Abstract: Disclosed is a system and associated methods for compressing motion within an animated point cloud. The resulting compressed file encodes different transforms that recreate the motion of different sets of points across different point clouds or frames of the animation in place of the data for the different sets of points from the different point clouds. The compression involves detecting a motion that changes positioning of a set of points between a first point cloud and subsequent point clouds of an uncompressed encoding of two or more frames of an animation. The compression further involves defining a transform that models the motion, and generating a compressed animated point cloud by encoding the data of the first point cloud in the compressed animated point cloud, and by replacing the data for the set of points in the one or more subsequent point clouds with the transform.

Abstract: Systems and methods for generating a virtual display of an item can include capturing images of an item in a first sequence and a second sequence and reordering the first sequence and the second sequence to generate the virtual display. The images can be taken in an automated photography studio. The virtual display of the item can comprise a video file created from the images showing an ordered rotation of the item.

Abstract: An automatic drug compounder system may be provided with various components and capabilities for reconstituting a drug in a vial and providing the reconstituted drug to a receiving container. The system may include a camera for capturing images of the vial and processing circuitry for extracting information such as a lot number and an expiration date from the images. The system may rotate the vial while capturing the images to capture images of the entire vial. The processing circuitry may search a portion of the image for the lot number and expiration date, the portion based on stored information. The stored information may include a location, orientation, font size, or font color for printed information for a particular vial. The stored information may be retrieved based on a bar code read by the system.

Abstract: Disclosed herein is a method of transmitting point cloud data. The method may include acquiring point cloud data, encoding geometry information including positions of points of the point cloud data, generating one or more LODs based on the geometry information and selecting one or more neighbor points of each point to be attribute-encoded based on the one or more LODs, wherein the selected one or more neighbor points of each point are located within a maximum neighbor point distance, encoding attribute information of each point based on the selected one or more neighbor points of each point, and transmitting the encoded geometry information, the encoded attribute information, and signaling information.

Abstract: A system and method for fisheye image processing can be configured to: receive fisheye image data from at least one fisheye lens camera associated with an autonomous vehicle, the fisheye image data representing at least one fisheye image frame; partition the fisheye image frame into a plurality of image portions representing portions of the fisheye image frame; warp each of the plurality of image portions to map an arc of a camera projected view into a line corresponding to a mapped target view, the mapped target view being generally orthogonal to a line between a camera center and a center of the arc of the camera projected view; combine the plurality of warped image portions to form a combined resulting fisheye image data set representing recovered or distortion-reduced fisheye image data corresponding to the fisheye image frame; generate auto-calibration data representing a correspondence between pixels in the at least one fisheye image frame and corresponding pixels in the combined resulting fisheye image

Abstract: The present disclosure describes systems and techniques for creating a super-resolution image (122) of a scene captured by a user device (102). Natural handheld motion (110) introduces, across multiple frames (204, 206, 208) of an image of a scene, sub-pixel offsets that enable the use of super-resolution computations (210) to form color planes (212, 214, 216), which are accumulated (218) and combined (220) to create a super-resolution image (122) of the scene.

Abstract: An information processing apparatus (2000) includes a first analyzing unit (2020), a second analyzing unit (2040), and an estimating unit (2060). The first analyzing unit (2020) calculates a flow of a crowd in a capturing range of a fixed camera (10) using a first surveillance image (12). The second analyzing unit (2040) calculates a distribution of an attribute of objects in a capturing range of a moving camera (20) using a second surveillance image (22). The estimating unit (2060) estimates an attribute distribution for a range that is not included in the capturing range of the moving camera (20).

Abstract: An apparatus includes a capturing unit, a first motion information acquisition unit configured to acquire a first captured image with a first parameter and first motion information, a second motion information acquisition unit configured to acquire second motion information, a setting unit configured to set a second parameter, a third acquisition unit configured to acquire driving information for either the optical system or the capturing unit, an estimation unit configured to estimate estimated information by converting motion blur of the object in the first captured image into motion blur of the object in a second captured image obtained in a case where second capturing is performed with the second parameter, and a notification unit configured to notify the estimated information.

Abstract: According to one embodiment, an image processing device includes storage and a processor. The storage is configured to store a statistical model generated by learning bokeh that occurs in a first image affected by aberration of an optical system and varies non-linearly in accordance with a distance to a subject in the first image. The processor is configured to acquire a second image affected by the aberration of the optical system, perform color correction on the second image to reduce a number of colors expressed in the second image, and input a third image, obtained by performing the color correction on the second image, into the statistical model and acquire first distance information indicating a distance to a subject in the third image.

Abstract: An information processing apparatus (2000) includes a first analyzing unit (2020), a second analyzing unit (2040), and an estimating unit (2060). The first analyzing unit (2020) calculates a flow of a crowd in a capturing range of a fixed camera (10) using a first surveillance image (12). The second analyzing unit (2040) calculates a distribution of an attribute of objects in a capturing range of a moving camera (20) using a second surveillance image (22). The estimating unit (2060) estimates an attribute distribution for a range that is not included in the capturing range of the moving camera (20).

