Abstract: A marking region image is obtained by cutting out the part corresponding to a marking region from an article image obtained by imaging an article to be inspected. Then, whether or not the marking is properly provided is determined by performing a character recognition of a marking part for a marking region image. Further, an image of an article having no marking and no defect is stored as a reference image, whereas a marking periphery image obtained by removing the image of the marking part from the marking region image is compared to the reference image. By that comparison, whether or not any defect is included in the marking peripheral part of the marking region except the marking part is determined.

Abstract: Example aspects of the present disclosure are directed to systems and methods for learning data augmentation strategies for improved object detection model performance. In particular, example aspects of the present disclosure are directed to iterative reinforcement learning approaches in which, at each of a plurality of iterations, a controller model selects a series of one or more augmentation operations to be applied to training images to generate augmented images. For example, the controller model can select the augmentation operations from a defined search space of available operations which can, for example, include operations that augment the training image without modification of the locations of a target object and corresponding bounding shape within the image and/or operations that do modify the locations of the target object and bounding shape within the training image.

Abstract: Defects of interest and nuisance can be separated into different segments which enables detection of the defects of interest in only one segment. A region of an image can be segmented into a plurality of segments. A range attribute of the segments can be determined. Thresholding can be used to select one of the segments from the range attribute. The segment that is selected can be dilated.

Abstract: The present disclosure provides a system and a method for controlling a semiconductor manufacturing apparatus. The system includes an inspection unit capturing at least one image of a wafer, a sensor interface generating at least one input signal for a database server, and a control unit. The control unit includes a front-end subsystem, a calculation subsystem, and a message and tuning subsystem. The front-end subsystem receives the at least one input signal from the database server and performs a front-end process to generate a data signal. The calculation subsystem performs an artificial intelligence analytical process to determine, according to the data signal, whether damage marks have been caused by the semiconductor manufacturing apparatus and to generate an output signal. The message and tuning subsystem generates an alert signal and a feedback signal according to the output signal and transmits the alert signal to a user.

Abstract: An example operation of depth map generation includes one or more of, simultaneously capturing a main-off camera image and an auxiliary-off camera image with an unpowered flash, sparse depth mapping an object based on the main-off camera image and the auxiliary-off camera image, capturing a main-on camera image with a powered flash, foreground probability mapping the object based on the main-off camera image and the main-on camera image and dense depth mapping the object based on the sparse depth map and the foreground probability map.

Abstract: System, methods, and other embodiments described herein relate to improving depth estimates for monocular images using a neural camera model that is independent of a camera type. In one embodiment, a method includes receiving a monocular image from a pair of training images derived from a monocular video. The method includes generating, using a ray surface network, a ray surface that approximates an image character of the monocular image as produced by a camera having the camera type. The method includes creating a synthesized image according to at least the ray surface and a depth map associated with the monocular image.

Abstract: A model-based method for quantifying a specimen. The method includes providing a specimen, capturing images of the specimen while illuminated by multiple spectra at different nominal wavelengths, and exposures, and classifying the specimen into various class types comprising one or more of serum or plasma portion, settled blood portion, gel separator (if used), air, tube, label, or cap; and quantifying of the specimen. Quantifying includes determining one or more of: a location of a liquid-air interface, a location of a serum-blood interface, a location of a serum-gel interface, a location of a blood-gel interface, a volume and/or a depth of the serum or plasma portion, or a volume and/or a depth of the settled blood portion. Quality check modules and specimen testing apparatus adapted to carry out the method are described, as are other aspects.

Abstract: A system, method, and computer program product for verifying signatures. The system includes at least one processing component, at least one memory component, and a reference storage comprising a set of reference signatures. The system also includes a model generator configured to generate a signature model based on the set of reference signatures. Further, the system includes a verification component configured to receive a signature, and determine whether the signature is valid.

Abstract: A system for inspecting features of an airframe, the system including a feature inspection device configured to measure an aspect of a first feature and a tracking subsystem configured to determine a position of the feature inspection device when the feature inspection device measures the aspect of the first feature. The system is configured to determine a position of the first feature on the airframe via the feature inspection device and the tracking subsystem, the determination of the position of the first feature being independent from the measurement of the aspect of the first feature.

Abstract: A 3D scanner system includes a scanning device capable of recording first and second data sets of a surface of an object when operating in a first configuration and a second configuration, respectively. A measurement unit is configured for measuring a distance from the scanning device to the surface. A control controls an operation of the scanning device based on the distance measured by the measurement unit, where the scanning device operates in the first configuration when the measured distance is within a first range of distances from the surface and the scanning device operates in the second configuration when the measured distance is within a second range of distances; and a data processor is configured to combine one or more first data sets and one or more second data sets to create a combined virtual 3D model of the object surface.

