generating a registration image by registration between the primarily positioned 3D image and a two-dimensional (2D) image, and secondarily positioning the registration image.

Abstract: In order to train a machine learning model such that the machine learning model makes inferences with higher accuracy, a training apparatus includes at least one processor, the at least one processor carries out: an acquiring process of acquiring training images; and a training process of for training the machine learning model with use of the training images, and treats a training image belonging to a subclass different from a subclass to which an image of interest belongs and belonging to a class the same as a class to which the image of interest belongs, as a quasi-positive sample, and a training apparatus belonging to a class different from the class to which the image of interest belong, as a negative sample, to train the machine learning model such that the degree of similarity between features of the image of interest and features of the quasi-positive sample is higher than the degree of similarity between the features of the image of interest and features of the negative sample.

Abstract: Metal artifacts are reduced in x-ray computed tomography (“CT”) images using a suitably trained neural network, such as a convolutional neural network (“CNN”). Virtual metal DATA objects are inserted to either the raw projection data or CT image data (e.g., from pre-procedural CT scans) to generate sets of matching artifact-corrupted and artifact-uncorrupted images, and a CNN, or other neural network, is trained to separate the contribution to each image pixel due to patient anatomy, metal object, or metal object-induced artifact. The contributions from metal object-induced artifacts can then be removed to generate a final, artifact-reduced image.

Abstract: A method for localizing structural connectivity biomarkers in neurological diseases, includes dividing a diffusion magnetic resonance imaging brain volume into a set of connected brain regions; extracting three-dimensional voxels along fiber connections which structurally connect the connected brain regions, wherein the brain regions comprise bundles of neurons; applying a deep neural network to diffusion magnetic resonance imaging features extracted from the three-dimensional voxels for each set of fiber connections which structurally connect brain regions; outputting a disease classification based on applying the deep neural network; and applying multi-instance learning to predict whether each fiber connection is indicative of a healthy brain or a diseased brain.

Abstract: A system, computer readable medium, apparatus and/or method for non-invasive, non-surgical, digital biopsy. The system, computer readable medium, apparatus and/or method accurately predicts benign kidney lesions from cancers in a patient. A processor may receive patient clinical factors, texture analysis of computer-tomographic imaging, and an artificial intelligence learning model. By implementing artificial intelligence, the processor may then predict or determine a probability of kidney cancer in the patient using the patient clinical factors, the texture analysis of computer-tomographic imaging, and the artificial intelligence learning model. Notably, the prediction is performed without needing invasive biopsy surgery and subsequent pathology analysis to arrive at a diagnosis but relies on radiomics metrics.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for mapping an electrical grid are disclosed. A method includes sampling multiple locations within a geographic region, executing a detection process for each location, the detection process including applying the set of images for the location as input to a machine learning (ML) model that is trained to identify electrical grid assets depicted within images taken from a combination of different perspectives and obtaining an output from the machine learning model that indicates whether a same electrical grid asset is identified in each of the images of the location. In response to an ML output that indicates a positive identification of the same electrical grid asset being depicted in a particular set of images of a particular location, the method further includes: selecting a number of sublocations within the region, and executing the detection process for each sublocation.

Abstract: An image processing apparatus which makes it possible to, during remote control, carry out suitable monitoring while suitably suppressing a transmission load. An image processing apparatus includes: an obtaining process for obtaining an image which has been captured from a movable body; an importance level estimating process for estimating levels of importance with respect to a respective plurality of regions included in the image; and a quality parameter determining process for determining quality parameters with respect to the respective plurality of regions with reference to the levels of importance.

Abstract: Provided is a method that addresses a problem of classifying cells through use of cell autofluorescence, the method including the steps of: acquiring autofluorescence information regarding a plurality of cells through use of an autofluorescence image regarding the plurality of cells; acquiring region information; extracting the autofluorescence information on individual cells from the autofluorescence information regarding the plurality of cells and the region information; and classifying the plurality of cells into two or more groups by performing clustering through use of the autofluorescence information on the individual cells.

