Abstract: In pose estimation from a depth sensor (12), depth information is matched (70) with 3D information. Depending on the shape captured in depth image information, different objects may benefit from more or less pose density from different perspectives. The database (48) is created by bootstrap aggregation (64). Possible additional poses are tested (70) for nearest neighbors already in the database (48). Where the nearest neighbor is far, then the additional pose is added (72). Where the nearest neighbor is not far, then the additional pose is not added. The resulting database (48) includes entries for poses to distinguish the pose without overpopulation. The database (48) is indexed and used to efficiently determine pose from a depth camera (12) of a given captured image.

Abstract: The present embodiments relate to generating synthetic depth data. By way of introduction, the present embodiments described below include apparatuses and methods for modeling the characteristics of a real-world light sensor and generating realistic synthetic depth data accurately representing depth data as if captured by the real-world light sensor. To generate accurate depth data, a sequence of procedures are applied to depth images rendered from a three-dimensional model. The sequence of procedures simulate the underlying mechanism of the real-world sensor. By simulating the real-world sensor, parameters relating to the projection and capture of the sensor, environmental illuminations, image processing and motion are accurately modeled for generating depth data.

Abstract: The present embodiments relate to automatically estimating a three]dimensional pose of an object from an image captured using a camera with a structured light sensor. By way of introduction, the present embodiments described below include apparatuses and methods for training a system for and estimating a pose of an object from a test image. Training and test images are sampled to generate local image patches. Features are extracted from the local image patches to generate feature databased used to estimate nearest neighbor poses for each local image patch. The closest nearest neighbor pose to the test image is selected as the estimated three]dimensional pose.

Abstract: Systems and methods are disclosed for processing an image to detect anomalous pixels. An image classification is received from a trained convolutional neural network (CNN) for an input image with a positive classification being defined to represent detection of an anomaly in the image and a negative classification being defined to represent absence of an anomaly. A backward propagation analysis of the input image for each layer of the CNN generates an attention mapping that includes a positive attention map and a negative attention map. A positive mask is generated based on intensity thresholds of the positive attention map and a negative mask is generated based on intensity thresholds of the negative attention map. An image of segmented anomalous pixels is generated based on an aggregation of the positive mask and the negative mask.

Abstract: Quinacridone pigments that are surface-functionalized with glycidyl methacrylate, maleic anhydride, or 4-methacryloxyethyl trimellitic anhydride to create a functionalized pigment. The functional groups are then activated to bond hydrophobic polymers, thereby coating the pigment with the hydrophobic polymers. The quinacridone pigments can be used for a variety of applications. They are well-suited for use in electro-optic materials, such as electrophoretic media for use in electrophoretic displays.

Abstract: Systems, methods, and computer-readable media are disclosed for determining feature representations of 2.5D image data using deep learning techniques. The 2.5D image data may be synthetic image data generated from 3D simulated model data such as 3D CAD data. The 2.5D image data may be indicative of any number of pose estimations/camera poses representing virtual or actual viewing perspectives of an object modeled by the 3D CAD data. A neural network such as a convolution neural network (CNN) may be trained using the 2.5D image data as training data to obtain corresponding feature representations. The pose estimations/camera poses may be stored in a data repository in association with the corresponding feature representations. The learnt CNN may then be used to determine an input feature representation from an input 2.5D image and index the input feature representation against the data repository to determine matching pose estimation(s).

Abstract: Electro-optic assemblies and related materials (e.g., adhesive) tier use therein are generally provided. The adhesive layer may comprise an end-capped polyurethane. Some adhesive layers comprise two or more reactive functional groups (e.g., reactive functional groups configured to react with one or more curing species such that, for example, at least one of the two or more functional groups forms a crosslink). The adhesive may also comprise a chain-extending reagent that includes one or more reactive functional groups. In some embodiments, the adhesive is cured by reacting one or more reactive functional groups with one or more curing species. Curing the adhesive may comprise two or more curing steps. In some embodiments the adhesive layer may comprise one or more cross-linkers.

Abstract: Systems, methods, and computer-readable media are disclosed for automated identification of parts of a parts assembly using image data of the parts assembly and 3D simulated model data of the parts assembly. The 3D simulated model data may be 3D CAD data of the parts assembly. An image of the parts assembly is captured by a mobile device and sent to a back-end server for processing. The back-end server determines a feature representation corresponding to the image and searches a repository to locate a matching feature representation stored in association with a corresponding pose estimation. The matching pose estimation is rendered as an overlay on the image of the parts assembly, thereby enabling automated identification of parts within the image or some user-selected portion of the image.

Abstract: A method for identifying a feature in a first image comprises establishing an initial database of image triplets, and in a pose estimation processor, training a deep learning neural network using the initial database of image triplets, calculating a pose for the first image using the deep learning neural network, comparing the calculated pose to a validation database populated with images data to identify an error case in the deep learning neural network, creating a new set of training data including a plurality of error cases identified in a plurality of input images and retraining the deep learning neural network using the new set of training data. The deep learning neural network may be iteratively retrained with a series of new training data sets. Statistical analysis is performed on a plurality of error cases to select a subset of the error cases included in the new set of training data.

Abstract: Method and system for detecting defects on surface of object are presented. An imaging device captures images of surface of object under ambient and dark field illumination conditions. The images are processed with a plurality of image operations to detect area of potential defect at location on surface of object based on predictable pattern consisting of bright and shadow regions. Kernels are defined corresponding to configurations of dark field illumination sources to enhance detecting potential defect. Areas of potential defect are cut from processed images to sub images. Sub images are stitched together to generate hypothesis of potential defect at location on surface of object. The hypothesis is classified with a classifier to determine whether the potential defect is true defect. The classifier is trained with training data having characteristics of true defect. The method provides efficient automated detection of micro defects on surface of object.