Abstract: A method for automatically reconstituting the architecture, along a reinforcing axis, of the reinforcement of a composite material, includes acquiring images of the reinforcement of the composite material, each image being acquired along a section plane perpendicular to the reinforcing axis; for each image acquired, detecting, using a neural network, barycentre and/or the circumference of each section of the reinforcing thread; for at least one acquired reference image, assigning a tag corresponding to a reinforcing thread, to each detected barycentre or circumference; for each other acquired image, assigning, to each detected barycentre and/or each detected circumference, the tag of the corresponding barycentre in the acquired reference image; reconstituting the architecture of each reinforcing thread from each detected barycentre and/or circumference having the tag of the reinforcing thread and the position on the reinforcing axis associated with the acquired image on which the barycentre and/or the circumf

Abstract: An image recognition method and system based on deep learning are provided. The image recognition system includes a first recognizing engine, at least one second recognizing engine and a processing circuit. The second recognizing engine is activated to recognize an image when the first recognizing engine is recognizing the image. The processing circuit determines whether to interrupt the first recognizing engine recognizing the image according to a result outputted by the second recognizing engine after the second recognizing engine completes recognition of the image.

Abstract: Provided is a robotic device, including: a chassis; a set of wheels; a control system; a battery; one or more sensors; a processor; a tangible, non-transitory, machine readable medium storing instructions that when executed by the processor effectuates operations including: capturing, with the one or more sensors, data of an environment of the robotic device and data indicative of movement of the robotic device; generating or updating, with the processor, a map of the environment based on at least a portion of the captured data; and generating or updating, with the processor, a movement path of the robotic device.

Abstract: An electronic device obtains an image that includes a face. The device performs feature extraction on the image, to obtain facial expression information corresponding to the face and facial feature information corresponding to the facial expression, wherein the facial feature information indicates an extent of the facial expression. The device determines facial emotion information according to the facial expression information. The device also determines facial feature expression information according to a target feature value corresponding to the facial emotion and the facial feature information. This expression recognition techniques disclosed herein can implement multi-task learning and reduce an amount of data required for model training, and can obtain both an emotion recognition result and a local expression recognition result, thereby improving efficiency and real-time performance of expression recognition and improving user experience.

Abstract: Provided is a tangible, non-transitory, machine readable medium storing instructions that when executed by the processor effectuates operations including: capturing visual readings to objects within an environment; capturing readings of wheel rotation; capturing readings of a driving surface; capturing distances to obstacles; determining displacement of the robotic device in two dimensions based on sensor readings of the driving surface; estimating, with the processor, a corrected position of the robotic device to replace a last known position of the robotic device; determining a most feasible element in an ensemble based on the visual readings; and determining a most feasible position of the robotic device as the corrected position based on the most feasible element in the ensemble and the visual readings.

Abstract: According to one embodiment, a defect classification apparatus includes a processor. The processor acquires a first design image which is an image based on design data created by design software and relates to a first inspection target, and acquires a first real image which is captured by imaging the first inspection target produced based on the design data. The processor converts the first design image to a reference image represented by using a second real image captured by imaging a second inspection target without a defect. The processor calculates a reliability of the reference image. The processor detects a defect in the first inspection target by comparing the reference image and the first real image and classifies the defect, based on the reliability.

Abstract: Provided is a method for predicting attribute information, a coder, a decoder, and a storage medium. The coder determines a current Morton code corresponding to a point to be predicted in a point cloud to be coded, determines a target Morton code corresponding to the point to be predicted based on the current Morton code and according to a preset neighbor information table, judges whether a neighbor point of the point to be predicted exists in the point cloud to be coded according to the target Morton code, and performs prediction to obtain a predicted attribute value of the point to be predicted according to attribute reconstruction information of the neighbor point in response to that the neighbor point exists in the point cloud to be coded.

Abstract: A meter readout on a meter has digits including a first digit, a second digit, etc. A sequence of images of the meter is obtained. The images include images of the digits in the meter readout. Automated recognition of the digits in the images result in likelihood arrays indicating the likelihoods for the digit values for the digits imaged in the meter images. Short chains of digit values are identified and spliced together to form a series of single digit, two-digit, three-digits, etc. paths that are built up based on the likelihood arrays. Various criteria are used to discard most of the chains and thereby avoid the combinatorial explosion of possible paths and thereby produce reliable meter readings without consuming considerable computational resources.

Abstract: Explanation jobs may be performed for computer vision tasks. A request to execute an explanation job for a computer vision machine learning model may be received. The execution job may be performed, including extracting different features from the image, determining the respective relative importance values of the different features on inferences generated by the computer vision machine learning model. The result of the explanation job, including the generated heat maps may be provided.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an image representation neural network.