Abstract: Methods and systems for identifying events of interest in images from imagers in an edge computing network, including: detecting a movement based on image processing in images captured by the imager, cropping an area of the at least one image where the movement is detected, applying a first machine learning (ML) algorithm having a deep neural network architecture to classify an object of interest in the cropped area of the at least one image, sending the at least one image to a server, when the first ML algorithm classifies the object of interest, applying a second ML algorithm having a deep neural network architecture to detect the object of interest in the at least one image where the object of interest is detected based on context analysis.

Abstract: The present invention relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining whether determined damage to a vehicle is consistent with information provided as to the cause of the damage to the vehicle. Aspects and/or embodiments seek to provide a computer-implemented method for determining whether damage to a vehicle, which is determined using images of the damage to the vehicle, is consistent with information documenting the cause of the damage to the vehicle, for example insurance claim data or repair shop proposed repair data.

Abstract: A method for inspecting images on print products in a printing material processing machine includes recording and digitizing produced print products using at least one image sensor in an image inspection process of a print job using an image inspection system. The recorded digital prints that have been created are compared to a digital reference image by using a computer and, if deviations are found between the recorded digital prints and the digital reference image, print products found to be defective are removed. The image sensor records the produced print products from at least two different viewing angles and/or viewing distances and the computer evaluates the at least two recorded digital prints as a function of the at least two viewing angles and/or viewing distances and assesses them in terms of deviations. A device for inspecting images on print products is also provided.

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: Systems and methods for automated detection of changes in extent of structures using imagery are disclosed, including a non-transitory computer readable medium storing computer executable code that when executed by a processor cause the processor to: align, with an image classifier model, a structure shape of a structure at a first instance of time to pixels within an aerial image depicting the structure captured at a second instance of time; assess a degree of alignment between the structure shape and the pixels, so as to classify similarities between the structure depicted within the pixels and the structure shape using a machine learning model to generate an alignment confidence score; and determine an existence of a change in the structure based upon the alignment confidence score indicating a level of confidence below a predetermined threshold level of confidence that the structure shape and the pixels within the aerial image are aligned.

Abstract: A computer-implemented method of generating electronic documents is described. The method comprises receiving a plurality of scanned documents for a plurality of vehicles; providing the plurality of scanned documents to a neural network model that outputs respective class identifiers of the plurality of scanned documents; for each scanned document of the plurality of scanned documents, extracting data from the scanned document according to a corresponding class identifier, and associating the scanned document and the extracted data with an identified vehicle of the plurality of vehicles, wherein the identified vehicle is identified by the extracted data; and generating an electronic document for the identified vehicle using the extracted data.

Abstract: An image depth determining method and a living body identification method, a circuit, a device, and a medium. The image depth determining method includes obtaining pixel coordinates of a feature point pair associated with an object point on a subject, determining a first straight line passing through the origin of a first camera coordinate system of a first camera based on first pixel coordinates and intrinsic parameters of the first camera, determining a second straight line passing through the origin of a second camera coordinate system of a second camera based on second pixel coordinates and intrinsic parameters of the second camera, and determining the depth of the object point based on the first straight line, the second straight line, and extrinsic parameters describing a relative position relationship between the first camera and the second camera.

Abstract: Exemplary systems and methods to extract, transform, and save to memory features from a training and a test dataset at extraction layers in a machine-learning model. For each data element in the training dataset, at each extraction layer: feature maps are created and grouped by k unique data labels to construct a set of k class-conditional distributions. For each data element in the datasets: distance sets between each feature map of each extraction layer and the extraction layer's class-conditional distributions are calculated and reduced to distance summary metrics. A drift test statistic for each extraction layer is computed between the datasets by comparing the extraction layer's distance summary metric distributions of the test dataset to distance summary metric distributions of the training dataset. The measure of drift between the datasets is computed by combining the test statistics of the extraction layers through a mathematical transform.

Abstract: A method and an apparatus for processing an image are provided. A method may include: acquiring a to-be-processed foggy image, and performing minimum value filtering on the to-be-processed foggy image to obtain a grayscale image of the to-be-processed foggy image; selecting, in a descending order of pixel values, a target number of pixel points from the grayscale image, and determining an image area corresponding to the selected pixel points from the to-be-processed foggy image; acquiring pixel values of the pixel points included in the image area, and determining an atmospheric light value based on the acquired pixel values; and performing image defogging processing on the to-be-processed foggy image based on the grayscale image and the atmospheric light value to obtain a processed image. The effect of image display and the efficiency of image processing can be improved.

Abstract: An image processing system which corrects a text obtained by optical character recognition (OCR) using a neural network which has performed learning based on a falsely recognized portion of OCR and a text near the falsely recognized portion is provided. The image processing system acquires a neural network model which has been trained based on learning data in which first text information included in print data and second text information acquired by performing optical character recognition (OCR) processing on an image that is based on the print data are associated with each other, acquires an image obtained by a scanner, acquires third text information which is generated by performing OCR processing on the image obtained by the scanner, and outputs fourth text information according to inputting of the third text information based on the neural network model.