Abstract: Provided is a grain gloss measurement apparatus capable of accurately measuring gloss of grain. A grain gloss measurement apparatus includes a light source to emit light to a measurement region for grain in an oblique direction, a light receiver to receive the light reflected from the measurement region, and a gloss value calculation device to calculate a gloss value of the grain based on the reflected light received by the light receiver. The light source includes a first light source to emit light to the measurement region from one side and a second light source to emit light from another side that is opposite to the first light source across the measurement region. The light emitted from the first light source and the light emitted from the second light source have different wavelengths.

Abstract: A computer-implemented method of localizing an image of a person captured using a camera is provided, the person in the field of view of a camera, comprising: obtaining the image captured using a camera, the image comprising the person within a bounding box; determining at least one slant value associated with the person within the bounding box; determining head image coordinates and feet image coordinates for the person using the at least one slant value; and localizing the person by projecting the head image coordinates to a head plane and the feet image coordinates for the person to a ground plane.

Abstract: In a computer-implemented method and associated tangible non-transitory computer-readable medium, an image of a damaged vehicle may be analyzed to generate a repair estimate. A dataset populated with digital images of damaged vehicles and associated claim data may be used to train a deep learning neural network to learn damaged vehicle image characteristics that are predictive of claim data characteristics, and a predictive similarity model may be generated. Using the predictive similarity model, one or more similarity scores may be generated for a digital image of a newly damaged vehicle, indicating its similarity to one or more digital images of damaged vehicles with known damage level, repair time, and/or repair cost. A repair estimate may be generated for the newly damaged vehicle based on the claim data associated with images that are most similar to the image of the newly damaged vehicle.

Abstract: Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, determine reflectance intensity values for at least a subset of the pixels, determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values, and generate a predicted or assessed healing parameter associated with the wound over a predetermined time interval.

Abstract: The present disclosure relates to a method for guiding a path of an electronic device and an electronic device. The method for guiding a path according to the present disclosure includes: acquiring a top-view image; extracting guiding information in a lane from lane information extracted from the top-view image; and guiding a path of a vehicle using the extracted lane information and the guiding information in the lane. According to the present disclosure, it is possible to promote safe driving by guiding a possible traveling direction on the path to a driver in real time through image analysis of a traveling road. In addition, it is possible to provide more detailed path guidance by providing the path guidance reflecting the necessity of lane change by recognizing the lane of the driving path.

Abstract: A system and method of measuring misregistration in the manufacture of semiconductor device wafers is disclosed. A first layer and the second layer are imaged in a first orientation with a misregistration metrology tool employing light having at least one first wavelength that causes images of both the first periodic structure and the second periodic structure to appear in at least two planes that are mutually separated by a perpendicular distance greater than 0.2 ?m. The first layer and the second layer are imaged in a second orientation with the misregistration metrology tool employing light having the at least one first wavelength that causes images of both the first periodic structure and the second periodic structure to appear in the at least two planes. At least one parameter of the misregistration metrology tool is adjusted based on the resulting analysis.

Abstract: Systems and methods for iris authentication are disclosed. In one aspect, a deep neural network (DNN) with a triplet network architecture can be trained to learn an embedding (e.g., another DNN) that maps from the higher dimensional eye image space to a lower dimensional embedding space. The DNN can be trained with segmented iris images or images of the periocular region of the eye (including the eye and portions around the eye such as eyelids, eyebrows, eyelashes, and skin surrounding the eye). With the triplet network architecture, an embedding space representation (ESR) of a person's eye image can be closer to the ESRs of the person's other eye images than it is to the ESR of another person's eye image. In another aspect, to authenticate a user as an authorized user, an ESR of the user's eye image can be sufficiently close to an ESR of the authorized user's eye image.

Abstract: Computer systems and associated methods are disclosed to implement a model development environment (MDE) that allows a team of users to perform iterative model experiments to develop machine learning (ML) media models. In embodiments, the MDE implements a media data management interface that allows users to annotate and manage training data for models. In embodiments, the MDE implements a model experimentation interface that allows users to configure and run model experiments, which include a training run and a test run of a model. In embodiments, the MDE implements a model diagnosis interface that displays the model's performance metrics and allows users to visually inspect media samples that were used during the model experiment to determine corrective actions to improve model performance for later iterations of experiments. In embodiments, the MDE allows different types of users to collaborate on a series of model experiments to build an optimal media model.

Abstract: A projecting apparatus includes a projecting device, an image-capturing device and a processing device. The projecting device projects a reversible structured light code onto a surface. The image-capturing device captures the reversible structured light code projected onto the surface and obtains image data. The processing device is coupled to the projecting device and the image-capturing device. The processing device receives the image data, generates three-dimensional point cloud information by performing decoding on the image data, and obtains scanning shift information corresponding to the projecting device according to the three-dimensional point cloud information and three-dimensional information corresponding to the surface.

Abstract: For patient model estimation from surface data in a medical system, a stream or sequence of depth camera captures are performed. The fitting of the patient model is divided between different times or parts of the sequence, using the streaming capture to distribute processing and account for patient movement. Less manual involvement may be needed due to the regular availability of image captures. Subsequent fitting may benefit from previous fitting.