Abstract: A system is disclosed, in accordance with one or more embodiment of the present disclosure. The system may include a controller including one or more processors configured to execute a set of program instructions. The set of program instructions may be configured to cause the processors to: receive images of a sample from a characterization sub-system; identify target clips from patch clips; prepare processed clips based on the target clips; generate encoded images by transforming the processed clips; sort the encoded images into a set of clusters; display sorted images from the set of clusters; receive labels for the displayed sorted images; determine whether the received labels are sufficient to train a deep learning classifier; and upon determining the received labels are sufficient to train the deep learning classifier, train the deep learning classifier via the displayed sorted images and the received labels.

Abstract: A method for inspecting nominally identical workpieces with manufacturing tolerances involves feeding a workpiece to an inspection device; applying a simultaneous translatory and rotational movement to the workpiece via the inspection device while illuminating the workpiece; repeatedly taking camera images of the workpiece during the simultaneous translatory and rotational movement as the workpiece moves across a camera window, the camera images consisting of pixels; setting workpiece parameters based on size and shape of the workpiece; assembling a composite image from the camera images based on the workpiece parameters; determining numerical brightness scores of the workpiece, comparing the determined numerical brightness scores with known numerical brightness scores; assigning a discrepancy score; setting a discrepancy score threshold; determining that the workpiece passes inspection when the discrepancy score is below the discrepancy score threshold; and that the workpiece fails the inspection when the di

Abstract: Systems and methods for detecting and marking quality defects on individual packaging products being processed through converting machines at high speeds are provided. An example system comprises a controller in communication with a first and second sensor as well as a first and second marker. The first and second sensors sense data corresponding to a first type and second type, respectively, of potential defects in the manufacture of the packaging product, enabling detection of a first- and second-type defect. The controller may be configured to trigger a first marker to mark the individual packaging product with a first mark for the first-type defect; and to trigger a second marker to mark the packaging product with a second mark for the second-type defect. Thus, the type of defects detected on an individual packaging product may be readily indicated on the individual packaging product itself.

Abstract: Systems and methods for real-time user verification in online education are disclosed. In certain example embodiments, user identifying information associated with a user and a request to access online education content may be received from a user device. A face template including historical facial image data for the user can be identified. Current facial image data can be compared to the face template to determine if a match exists. Biometric sensor data, such as heart rate data, may also be received for the user. The biometric sensor data may be evaluated to determine if the user is currently located at the user device. If the user is currently located at the user device and the current facial image data matches the face template, access to the online education content may be provided to the user at the user device.

Abstract: This application discloses an inspection method and an inspection machine for a display panel. The inspection method includes the steps of: taking a picture of a to-be-inspected display panel to obtain a to-be-inspected image of the to-be-inspected display panel; removing high-frequency information in the to-be-inspected image to obtain a high-frequency-removed image; calculating a difference between the to-be-inspected image and the high-frequency-removed image to obtain a difference image; making a determination regarding the difference image by comparing the difference image with a preset threshold, determining that the display panel fails the inspection if the difference image exceeds the preset threshold, and determining that the display panel passes the inspection if the difference image does not exceed the preset threshold.

Abstract: Described embodiments relate to a method comprising: determining a candidate document comprising image data and character data and extracting the image data and the character data from the candidate document. The method comprises providing, to an image-based numerical representation generation model, the image data, and generating, by the image-based numerical representation generation model, an image-based numerical representation of the image data. The method comprises providing, to a character-based numerical representation generation model, the character data; and generating, by the character-based numerical representation generation model, a character-based numerical representation of the character data.

Abstract: A basic pattern extracting unit (15) extracts a “basic pattern” for each human from detection points acquired by an acquiring unit (11). The “basic pattern” includes a “reference body region point” corresponding to a “reference body region type”, and base body region points corresponding to base body region types that are different from the reference body region type and that are different from each other. For example, the “basic pattern” includes at least one of the following two combinations. A first combination is a combination of the reference body region point corresponding to a neck as the reference body region type and two base body region points respectively corresponding to a left shoulder and a left ear as the base body region type. A second combination is a combination of body region points corresponding to a neck, a right shoulder and a right ear.

Abstract: The disclosure provides a plant blooming period broadcast method and system, and a computer-readable storage medium. The method comprises: receiving an image, and identifying a plant in the image to obtain the species of the plant; using a plant variety identification model corresponding to the species of the plant to identify the specific variety and blooming state of the plant; obtaining the photographing time and photographing position of the image, and determining the blooming period of the plant according to the photographing time and the blooming state; and marking the photographing position on a blooming period broadcast map as a viewing place of the plant, and correspondingly displaying the specific variety and blooming period of the plant.

Abstract: A method for tomographic imaging comprising acquiring [200] a set of one or more 2D projection images [202] and reconstructing [204] a 3D volumetric image [216] from the set of one or more 2D projection images [202] using a residual deep learning network comprising an encoder network, a transform module and a decoder network, wherein the reconstructing comprises: transforming [206] by the encoder network the set of one or more 2D projection images [202] to 2D features [208]; mapping [210] by the transform module the 2D features [208] to 3D features [212]; and generating [214] by the decoder network the 3D volumetric image [216] from the 3D features [212]. Preferably, the encoder network comprises 2D convolution residual blocks and the decoder network comprises 3D blocks without residual shortcuts within each of the 3D blocks.

