Abstract: A three-dimensional (3D) printer includes a nozzle and a camera configured to capture a real image or a real video of a liquid metal while the liquid metal is positioned at least partially within the nozzle. The 3D printer also includes a computing system configured to perform operations. The operations include generating a model of the liquid metal positioned at least partially within the nozzle. The operations also include generating a simulated image or a simulated video of the liquid metal positioned at least partially within the nozzle based at least partially upon the model. The operations also include generating a labeled dataset that comprises the simulated image or the simulated video and a first set of parameters. The operations also include reconstructing the liquid metal in the real image or the real video based at least partially upon the labeled dataset.

Abstract: A method includes defining a model for a liquid while the liquid is positioned at least partially within a nozzle of a printer. The method also includes synthesizing video frames of the liquid using the model to produce synthetic video frames. The method also includes generating a labeled dataset that includes the synthetic video frames and corresponding model values. The method also includes receiving real video frames of the liquid while the liquid is positioned at least partially within the nozzle of the printer. The method also includes generating an inverse mapping from the real video frames to predicted model values using the labeled dataset. The method also includes reconstructing the liquid in the real video frames based at least partially upon the predicted model values.

Abstract: Embodiments described herein provide a system for generating semantically accurate synthetic images. During operation, the system generates a first synthetic image using a first artificial intelligence (AI) model and presents the first synthetic image in a user interface. The user interface allows a user to identify image units of the first synthetic image that are semantically irregular. The system then obtains semantic information for the semantically irregular image units from the user via the user interface and generates a second synthetic image using a second AI model based on the semantic information. The second synthetic image can be an improved image compared to the first synthetic image.

Abstract: A 3D printer includes a nozzle configured to jet a drop of liquid metal therethrough. The 3D printer also includes a light source configured to illuminate the drop with a pulse of light. A duration of the pulse of light is from about 0.0001 seconds to about 0.1 seconds. The 3D printer also includes a camera configured to capture an image, video, or both of the drop. The 3D printer also includes a computing system configured to detect the drop in the image, the video, or both. The computing system is also configured to characterize the drop after the drop is detected. Characterizing the drop includes determining a size of the drop, a location of the drop, or both in the image, the video, or both.

Abstract: A method includes illuminating a drop with a pulse of light from a light source. A duration of the pulse of light is from about 0.0001 seconds to about 0.1 seconds. The method also includes capturing an image, video, or both of the drop. The method also includes detecting the drop in the image, the video, or both. The method also includes characterizing the drop after the drop is detected. Characterizing the drop includes determining a size of the drop, a location of the drop, or both in the image, the video, or both.

Abstract: Embodiments described herein provide a system for generating synthetic images with localized editing. During operation, the system obtains a source image and a target image for image synthesis and selects a semantic element from the source image. The semantic element indicates a semantically meaningful part of an object depicted in the source image. The system then determines the style information associated with the source and target images. Subsequently, the system generates a synthetic image by transferring the style of the semantic element from the source image to the target image based on the feature representations. In this way, the system can facilitate localized editing of the target image.

Abstract: A 3D printer includes a nozzle and a camera configured to capture an image, a video, or both of a plurality of drops of liquid metal being jetted through the nozzle. The 3D printer also includes a computing system configured to measure a signal proximate to the nozzle based at least partially upon the image, the video, or both. The computing system is also configured to determine one or more metrics that characterize a behavior of the drops based at least partially upon the signal.

Abstract: A method includes capturing a video of a plurality of drops being jetted through a nozzle of a printer. The method also includes measuring a signal proximate to the nozzle based at least partially upon the video. The method also includes determining one or more metrics that characterize a behavior of the drops based at least partially upon the signal.

Abstract: A method of image annotation includes selecting a plurality of annotation models related to an annotation task for an image, obtaining a candidate annotation map for the image from each of the plurality of annotation models, and selecting at least one of the candidate annotation maps to be displayed via a user interface, the candidate annotation maps comprising suggested annotations for the image. The method further includes receiving user selections or modifications of at least one of the suggested annotations from the candidate annotation map and generating a final annotation map based on the user selections or modifications.

Abstract: A method of labeling a dataset of input samples for a machine learning task includes selecting a plurality of pre-trained machine learning models that are related to a machine learning task. The method further includes processing a plurality of input data samples through each of the pre-trained models to generate a set of embeddings. The method further includes generating a plurality of clusterings from the set of embeddings. The method further includes analyzing, by a processing device, the plurality of clusterings to extract superclusters. The method further includes assigning pseudo-labels to the input samples based on analysis.

