Abstract: Embodiments herein relate to prediction, based on previous usage of a cloud-based computing resource by a user of one or more users of the cloud-based computing resource, future usage of the cloud-based computing resource. Based on the predicted future usage, embodiments relate to identifying that throttling of access to the cloud-based computing resource is to occur, and notifying the user of the throttling. Other embodiments may be described and/or claimed.

Abstract: Methods and systems to remotely operate robotic devices are provided. A number of embodiments allow users to remotely operate robotic devices using generalized consumer devices (e.g., cell phones). Additional embodiments provide for a platform to allow communication between consumer devices and the robotic devices. Further embodiments allow for training robotic devices to operate autonomously by training the robotic device with machine learning algorithms using data collected from scalable methods of controlling robotic devices.

Abstract: A data annotation apparatus for machine learning is provided, which includes a stimulus generation portion, a biometrics reading portion, and a data integration portion. The stimulus generation portion is configured to generate, and present to an agent, at least one stimulus based on a first data from a first machine learning dataset. The biometrics reading portion is configured to measure at least one response of the agent to the at least one stimulus, and to generate biometrics data based on the at least one response. The data integration portion is configured to integrate the biometrics data, data of the at least one stimulus, and data of the first machine learning dataset to thereby obtain a second machine learning dataset. The data annotation apparatus can result in an improved data labeling and an enhanced machine learning.

Abstract: Systems and methods for obtaining 3D point-level segmentation of 3D point clouds in accordance with various embodiments of the invention are disclosed. One embodiment includes: at least one processor, and a memory containing a segmentation pipeline application. In addition, the segmentation pipeline application configures the at least one processor to: pre-process a 3D point cloud to group 3D points; provide the groups of 3D points to a 3D neural network to generate initial label predictions for the groups of 3D points; interpolate label predictions for individual 3D points based upon initial label predictions for at least two neighboring groups of 3D points including the group of 3D points to which a given individual 3D point belongs; refine the label predictions using a graph neural network; and output a segmented 3D point cloud.

Abstract: A system and method for determining an object or product represented in an image is disclosed. The system receives a first image, determines a region of interest in the first image, determines a classification score for the region of interest using a convolutional neural network that assigns the region of interest the classification score corresponding to a class, and identifies a first product in the first image based on the classification score.

Abstract: Disclosed is a system and method for generating intermediate views between two received images. To generate the intermediate views, a rectification network rectifies the two images and an encoder network encodes the two rectified images to generate convolutional neural network features. The convolutional neural network features are fed to a decoder network that decodes the features to generate a correspondence between the two rectified images and blending masks to predict the visibility of pixels of the rectified images in the intermediate view images. Using the correspondence between the two rectified images and blending masks, a view morphing network synthesizes intermediate view images depicting an object in the two images in a view between the two images.

Abstract: The disclosure includes a system and method for performing image rectification using a single image and information identified from the single image. An image recognition application receives an input image, identifies a plurality of objects in the input image, estimates rectification parameters for the plurality of objects, identifies a plurality of candidate rectification parameters using a voting procedure on the rectification parameters for the plurality of objects, estimates final rectification parameters based on the plurality of candidate rectification parameters, computes a global transformation matrix using the final rectification parameters, and performs image rectification on the input image using the global transformation matrix.

Abstract: Systems and methods for performing three-dimensional semantic parsing of indoor spaces in accordance with embodiments of the invention are disclosed. In one embodiment, a method includes receiving input data representing a three-dimensional space, determining disjointed spaces within the received data by generating a density histogram on each of a plurality of axes, determining space dividers based on the generated density histogram, and dividing the point cloud data into segments based on the determined space dividers, and determining elements in the disjointed spaces by aligning the disjointed spaces within the point cloud data along similar axes to create aligned versions of the disjointed spaces normalizing the aligned version of the disjointed spaces into the aligned version of the disjointed spaces, determining features in the disjointed spaces, generating at least one detection score, and filtering the at least one detection score to determine a final set of determined elements.

Abstract: A system and method for determining an object or product represented in an image is disclosed. The system receives a first image, determines a region of interest in the first image, determines a classification score for the region of interest using a convolutional neural network that assigns the region of interest the classification score corresponding to a class, and identifies a first product in the first image based on the classification score.

Abstract: Systems and methods for obtaining 3D point-level segmentation of 3D point clouds in accordance with various embodiments of the invention are disclosed. One embodiment includes: at least one processor, and a memory containing a segmentation pipeline application. In addition, the segmentation pipeline application configures the at least one processor to: pre-process a 3D point cloud to group 3D points; provide the groups of 3D points to a 3D neural network to generate initial label predictions for the groups of 3D points; interpolate label predictions for individual 3D points based upon initial label predictions for at least two neighboring groups of 3D points including the group of 3D points to which a given individual 3D point belongs; refine the label predictions using a graph neural network; and output a segmented 3D point cloud.

