Abstract: In a real-time traceability method of a width of a defect based on divide-and-conquer provided by the present invention, through the calibration transfer function, the multidimensional eigenvector analysis technology based on the electromagnetic field simulation database of defect scattered dark-field imaging and the adaptive threshold segmentation method, the real-time traceability of the width of the defect greater than and close to the diffraction limit of the system is performed, respectively. The extreme random tree regression model is trained by multidimensional eigenvector analysis technology based on the multidimensional eigenvectors in the electromagnetic field simulation database of the defect scattered dark-field imaging.

Abstract: To previously predict learning performance in accordance with the labeling status of learning data. Provided is an information processing device including a data distribution presentation unit that performs dimensionality reduction on input learning data to generate a data distribution diagram related to the learning data, a learning performance prediction unit that predicts learning performance on the basis of the data distribution diagram and a labeling status related to the learning data, and a display control unit that controls a display related to the data distribution diagram and the learning performance. The data distribution diagram includes overlap information about clusters including the learning data and information about the number of pieces of the learning data belonging to each of the clusters.

Abstract: The present invention discloses a smart counting method and system in manufacturing, specifically in custom clothing or fabric manufacturing. The smart counting method and system uses a camera to feed real-time image data of a working platform where a worker takes a unfinished clothing or fabric, processes the clothing or fabric, and puts the finished clothing or fabric in a finished pile to a processing unit. The processing unit automatically starts a new work order and counts the number of finished products in this work order by using computer vision techniques.

Abstract: Techniques for detecting label shift and adjusting training data of predictive models in response are provided. In an embodiment, a first machine-learned model is used to generate a predicted label for each of multiple scoring instances. The first machine-learned model is trained using one or more machine learning techniques based on a plurality of training instances, each of which includes an observed label. In response to detecting a shift in observed labels, for each segment of one or more segments in multiple segments, a portion of training data that corresponds to the segment is identified. For each training instance in a subset of the portion of training data, the training instance is adjusted. The adjusted training instance is added to a final set of training data. The machine learning technique(s) are used to train a second machine-learned model based on the final set of training data.

Abstract: Various embodiments of systems and methods allow a system to identify subsets of items by mixing and matching identified features in one or more other items. A system can identify features of items in an item database. The system can then calculate “fingerprints” of these features which are vectors describing the characteristics of the features. The system can present a collection of items and a user can select an item of the collection. The user can then select positive features to include in a search and/or negative features to include in the search. The system can then do a search of the database for items that contain features similar to those positive features and do not contain features similar to those negative features. The user can select features through a variety of means.

Abstract: Systems and methods for automatically grading a user device are provided. Such systems and methods can include (1) a lighting element positioned at an angle relative to a platform, (2) an imaging device positioned at the angle relative to the platform such that light emitted from the lighting element and a field of view of the imaging device form a right angle where the light emitted from the lighting element and the field of view meet at a user device when the user device is positioned at a predetermined location on the platform, and (3) control circuitry that can activate the lighting element, instruct the imaging device to capture an image of a screen of the user device while the user device is at the predetermined location and is being illuminated by the first lighting element, and parse the image to determine whether the screen is damaged.

Abstract: In order to acquire recognition environment information impacting the recognition accuracy of a recognition engine, an information processing device 100 comprises a detection unit 101 and an environment acquisition unit 102. The detection unit 101 detects a marker, which has been disposed within a recognition target zone for the purpose of acquiring information, from an image captured by means of an imaging device which captures images of objects located within the recognition target zone. The environment acquisition unit 102 acquires the recognition environment information based on image information of the detected marker. The recognition environment information is information representing the way in which a recognition target object is reproduced in an image captured by the imaging device when said imaging device captures an image of the recognition target object located within the recognition target zone.

Abstract: The information processing apparatus (2000) includes a feature point detection unit (2020), a determination unit (2040), an extraction unit (2060), and a comparison unit (2080). A feature point detection unit (2020) detects a plurality of feature points from the query image. The determination unit (2040) determines, for each feature point, one or more object images estimated to include the feature point. The extraction unit (2060) extracts an object region estimated to include the object in the query image in association with the object image of the object estimated to be included in the object region, on the basis of the result of the determination. The comparison unit (2080) cross-checks the object region with the object image associated with the object region and determines an object included in the object region.

Abstract: A method can include identifying a geolocation of an object in an image, the method comprising receiving data indicating a pixel coordinate of the image selected by a user, identifying a data point in a targetable three-dimensional (3D) data set corresponding to the selected pixel coordinate, and providing a 3D location of the identified data point.

Abstract: A method and system are provided for estimating tire tread depth, the method comprising: obtaining an image of the tire informative of tread and grooves embedded therein, wherein the image is acquired by an imaging device from a first angle relative to a horizontal direction perpendicular to tread surface, and the tire is illuminated by an illumination device from a second angle relative to the horizontal direction, causing a shadow section and an illuminated section at the bottom and/or sidewall of a groove, the first angle being smaller than the second angle, such that the image captures the illuminated section and at least part of the shadow section; performing segmentation on the image to obtain image segments corresponding to the illuminated section and the at least part of the shadow section; and estimating the tread depth based on the image segments, the groove width, and the second angle.