Abstract: Examples described herein provide a method that includes performing an image analysis on an image of a layer of an object being manufactured by an additive manufacturing system to identify an exposed surface in the image of the layer. The method further includes performing a build simulation to generate a simulated distortion for the layer. The method further includes evaluating build data to determining a value of an influencing factor for the layer. The method further includes predicting at least one of a predicted distortion or a predicted re-coater interference for a next layer, using a machine learning model, based at least in part on the image analysis, the build simulation, and the build data. The method further includes implementing an action, based at least in part on the at least one of the predicted distortion or the predicted re-coater interference, to alter fabrication of the next layer.

Abstract: An image analysis server, an object counting method using the image analysis server, and an object counting system are provided, in which the object counting method using the image analysis server includes, by a user terminal, inputting an image including one or more objects, by an image analysis server, forming a plurality of boxes for each of the objects, and keeping only the number of boxes corresponding to the objects and deleting the other boxes of the plurality of boxes, and by the image analysis server, counting the number of the remaining boxes and transmitting the corresponding number of the boxes to the user terminal.

Abstract: Methods and systems for computing a surface normal vector for a point cloud are disclosed. A sparse range map is generated by projecting the point cloud. A vertical kernel and a horizontal kernel are used to generate a vertical kernel range map and a horizontal kernel range map from the sparse range map. To complete an empty entry in the sparse range map, a guidance range map is used compute a vertical gradient and a horizontal gradient. The empty entry is filled using a corresponding entry in the vertical kernel range map or in the horizontal kernel range map, based on a comparison between the vertical gradient and the horizontal gradient. A surface normal vector is computed using the completed dense range map.

Abstract: An image processing method includes using different filter weights to process a multi-scale feature map obtained by encoding an initial image to obtain a target residual image after obtaining the initial image, and performing target processing on the initial image and the target residual image to obtain a target output image.

Abstract: A method and device for generating an all-in-focus image are disclosed. A method of generating an all-in-focus image includes obtaining a scene image and a phase detection image by capturing a scene using a phase detection image sensor; determining a blur kernel based on the phase detection image and a particular mapping relationship indicating a depth and a blur kernel distribution; and generating the all-in-focus image by deblurring the scene image using the blur kernel.

Abstract: A method of monitoring synthetic gas emissions comprises obtaining a gas concentration image within a first area of interest from an overhead image acquisition device, detecting from the gas concentration image at least one gas plume of a synthetic gas emission, quantifying an emission rate of the detected gas plume, estimating an estimated point source of the detected gas plume, and attributing the detecting gas plume to an actual point source. A corresponding system for monitoring synthetic gas emissions comprises a memory storing a gas concentration image of a first area of interest, and a processor communicatively coupled thereto and configured to detect from the image of the gas concentration at least one gas plume of a synthetic gas emission, quantify an emission rate of the detected gas plume, estimate an estimated point source of the detected gas plume, and attribute the detecting gas plume to an actual point source.

Abstract: A product detection device is provided with: an image acquisition unit; a binarization unit; and a detection unit. The image acquisition unit acquires an image of shelves for displaying products. The binarization unit binarizes a region in the image into a product region where products are imaged and a non-product region where things other than the products are imaged. The detection unit detects the display state of products displayed on the shelves in accordance with the width of the binarized product region and the width of a gap region adjacent to the products.

Abstract: The luminances of an image are not only optimized for a particular maximum displayable luminance of a display, but also for a particular amount of light in the viewing environment in which the display is watched. A method of processing an input image to obtain an output image includes receiving a reference luminance mapping function as metadata associated with an input image, calculating an adapted luminance mapping function on the reference luminance mapping function; calculating a control parameter that specifies how much the adapted luminance mapping function deviates from the reference luminance function and is based on a maximum luminance of a display and a black level of the display; and applying the adapted luminance mapping function to at least a portion of the input pixel luminances so as to obtain output luminances for at least a portion of pixels of the output image.

Abstract: A method according to the present disclosure includes selecting a reference class from a plurality of classes, calculating a plurality of degrees of similarity between a feature spectrum corresponding to target training data and a plurality of the feature spectra belonging to the reference class, applying, to the plurality of degrees of similarity, a defectiveness function that is determined in advance, and calculating a defectiveness index with respect to the target training data, and determining whether the target training data is the defective data, based on a result of comparison between the defectiveness index and a threshold value.