G06T11/00

System and method for three-dimensional scanning and for capturing a bidirectional reflectance distribution function

A method for generating a three-dimensional (3D) model of an object includes: capturing images of the object from a plurality of viewpoints, the images including color images; generating a 3D model of the object from the images, the 3D model including a plurality of planar patches; for each patch of the planar patches: mapping image regions of the images to the patch, each image region including at least one color vector; and computing, for each patch, at least one minimal color vector among the color vectors of the image regions mapped to the patch; generating a diffuse component of a bidirectional reflectance distribution function (BRDF) for each patch of planar patches of the 3D model in accordance with the at least one minimal color vector computed for each patch; and outputting the 3D model with the BRDF for each patch.

Context-aware text color recommendation system

Embodiments are disclosed for determining a context-aware text color recommendation for text at a text location on an image. In particular, in one or more embodiments, the disclosed systems and methods comprise obtaining an image and a text location on the image, identifying at least one color theme based on a color harmonic template associated with the image, modifying the at least one color theme based on characteristics of the image, determining accessibility for at least one color in the at least one color theme based on the text location on the image, and determining a color palette recommendation for text at the text location on the image based on the determined accessibility for the at least one color in the at least one color theme.

Multi-state magnetic resonance fingerprinting

The invention provides for a magnetic resonance imaging system (100) for acquiring magnetic resonance data (142) from a subject (118) within a measurement zone (108). The magnetic resonance imaging system (100) comprises: a processor (130) for controlling the magnetic resonance imaging system (100) and a memory (136) storing machine executable instructions (150, 152, 154), pulse sequence commands (140) and a dictionary (144). The pulse sequence commands (140) are configured for controlling the magnetic resonance imaging system (100) to acquire the magnetic resonance data (142) of multiple steady state free precession (SSFP) states per repetition time. The pulse sequence commands (140) are further configured for controlling the magnetic resonance imaging system (100) to acquire the magnetic resonance data (142) of the multiple steady state free precession (SSFP) states according to a magnetic resonance fingerprinting protocol. The dictionary (144) comprises a plurality of tissue parameter sets. Each tissue parameter set is assigned with signal evolution data pre-calculated for multiple SSFP states.

Method for simulating the rendering of a make-up product on a body area
11576478 · 2023-02-14 · ·

A method for simulating a rendering of a makeup product on a body area including the steps of: acquiring an image of the body area without makeup of a subject, determining first color parameters of the pixels of the image corresponding to the body area without makeup, identifying the pixels of the body area without makeup exhibiting highest brightness or red component value, and determining second color parameters of the pixels of the image corresponding to the body area, wherein the second color parameters render a making up of the body area by the makeup product.

Systems and methods for deep learning-based image reconstruction

Methods and systems for deep learning based image reconstruction are disclosed herein. An example method includes receiving a set of imaging projections data, identifying a voxel to reconstruct, receiving a trained regression model, and reconstructing the voxel. The voxel is reconstructed by: projecting the voxel on each imaging projection in the set of imaging projections according to an acquisition geometry, extracting adjacent pixels around each projected voxel, feeding the regression model with the extracted adjacent pixel data to produce a reconstructed value of the voxel, and repeating the reconstruction for each voxel to be reconstructed to produce a reconstructed image.

Color-sensitive virtual markings of objects
11582312 · 2023-02-14 · ·

Disclosed are systems, methods, and non-transitory computer readable media for making virtual colored markings on objects. Instructions may include receiving an indication of an object; receiving from an image sensor an image of a hand of an individual holding a physical marking implement; detecting in the image a color associated with the marking implement; receiving from the image sensor image data indicative of movement of a tip of the marking implement and locations of the tip; determining from the image data when the locations of the tip correspond to locations on the object; and generating, in the detected color, virtual markings on the object at the corresponding locations.

Color-sensitive virtual markings of objects
11582312 · 2023-02-14 · ·

Disclosed are systems, methods, and non-transitory computer readable media for making virtual colored markings on objects. Instructions may include receiving an indication of an object; receiving from an image sensor an image of a hand of an individual holding a physical marking implement; detecting in the image a color associated with the marking implement; receiving from the image sensor image data indicative of movement of a tip of the marking implement and locations of the tip; determining from the image data when the locations of the tip correspond to locations on the object; and generating, in the detected color, virtual markings on the object at the corresponding locations.

Virtual reality training, simulation, and collaboration in a robotic surgical system

A virtual reality system providing a virtual robotic surgical environment, and methods for using the virtual reality system, are described herein. Within the virtual reality system, various user modes enable different kinds of interactions between a user and the virtual robotic surgical environment. For example, one variation of a method for facilitating navigation of a virtual robotic surgical environment includes displaying a first-person perspective view of the virtual robotic surgical environment from a first vantage point, displaying a first window view of the virtual robotic surgical environment from a second vantage point and displaying a second window view of the virtual robotic surgical environment from a third vantage point. Additionally, in response to a user input associating the first and second window views, a trajectory between the second and third vantage points can be generated sequentially linking the first and second window views.

Virtual reality training, simulation, and collaboration in a robotic surgical system

A virtual reality system providing a virtual robotic surgical environment, and methods for using the virtual reality system, are described herein. Within the virtual reality system, various user modes enable different kinds of interactions between a user and the virtual robotic surgical environment. For example, one variation of a method for facilitating navigation of a virtual robotic surgical environment includes displaying a first-person perspective view of the virtual robotic surgical environment from a first vantage point, displaying a first window view of the virtual robotic surgical environment from a second vantage point and displaying a second window view of the virtual robotic surgical environment from a third vantage point. Additionally, in response to a user input associating the first and second window views, a trajectory between the second and third vantage points can be generated sequentially linking the first and second window views.

Automated honeypot creation within a network

Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.