Abstract: Method and system for detecting defects on surface of object are presented. An imaging device captures images of surface of object under ambient and dark field illumination conditions. The images are processed with a plurality of image operations to detect area of potential defect at location on surface of object based on predictable pattern consisting of bright and shadow regions. Kernels are defined corresponding to configurations of dark field illumination sources to enhance detecting potential defect. Areas of potential defect are cut from processed images to sub images. Sub images are stitched together to generate hypothesis of potential defect at location on surface of object. The hypothesis is classified with a classifier to determine whether the potential defect is true defect. The classifier is trained with training data having characteristics of true defect. The method provides efficient automated detection of micro defects on surface of object.

Abstract: Method and system for detecting line defects on surface of object are presented. An imaging device captures images of surface of object under ambient and dark field illumination conditions. The images are processed with a plurality of image operations to detect areas of potential defects based on predictable pattern consisting of bright and shadow regions. Areas of potential defect are cut from processed images to sub images. Sub images are stitched together to generate hypotheses of potential defects at locations on surface of object. The hypotheses are classified to determine whether the potential defects are true defects at the locations. Line defect is detected by refining line segments detected on the processed image based on criteria. The criteria include distance from the true defects to the line segments and slops between the true defects and the line segments are less than threshold values.

Abstract: Quinacridone pigments that are surface-functionalized with glycidyl methacrylate, maleic anhydride, or 4-methacryloxyethyl trimellitic anhydride to create a functionalized pigment. The functional groups are then activated to bond hydrophobic polymers, thereby coating the pigment with the hydrophobic polymers. The quinacridone pigments can be used for a variety of applications. They are well-suited for use in electro-optic materials, such as electrophoretic media for use in electrophoretic displays.

Abstract: Method and system for detecting line defects on surface of object are presented. An imaging device captures images of surface of object under ambient and dark field illumination conditions. The images are processed with a plurality of image operations to detect areas of potential defects based on predictable pattern consisting of bright and shadow regions. Areas of potential defect are cut from processed images to sub images. Sub images are stitched together to generate hypotheses of potential defects at locations on surface of object. The hypotheses are classified to determine whether the potential defects are true defects at the locations. Line defect is detected by refining line segments detected on the processed image based on criteria. The criteria include distance from the true defects to the line segments and slops between the true defects and the line segments are less than threshold values.

Abstract: Electro-optic assemblies and related materials (e.g., adhesive) for use therein are generally provided. The adhesive layer may comprise an end-capped polyurethane. Some adhesive layers comprise two or more reactive functional groups (e.g., reactive functional groups configured to react with one or more curing species such that, for example, at least one of the two or more functional groups forms a crosslink). The adhesive may also comprise a chain-extending reagent that includes one or more reactive functional groups. In some embodiments, the adhesive is cured by reacting one or more reactive functional groups with one or more curing species. Curing the adhesive may comprise two or more curing steps. In some embodiments the adhesive layer may comprise one or more cross-linkers.

Abstract: Embodiments of the present invention provide a computer-implemented method for training an RGB-D classifier for a scene classification task. The method receives task-relevant labeled depth data, task-irrelevant RGB-D data, and a given trained representation in RGB. The method simulates an RGB representation using only the task-irrelevant RGB-D data. The method builds a joint neural network using only the task-irrelevant RGB-D data and the task-relevant labeled depth data.

Abstract: Aspects include receiving a request to perform an image classification task in a target domain. The image classification task includes identifying a feature in images in the target domain. Classification information related to the feature is transferred from a source domain to the target domain. The transferring includes receiving a plurality of pairs of task-irrelevant images that each includes a task-irrelevant image in the source domain and in the target domain. The task-irrelevant image in the source domain has a fixed correspondence to the task-irrelevant image in the target domain. A target neural network is trained to perform the image classification task in the target domain. The training is based on the plurality of pairs of task-irrelevant images. The image classification task is performed in the target domain and includes applying the target neural network to an image in the target domain and outputting an identified feature.

Abstract: A system and method is disclosed for development of a control application for a controller of an automation system. The controller receives sensor signals associated with perception of a first real component during an execution of the control application program. Activity of a virtual component, including interaction with the real first component, is simulated, the virtual component being a digital twin of a second real component designed for the work environment and absent in the work environment. Virtual data is produced in response to the simulated activity of the virtual component. A control application module determines parameters for development of the control application program using the sensor signals for the first real component and the virtual data. An AR display signal for the work environment is rendered and displayed based on a digital representation of the virtual data during an execution of the control application program.

Abstract: A method for inspecting an object to assist in determining whether the object has a surface defect. The method includes moving the object in a first direction and illuminating the object under ambient lighting conditions. The method also includes capturing at least one image of the object under the ambient lighting conditions while the object moves in the first direction. In addition, the object is illuminated under object lighting conditions and at least one image of the object under the object lighting conditions is captured while the object moves in the first direction to provide at least one object image. Further, the method includes selecting at least one object image having at least one indication of a possible defect to provide images having defect candidates and comparing the defect candidates with previously defined characteristics associated with the defect to facilitate determination of whether a defect exists.

Abstract: For human body representation, bone length or other size characteristic that varies within the population is incorporated into the geometric model of the skeleton. The geometric model may be normalized for shape or tissue modeling, allowing modeling of the shape without dedicating aspects of the data-driven shape model to the length or other size characteristic. Given the same number or extent of components of the data-driven shape model, greater or finer details of the shape may be modeled since components are not committed to the size characteristic.