Abstract: This disclosure provides systems, methods, and apparatus detecting defects in a substrate. An image of the substrate is compared with a reference image to identify potential defects. Images corresponding to the potential defects are processed sequentially by a set of classifiers to generate a set of images that include a defect. The set of classifiers can be arranged to have increasing accuracy. A subset of the images corresponding to the potential defects is processed by a type classifier that can determine the type, size, and location of the defect in the images. The defects can be further processed to determine the severity of the defects based on the location of the defects on the substrate.

Abstract: Systems and methods are provided for generating a base visual score for each candidate image of a plurality of images received by a computing system, based on the scene type of each image. For each candidate image, the computing system multiplies the base visual score by a feature importance weight to generate a first visual score, adds respective scene type bonus points to the first visual score to generate a second visual score, and adds diversity scoring points to the second visual score to generate a final visual score for each candidate image. The computing system ranks the candidate images based on the final visual scores and provides a specified number of the top-ranked candidate images to be displayed on a display of the computing device.

Abstract: A computer architecture for artificial image generation is disclosed. According to some aspects, a computing machine receives a voxel model of a target object. The target object is to be recognized using an image recognizer. The computing machine generates, based on the voxel model, a set of TSB (target shadow background-mask) images of the target object. The computing machine receives, at an auto-encoder, a set of real images of the target object. The computing machine generates, using an auto-encoder and based on the set of real images, one or more artificial images of the target object based on the set of TSB images. The computing machine provides, as output, the generated one or more artificial images of the target object.

Abstract: A memory stores shape information representing a shape of an object. A processor detects a plurality of feature lines from an image of an object that has been captured by an image capturing device in a manufacturing process of manufacturing the object, and produces a plurality of combinations by correlating each of a plurality of line segments in the shape information and each of the plurality of feature lines with each other. Next, the processor performs classification of each of the combinations into an identified combination that has a certain correlation result or an unidentified combination that has an uncertain correlation result. The processor also changes a result of the classification for the identified combination and the unidentified combination according to a reliability of each of the plurality of combinations and determines a degree of progress of the manufacturing process by using the identified combination included in the changed result.

Abstract: A method and a system are described for detection of anomalies in surfaces, such as pipes. The method includes receiving an input comprising surface material type and plurality of frames of a real-time video stream associated with the surface. The method includes eliminating unwanted frames based on magnitude of 2-Dimensional optical flow vectors of the plurality of frames. The method includes identifying potential anomaly frames based on contours in a dense map created using a magnitude of displacement of each pixel of a frame. The method includes detecting in real-time anomalies in anomaly frames from potential anomaly frames based on trained models selected from a Model Mapping Table. The method includes classifying anomalies in the anomaly frames into anomaly classes using one or more deep learning techniques. The method includes generating a health report comprising anomalies in anomaly frames associated with the surface and providing health report to a user.

Abstract: A radiation image display apparatus that constitutes a radiation imaging system includes a displayer and a hardware processor that acquires image data of a dynamic image constituted of a plurality of frame images, image data of an analysis dynamic image obtained by applying predetermined image processing to the image data of the dynamic image and image data of a related dynamic image which is related to the dynamic image or the analysis dynamic image respectively, and causes the displayer to display the related dynamic image together with the dynamic image and the analysis dynamic image.

Abstract: The embodiments of the present disclosure disclose an image processing method and device. The image processing method comprises transforming a first image to obtain a plurality of second images; obtaining feature maps of each of the second images by performing feature extraction on the second images using a first machine learning unit selected from a group including at least one first machine learning unit; and inputting the feature maps of each of the second images to a second machine learning unit to obtain a processing result of the first image.

Abstract: An image processor (IP) and a related method. The processor (IP) comprises an input interface (IN) for receiving an input sequence (F) of frames acquired by an imaging apparatus (IA). A temporal noise filter (NP) of the processor (IP) is configured to bidirectionally process the input sequence into at least two noise processed sequences (FLR, FRL). A merger (SUM) of the processor (IP) is configured to merge said at least two noise processed sequences into an output sequence (Formula (A)).

Abstract: Systems and method are described for medical image segmentation. A medical image of a patient in a first domain is received. The medical image comprises one or more anatomical structures. A synthesized image in a second domain is generated from the medical image of the patient in the first domain using a generator of a task driven generative adversarial network. The one or more anatomical structures are segmented from the synthesized image in the second domain using a dense image-to-image network of the task driven generative adversarial network. Results of the segmenting of the one or more anatomical structures from the synthesized image in the second domain represent a segmentation of the one or more anatomical structures in the medical image of the patient in the first domain.

Abstract: A method for explainable active learning, to be used for an object detector, by using a deep autoencoder is provided. The method includes steps of an active learning device (a) (i) inputting acquired test images into the object detector to detect objects and output bounding boxes, (ii) cropping regions, corresponding to the bounding boxes, in the test images, (iii) resizing the test images and the cropped images into a same size, and (iv) inputting the resized images into a data encoder of the deep autoencoder to output data codes, and (b) (i) confirming reference data codes corresponding to the number of the resized images less than a counter threshold by referring to a data codebook, (ii) extracting specific data codes from the data codes, (iii) selecting specific test images as rare samples, and (iv) updating the data codebook by referring to the specific data codes.