Abstract: A method of labeling a dataset includes inputting a testing set comprising a plurality of input data samples into a plurality of pre-trained machine learning models to generate a set of embeddings output by the plurality of pre-trained machine learning models. The method further includes performing an iterative cluster labeling algorithm that includes generating a plurality of clusterings from the set of embeddings, analyzing the plurality of clusterings to identify a target embedding with a highest duster quality, analyzing the target embedding to determine a compactness for each of the plurality of clusterings of the target embedding, and identifying a target cluster among the plurality of clusterings of the target embedding based on the compactness. The method further includes assigning pseudo-labels to the subset of the plurality of input data samples that are members of the target duster.

Abstract: A method for determining motional branch current in an ultrasonic transducer of an ultrasonic surgical device over multiple frequencies of a transducer drive signal. The method may comprise, at each of a plurality of frequencies of the transducer drive signal, oversampling a current and voltage of the transducer drive signal, receiving, by a processor, the current and voltage samples, and determining, by the processor, the motional branch current based on the current and voltage samples, a static capacitance of the ultrasonic transducer and the frequency of the transducer drive signal.

Abstract: One embodiment provides a system that facilitates efficient collection of training data. During operation, the system obtains, by a recording device, a first image of a physical object in a scene which is associated with a three-dimensional (3D) world coordinate frame. The system marks, on the first image, a plurality of vertices associated with the physical object, wherein a vertex has 3D coordinates based on the 3D world coordinate frame. The system obtains a plurality of second images of the physical object in the scene while changing one or more characteristics of the scene. The system projects the marked vertices on to a respective second image to indicate a two-dimensional (2D) bounding area associated with the physical object.

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 system determines an input video and a first annotated image from the input video which identifies an object of interest. The system initiates a tracker based on the first annotated image and the input video. The tracker generates, based on the first annotated image and the input video, information including: a sliding window for false positives; a first set of unlabeled images from the input video; and at least two images with corresponding labeled states. A semi-supervised classifier classifies, based on the information, the first set of unlabeled images from the input video. If a first unlabeled image is classified as a false positive, the system reinitiates the tracker based on a second annotated image occurring in a frame prior to a frame with the false positive. The system generates an output video comprising the input video displayed with tracking on the object of interest.

Abstract: A system is provided which obtains images of a physical object captured by an AR recording device in a 3D scene. The system measures a level of diversity of the obtained images, for a respective image, based on at least: a distance and angle; a lighting condition; and a percentage of occlusion. The system generates, based on the level of diversity, a first visualization of additional images to be captured by projecting, on a display of the recording device, first instructions for capturing the additional images using the AR recording device. The system trains a model based on the collected data. The system performs an error analysis on the collected data to estimate an error rate for each image of the collected data. The system generates, based on the error analysis, a second visualization of further images to be captured. The model is further trained based on the collected data.

Abstract: A method and system are provided for an improved semantic segmentation using a multi-stream late fusion using pretrained encoders to encode disparate channels independently while also integrating selected image features at a more abstract level in order to provide improved localization and image classification.

Abstract: A system and method provide extractions of regions of interest from images hand annotated by reviewers by lifting annotations from images, filtering out text labels, reconstructing continuous closed boundaries, and marking the contained region.

Abstract: Techniques for determining print quality for a 3D printer are disclosed. An example method includes obtaining an image of a stream of material jetted from a nozzle of the 3D printer, and binarizing the image to distinguish background features from foreground features contained in the image. The method also includes identifying elements of jetted material in the foreground features, and computing statistical data characterizing the identified elements. The method also includes generating a quality score of jetting quality based on the statistical data and controlling the 3D printer based on the quality score. The quality score indicates a degree to which the elements of jetted material form droplets of a same size, shape, alignment, and jetting frequency.

Abstract: Image processing techniques for determining print quality for a 3D printer are disclosed. An example method includes obtaining an image of material jetted from a nozzle of the 3D printer. The method also includes binarizing the image to distinguish background features from foreground features contained in the image. The method also includes determining, by a processing device, a jetting quality based on the binarized image.

Abstract: A system is provided which obtains images of a physical object captured by an AR recording device in a 3D scene. The system measures a level of diversity of the obtained images, for a respective image, based on at least: a distance and angle; a lighting condition; and a percentage of occlusion. The system generates, based on the level of diversity, a first visualization of additional images to be captured by projecting, on a display of the recording device, first instructions for capturing the additional images using the AR recording device. The system trains a model based on the collected data. The system performs an error analysis on the collected data to estimate an error rate for each image of the collected data. The system generates, based on the error analysis, a second visualization of further images to be captured. The model is further trained based on the collected data.

Abstract: Embodiments described herein provide a system for facilitating dynamic assistance to a user in an augmented reality (AR) environment of an AR device. During operation, the system detects a first element of an object using an object detector, wherein the object is associated with a task and the first element is associated with a step of the task. The system then determines an orientation and an alignment of the first element in the physical world of the user, and an overlay for the first element. The overlay can distinctly highlight one or more regions of the first element and indicate how the first element fits in the object. The system then applies the overlay to the one or more regions of the first element at the determined orientation in the AR environment.

Abstract: A method of labeling a dataset includes inputting a testing set comprising a plurality of input data samples into a plurality of pre-trained machine learning models to generate a set of embeddings output by the plurality of pre-trained machine learning models. The method further includes performing an iterative cluster labeling algorithm that includes generating a plurality of clusterings from the set of embeddings, analyzing the plurality of clusterings to identify a target embedding with a highest duster quality, analyzing the target embedding to determine a compactness for each of the plurality of clusterings of the target embedding, and identifying a target cluster among the plurality of clusterings of the target embedding based on the compactness. The method further includes assigning pseudo-labels to the subset of the plurality of input data samples that are members of the target duster.

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: A system and method for forward path planning of cab trailer systems is provided. Movement of a robot moving forward is tracked and a trajectory of at least one trailer attached to a back of the robot is estimated separate from a path of the robot. The trajectory of the trailer is based on the movement of the robot. A test for collision of one of the trailers is performed by identifying an obstacle and determining a distance of each trailer from the obstacle based on estimated trajectory. The trajectory of the robot is moved away from the obstacle when the distance fails to satisfy a threshold distance from the obstacle.

Abstract: Embodiments described herein provide a system for assessing the surface of an object for detecting contamination or other defects. During operation, the system obtains an input image indicating the contamination on the object and generates a synthetic image using an artificial intelligence (AI) model based on the input image. The synthetic image can indicate the object without the contamination. The system then determines a difference between the input image and the synthetic image to identify an image area corresponding to the contamination. Subsequently, the system generates a contamination map of the contamination by highlighting the image area based on one or more image enhancement operations.

Abstract: Embodiments described herein provide a system for facilitating efficient dataset management. During operation, the system obtains a first dataset comprising a plurality of elements. The system then determines a set of categories for a respective element of the plurality of elements by applying a plurality of AI models to the first dataset. A respective category can correspond to an AI model. Subsequently, the system selects a set of sample elements associated with a respective category of a respective AI model and determines a second dataset based on the selected sample elements.

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: 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: 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.