Abstract: Provided is an apparatus for analyzing composition of electronic and electrical device part scraps which can determine a composition of part scraps in the electronic and electrical device part scraps in a short time, a device for processing electronic and electrical device part scraps, and a method for processing electronic and electrical device part scraps using those devices.

Abstract: An image processing method includes: obtaining a defect image of a wafer; processing the defect image to generate a rebuilt image; and when the rebuilt image comprises at least one object pattern, outputting the rebuilt image. The at least one object pattern corresponds to a part of the wafer. A non-transitory computer readable medium and an image processing system are also disclosed herein.

Abstract: According to one embodiment, a defect inspection apparatus includes a storage and a processor. The storage stores dictionary in which a first design image generated by software is associated with a first real image corresponding to the first design image. The processor acquires a second design image generated by the software. The processor searches for a similar first design image similar to the second design image. The processor generates a reference image that is a pseudo real image of a second inspection object, based on the second design data and free of defect, by using the first real image associated with the similar first design image. The first design image includes information of an image area wider than the first real image.

Abstract: A computing device stores a predictive model generated by a neural network training engine. The computing device receives first and second two-dimensional (2D) thermal images comprising temperature measurements from respective first and second infrared (IR) sensors. The first and second images have the same size. An image capturing visual field of the second IR sensor partially overlaps with an image capturing visual field of the first IR sensor. The computing device executes a neural network using a predictive model for generating outputs based on inputs. The inputs comprise the temperature measurements of the first and second images. The outputs comprise horizontal and vertical shifts defining a translation of the second image with respect to the first image. An overlapping area in the first image, having a rectangular shape and overlapping with the second image, is determined using the horizontal and vertical shifts.

Abstract: A method includes obtaining dual-pixel image data that includes a first sub-image and a second sub-image, and generating an in-focus image, a first kernel corresponding to the first sub-image, and a second kernel corresponding to the second sub-image. A loss value may be determined using a loss function that determines a difference between (i) a convolution of the first sub-image with the second kernel and (ii) a convolution of the second sub-image with the first kernel, and/or a sum of (i) a difference between the first sub-image and a convolution of the in-focus image with the first kernel and (ii) a difference between the second sub-image and a convolution of the in-focus image with the second kernel. Based on the loss value and the loss function, the in-focus image, the first kernel, and/or the second kernel, may be updated and displayed.

Abstract: System and method for determining physiological parameters of a person are disclosed. A physiological parameter may be obtained by analyzing a facial image of a person, and determining, from the facial image, a physiological parameter of the person by processing the facial image with a data processor. A neural network model such as regression deep learning convolutional neural network is used to predict the physiological parameter. An image processor screens out images which can't be recognized as facial images and adjust facial images to frontal facial images for predicting of physiological parameters.

Abstract: Embodiments of the present disclosure provide a detection method and apparatus, an electronic device, and a storage medium. In one form, the detection method includes: providing a layout graphic and a scan graphic; superimposing and comparing the layout graphic and the scan graphic, and extracting a sample non-overlapping pattern; encoding the sample non-overlapping pattern, to form sample coded data; using the sample coded data as input data of machine learning, to obtain a detection model library; and detecting a defect point of a to-be-detected device by using the detection model library. The present disclosure can improve the accuracy of defect point analysis, thereby accelerating the development of technology and improving the production efficiency.

Abstract: A method for detecting defects on a sample based on a defect inspection apparatus is provided. In the method, an image data set that includes defect data and non-defect data is organized. A convolutional neural network (CNN) model is defined. The CNN model is trained based on the image data set. The defects on the sample are detected based on inspection data of the defect inspection apparatus and the CNN model. The sample includes uniformly repeating structures, and the inspection data of the defect inspection apparatus is generated by filtering out signals of the uniformly repeating structures of the sample.

Abstract: A processing system includes a machine tool including a measuring unit which outputs measurement information for calculating a shape of a processing object, a control unit which generates position information related to a position of the measuring unit at the time of measuring the processing object and outputs the generated position information and a generation time signal indicating a time at which the position information is generated, an acquisition unit which acquires the output position information and the generation time signal, an estimation unit which estimates the time at which the position information is generated on the basis of a time at which the acquisition unit acquires the generation time signal, and a shape calculation unit which calculates a shape of the processing object on the basis of the measurement information, the position information, and the time estimated by the estimation unit.

Abstract: Methods and systems for deep learning alignment for semiconductor applications are provided. One method includes transforming first actual information for an alignment target on a specimen from either design data to a specimen image or a specimen image to design data by inputting the first actual information into a deep generative model such as a GAN. The method also includes aligning the transformed first actual information to second actual information for the alignment target, which has the same information type as the transformed first actual information. The method further includes determining an offset between the transformed first actual information and the second actual information based on results of the aligning and storing the determined offset as an align-to-design offset for use in a process performed on the specimen.

Abstract: There are provided methods and apparatus for in-loop artifact filtering. An apparatus includes an encoder for encoding an image region. The encoder has at least two filters for successively performing in-loop filtering to respectively reduce at least a first and a second type of quantization artifact.