Abstract: A system and method for determining an object or product represented in an image is disclosed. The system receives a first image, determines a region of interest in the first image, determines a classification score for the region of interest using a convolutional neural network that assigns the region of interest the classification score corresponding to a class, and identifies a first product in the first image based on the classification score.

Abstract: A computer-implemented method for training a convolutional neural network (CNN) is presented. The method includes extracting coordinates of corresponding points in the first and second locations, identifying positive points in the first and second locations, identifying negative points in the first and second locations, training features that correspond to positive points of the first and second locations to move closer to each other, and training features that correspond to negative points in the first and second locations to move away from each other.

Abstract: Velocity controllers in accordance with embodiments of the invention enable velocity estimation for tracked objects. One embodiment includes a tracker controller, including: a processor; and a memory containing: a velocity tracker application; a state space describing relationships between measured locations, calculated locations, and changes in locations, where the calculated locations in the state space correspond to unoccluded points on the surface of the tracked object; wherein the processor is configured by the velocity tracker application to: pre-process the state space to identify a tracked object; estimate a velocity of the tracked object using a location history calculated from the measured locations of the tracked object within the state space and a motion model calculated from the state space; and return the velocity of the tracked object.

Abstract: The disclosure includes a system and method for performing image rectification using a single image and information identified from the single image. An image recognition application receives an input image, identifies a plurality of objects in the input image, estimates rectification parameters for the plurality of objects, identifies a plurality of candidate rectification parameters using a voting procedure on the rectification parameters for the plurality of objects, estimates final rectification parameters based on the plurality of candidate rectification parameters, computes a global transformation matrix using the final rectification parameters, and performs image rectification on the input image using the global transformation matrix.

Abstract: Disclosed is a system and method for generating intermediate views between two received images. To generate the intermediate views, a rectification network rectifies the two images and an encoder network encodes the two rectified images to generate convolutional neural network features. The convolutional neural network features are fed to a decoder network that decodes the features to generate a correspondence between the two rectified images and blending masks to predict the visibility of pixels of the rectified images in the intermediate view images. Using the correspondence between the two rectified images and blending masks, a view morphing network synthesizes intermediate view images depicting an object in the two images in a view between the two images.

Abstract: A computer-implemented method for detecting objects by using subcategory-aware convolutional neural networks (CNNs) is presented. The method includes generating object region proposals from an image by a region proposal network (RPN) which utilizes subcategory information, and classifying and refining the object region proposals by an object detection network (ODN) that simultaneously performs object category classification, subcategory classification, and bounding box regression. The image is an image pyramid used as input to the RPN and the ODN. The RPN and the ODN each include a feature extrapolating layer to detect object categories with scale variations among the objects.

Abstract: A data annotation apparatus for machine learning is provided, which includes a stimulus generation portion, a biometrics reading portion, and a data integration portion. The stimulus generation portion is configured to generate, and present to an agent, at least one stimulus based on a first data from a first machine learning dataset. The biometrics reading portion is configured to measure at least one response of the agent to the at least one stimulus, and to generate biometrics data based on the at least one response. The data integration portion is configured to integrate the biometrics data, data of the at least one stimulus, and data of the first machine learning dataset to thereby obtain a second machine learning dataset. The data annotation apparatus can result in an improved data labeling and an enhanced machine learning.

Abstract: The disclosure includes a system and method for performing image rectification using a single image and information identified from the single image. An image recognition application receives an input image, identifies a plurality of objects in the input image, estimates rectification parameters for the plurality of objects, identifies a plurality of candidate rectification parameters using a voting procedure on the rectification parameters for the plurality of objects, estimates final rectification parameters based on the plurality of candidate rectification parameters, computes a global transformation matrix using the final rectification parameters, and performs image rectification on the input image using the global transformation matrix.

Abstract: Systems and methods for performing three-dimensional semantic parsing of indoor spaces in accordance with embodiments of the invention are disclosed. In one embodiment, a method includes receiving input data representing a three-dimensional space, determining disjointed spaces within the received data by generating a density histogram on each of a plurality of axes, determining space dividers based on the generated density histogram, and dividing the point cloud data into segments based on the determined space dividers, and determining elements in the disjointed spaces by aligning the disjointed spaces within the point cloud data along similar axes to create aligned versions of the disjointed spaces normalizing the aligned version of the disjointed spaces into the aligned version of the disjointed spaces, determining features in the disjointed spaces, generating at least one detection score, and filtering the at least one detection score to determine a final set of determined elements.

Abstract: Velocity controllers in accordance with embodiments of the invention enable velocity estimation for tracked objects. One embodiment includes a tracker controller, including: a processor; and a memory containing: a velocity tracker application; a state space describing relationships between measured locations, calculated locations, and changes in locations, where the calculated locations in the state space correspond to unoccluded points on the surface of the tracked object; wherein the processor is configured by the velocity tracker application to: pre-process the state space to identify a tracked object; estimate a velocity of the tracked object using a location history calculated from the measured locations of the tracked object within the state space and a motion model calculated from the state space; and return the velocity of the tracked object.