Abstract: One embodiment can include a system for providing an image-capturing recommendation. During operation the system receives, from a mobile computing device, one or more images. The one or more images are captured by one or more cameras associated with the mobile computing device. The system analyzes the received images to obtain image-capturing conditions for capturing images of a target within a physical space; determines, based on the obtained image-capturing conditions and a predetermined image-quality requirement, one or more image-capturing settings; and recommends the determined one or more image-capturing settings to a user.

Abstract: Methods of removing text from digital video or still images are disclosed. An image processing system receives an input image set defining a region of interest (ROI) that contains text. The system determines an input background color for the ROI. The system applies a text infilling function to remove text from the ROI to yield a preliminary output image set. The system may determine a residual corrective signal that corresponds to a measurement of background color error between the input set and the preliminary output set. The system may apply the residual corrective signal to the ROI in the preliminary output set to yield a final output set that does not contain the text. Alternatively, the system may remove background from the ROI of the input set before text infilling, then return background to the ROI after the text infilling.

Abstract: Methods of removing text from digital video or still images are disclosed. An image processing system receives an input image set defining a region of interest (ROI) that contains text. The system determines an input background color for the ROI. The system applies a text infilling function to remove text from the ROI to yield a preliminary output image set. The system may determine a residual corrective signal that corresponds to a measurement of background color error between the input set and the preliminary output set. The system may apply the residual corrective signal to the ROI in the preliminary output set to yield a final output set that does not contain the text. Alternatively, the system may remove background from the ROI of the input set before text infilling, then return background to the ROI after the text infilling.

Abstract: One embodiment can provide a system for detecting occlusion at an orifice of a three-dimensional (3D) printer nozzle while the printer nozzle is jetting liquid droplets. During operation, the system uses one or more cameras to capture an image of the orifice of the printer nozzle while the 3D printer nozzle is jetting liquid droplets. The system performs an image-analysis operation on the captured image to identify occluded regions within the orifice of the 3D printer nozzle, compute an occlusion fraction based on the determined occluded regions, and generate an output based on the computed occlusion fraction, thereby facilitating effective maintenance of the 3D printer.

Abstract: Embodiments described herein provide a system for localized contextual video annotation. During operation, the system can segment a video into a plurality of segments based on a segmentation unit and parse a respective segment for generating multiple input modalities for the segment. A respective input modality can indicate a form of content in the segment. The system can then classify the segment into a set of semantic classes based on the input modalities and determine an annotation for the segment based on the set of semantic classes.

Abstract: One embodiment can provide a system for detecting occlusion at an orifice of a three-dimensional (3D) printer nozzle while the printer nozzle is jetting liquid droplets. During operation, the system uses one or more cameras to capture an image of the orifice of the printer nozzle while the 3D printer nozzle is jetting liquid droplets. The system performs an image-analysis operation on the captured image to identify occluded regions within the orifice of the 3D printer nozzle, compute an occlusion fraction based on the determined occluded regions, and generate an output based on the computed occlusion fraction, thereby facilitating effective maintenance of the 3D printer.

Abstract: A mobile hyperspectral camera system is described. The mobile hyperspectral camera system comprises a mobile host device comprising a processor and a display: a plurality of cameras, coupled to the processor, configured to capture images in distinct spectral bands; and a hyperspectral flash array, coupled to the processor, configured to provide illumination to the distinct spectral bands. A method of implementing a mobile hyperspectral camera system is also described.

Abstract: One embodiment can include a system for providing an image-capturing recommendation. During operation the system receives, from a mobile computing device, one or more images. The one or more images are captured by one or more cameras associated with the mobile computing device. The system analyzes the received images to obtain image-capturing conditions for capturing images of a target within a physical space; determines, based on the obtained image-capturing conditions and a predetermined image-quality requirement, one or more image-capturing settings; and recommends the determined one or more image-capturing settings to a user.

Abstract: A method for predicting the realism of an object within an image includes generating a training image set for a predetermined object type. The training image set comprises one or more training images at least partially generated using a computer. A pixel level training spatial realism map is generated for each training image of the one or more training images. Each training spatial realism map configured to represent a perceptual realism of the corresponding training image. A predictor is trained using the training image set and the corresponding training spatial realism maps. An image of the predetermined object is received. A spatial realism map of the received image is produced using the trained predictor.

Abstract: One embodiment can provide a system for detecting occlusion at an orifice of a three-dimensional (3D) printer nozzle while the printer nozzle is jetting liquid droplets. During operation, the system uses one or more cameras to capture an image of the orifice of the printer nozzle while the 3D printer nozzle is jetting liquid droplets. The system performs an image-analysis operation on the captured image to identify occluded regions within the orifice of the 3D printer nozzle, compute an occlusion fraction based on the determined occluded regions, and generate an output based on the computed occlusion fraction, thereby facilitating effective maintenance of the 3D printer.