Patent classifications
G06T7/49
SYSTEM AND METHOD FOR AUTOMATING CONSTRUCTION AND INSTALLATION OF SURFACES IN CONSTRUCTION
A system and method for automating construction that includes collecting a multi-dimensional point cloud measurement of at least one construction structure that includes at least one surface; generating a construction plan from the multi-dimensional point cloud measurement, wherein the construction plan defines an assembly arrangement of a set of parts; from the construction plan, automatically generating a cut list for a subset of parts; communicating the cut list to a cutting device; and at the cutting device, cutting a set of materials according to the cut list.
System and method for generating and analyzing roughness measurements and their use for process monitoring and control
In one embodiment, a method includes receiving measured linescan information describing a pattern structure of a feature, applying the received measured linescan information to an inverse linescan model that relates measured linescan information to feature geometry information, and identifying, based at least in part on the applying the received measured linescan model to the inverse linescan model, feature geometry information that describes a feature that would produce a linescan corresponding to the received measured linescan information. The method also includes determining, at least in part using the inverse linescan model, feature edge positions of the identified feature, analyzing the feature edge positions to determine errors in the manufacture of the pattern structure, and controlling a lithography tool based on the analysis of the feature edge positions.
System and method for generating and analyzing roughness measurements and their use for process monitoring and control
In one embodiment, a method includes receiving measured linescan information describing a pattern structure of a feature, applying the received measured linescan information to an inverse linescan model that relates measured linescan information to feature geometry information, and identifying, based at least in part on the applying the received measured linescan model to the inverse linescan model, feature geometry information that describes a feature that would produce a linescan corresponding to the received measured linescan information. The method also includes determining, at least in part using the inverse linescan model, feature edge positions of the identified feature, analyzing the feature edge positions to determine errors in the manufacture of the pattern structure, and controlling a lithography tool based on the analysis of the feature edge positions.
READING SYSTEM, READING DEVICE, AND STORAGE MEDIUM
According to one embodiment, a reading system includes an extractor, a determiner, and a reader. The extractor extracts a candidate image from an input image. The candidate image is of a portion of the input image in which a segment display is imaged. The determiner calculates an angle with respect to a reference line of each of a plurality of straight lines detected from the candidate image, and determines whether or not the candidate image is an image of a segment display based on a distribution indicating a relationship between the angle and a number of the straight lines. The reader reads a numerical value displayed in a segment display from the candidate image determined to be an image of a segment display.
READING SYSTEM, READING DEVICE, AND STORAGE MEDIUM
According to one embodiment, a reading system includes an extractor, a determiner, and a reader. The extractor extracts a candidate image from an input image. The candidate image is of a portion of the input image in which a segment display is imaged. The determiner calculates an angle with respect to a reference line of each of a plurality of straight lines detected from the candidate image, and determines whether or not the candidate image is an image of a segment display based on a distribution indicating a relationship between the angle and a number of the straight lines. The reader reads a numerical value displayed in a segment display from the candidate image determined to be an image of a segment display.
GENERATING PROCEDURAL TEXTURES WITH THE AID OF PARTICLES
System and Method for generating textures on an object on the basis of the particles emitted by a particles engine, including: an access to data of a particles emitter, of particles emitted, of target object, of traces, and of graphical effects; an animation simulation module provided so as to perform a simulation of emission and of displacement for each of the particles provided; a tracer module provided for generating a trace on the surface of a target object corresponding to the displacement of a particle along said surface after an impact of the particle against the target object with the aid of the traces data and of the target object data; and a physical parameters integrator module provided for generating a new set of textures for said object taking into account the date of the object, the data of each new or modified trace, and the data of the corresponding graphical effects.
GENERATING PROCEDURAL TEXTURES WITH THE AID OF PARTICLES
System and Method for generating textures on an object on the basis of the particles emitted by a particles engine, including: an access to data of a particles emitter, of particles emitted, of target object, of traces, and of graphical effects; an animation simulation module provided so as to perform a simulation of emission and of displacement for each of the particles provided; a tracer module provided for generating a trace on the surface of a target object corresponding to the displacement of a particle along said surface after an impact of the particle against the target object with the aid of the traces data and of the target object data; and a physical parameters integrator module provided for generating a new set of textures for said object taking into account the date of the object, the data of each new or modified trace, and the data of the corresponding graphical effects.
Reading system, reading device, and storage medium
According to one embodiment, a reading system includes an extractor, a determiner, and a reader. The extractor extracts a candidate image from an input image. The candidate image is of a portion of the input image in which a segment display is imaged. The determiner calculates an angle with respect to a reference line of each of a plurality of straight lines detected from the candidate image, and determines whether or not the candidate image is an image of a segment display based on a distribution indicating a relationship between the angle and a number of the straight lines. The reader reads a numerical value displayed in a segment display from the candidate image determined to be an image of a segment display.
Reading system, reading device, and storage medium
According to one embodiment, a reading system includes an extractor, a determiner, and a reader. The extractor extracts a candidate image from an input image. The candidate image is of a portion of the input image in which a segment display is imaged. The determiner calculates an angle with respect to a reference line of each of a plurality of straight lines detected from the candidate image, and determines whether or not the candidate image is an image of a segment display based on a distribution indicating a relationship between the angle and a number of the straight lines. The reader reads a numerical value displayed in a segment display from the candidate image determined to be an image of a segment display.
Systems and methods for tumor characterization
Systems and methods for characterizing a region of interest (ROI) in a medical image are provided. An exemplary system may include a memory storing instructions and at least one processor communicatively coupled to the memory to execute the instructions which, when executed by the processor, may cause the processor to perform operations. The operations may include detecting one or more candidate ROIs from the medical image using a three-dimensional (3D) machine learning network. The operations may also include determining a key slice for each candidate ROI. The operations may further include selecting a primary ROI from the one or more candidate ROIs based on the respective key slices. In addition, the operations may include classifying the primary ROI into one of a plurality of categories using a texture-based classifier based on the key slice corresponding to the primary ROI.