Abstract: A vision inspection system includes a sorting platform having an upper surface supporting parts for inspection, wherein the parts are configured to be loaded onto the upper surface of the sorting platform in a random orientation. The vision inspection system includes an inspection station including an imaging device. The vision inspection system includes a vision inspection controller receiving images and processing the images based on an image analysis model to determine inspection results for each of the parts. The vision inspection controller has a shape recognition tool configured to recognize the parts in the field of view regardless of the orientation of the parts on the sorting platform. The vision inspection controller has an AI learning module operated to customize and configure the image analysis model based on the images received from the imaging device.

Abstract: A method is disclosed that includes receiving, by a processing device, a plurality of images of a test assembly. The processing device selects a component in the test assembly and an image of the plurality of images of the test assembly as received. For the component as selected and the image as selected, the processing device compares a plurality of portions of the image as selected to a corresponding plurality of portions of a corresponding profile image and computing a matching score for each of the plurality of portions. The processing device selects a largest matching score from the matching score for each of the plurality of portions as a first matching score for the component as selected and the image as selected. The first matching score is stored for the component as selected and the image as selected.

Abstract: A machine learning model training method includes: training a machine learning model using features of each sample in a training set based on an initial first weight and an initial second weight. In one iteration, the method includes determining a first sample set in which a target variable is incorrectly predicted, and a second sample set in which a target variable is correctly predicted, based on a predicted loss of each sample; and determining overall predicted loss of the first sample set based on a predicted loss and a first weight of each sample in the first sample set. The method also includes updating the first weight and a second weight of each sample in the first sample set based on the overall predicted loss; and inputting the updated second weight, the features, and the target variable of each sample to the machine learning model, and initiating a next iteration.

Abstract: A measurement method comprises acquiring, using image sensors (Cji) for each object during its displacement, at least three radiographic images of the region to be inspected. The images are obtained from at least three radiographic projections of the region to be inspected, the directions of projection (Dji) of which are different from each other. A computer system is provided with an a priori geometric model of the region to be inspected for the series of objects. Using the computer system and considering a constant attenuation coefficient and, from the a priori geometric model, at least three radiographic images of the region to be inspected, a digital geometric model of the region to be inspected is determined. For each object of the series, from the digital geometric model of the region to be inspected, at least one linear dimension measurement of the region to be inspected is determined.

Abstract: An evaluation device evaluates an image clarity of a print image. The evaluation device includes: an object that is projected onto a measurement surface of a recording medium; an illuminator that makes the object projected onto the measurement surface; an imager that images the measurement surface on which the object has been projected to obtain image data; and a hardware processor that, based on a distribution of parameters related to a brightness of the image data, quantifies a degree of the image clarity into a numerical value and defines the numerical value as an evaluation value of the image clarity.

Abstract: The present disclosure provides techniques for optical inspection systems and methods for moving objects. In some embodiments, an optical inspection system includes: a first and a second image capturing device configured to acquire images from moving objects; a first and a second first-stage storage system; a first and a second second-stage processor; a second-stage storage system; a third-stage processor; and a third-stage storage system. In some embodiments, an optical inspection system, includes: a first and a second image capturing device; a first and a second volatile memory system; a first and a second second-stage processor; a third second-stage processor; and a third-stage storage system. The first and second second-stage processors can be configured to analyze the images from the image capturing devices. The third-stage processor or the third second-stage processor can be configured to process information from a processor and/or storage system and produce a report.

Abstract: The degree of concrete surface roughness contributes to the bond strength between two concrete surfaces for either new construction or repair and retrofitting of concrete structures. Provided are novel systems and methods with industrial application to quantify concrete surface roughness from images which may be obtained from basic cameras or smartphones. A digital image processing system and method with a new index for concrete surface roughness based on the aggregate area-to-total surface area is provided. A machine learning method applying a combination of advanced techniques, including data augmentation and transfer learning, is utilized to categorize images based on the classification given during the learning process. Both methods compared favorably to a well-established method of 3D laser scanning.

Abstract: Methods and systems for automating the management and processing of roof damage analysis. In some embodiments, methods and systems include obtaining data associated with a property. Methods and systems include using obtained data to generate a model of the property. Methods and systems include transmitting the model of the property to a user device. Methods and systems include receiving annotation data from the user device. Methods and systems include generating an estimate scope of repair for the property based on the annotation data.

Abstract: A multi-function biometric scanner is provided. The multi-function biometric scanner includes a housing that includes a dome-shaped or semi-dome shaped user interface, the user interface including a capacitive film for fingerprint capture that is disposed along an outer-surface of the housing and a plurality of biometric sensors that are disposed within the housing and that are configured to concurrently retrieve a plurality of biometrics from a user, each sensor being configured to measure a respective biometric of the plurality of biometrics. Fingerprints and the plurality of biometrics are compared to threat information in one or more threat databases to identify a person of interest.

Abstract: The degree of concrete surface roughness contributes to the bond strength between two concrete surfaces for either new construction or repair and retrofitting of concrete structures. Provided are novel systems and methods with industrial application to quantify concrete surface roughness from images which may be obtained from basic cameras or smartphones. A digital image processing system and method with a new index for concrete surface roughness based on the aggregate area-to-total surface area is provided. A machine learning method applying a combination of advanced techniques, including data augmentation and transfer learning, is utilized to categorize images based on the classification given during the learning process. Both methods compared favorably to a well-established method of 3D laser scanning.