A method of identifying a defect in a wet film comprises conveying said wet film (20), in a wet state, on a conveyor (10), providing a laser projection (1511) onto the wet film, acquiring a series of images, each depicting an area of the wet film, wherein at least a portion of the laser projection is visible, and using at least some of said images to identify said defect. There is also disclosed a method and device for producing a film.

A method of identifying a defect in a wet film comprises conveying said wet film (20), in a wet state, on a conveyor (10), providing a laser projection (1511) onto the wet film, acquiring a series of images, each depicting an area of the wet film, wherein at least a portion of the laser projection is visible, and using at least some of said images to identify said defect. There is also disclosed a method and device for producing a film.

There are provided techniques for analyzing an atom probe tomography data set obtained from a tip-shaped sample. The techniques include defining analysis sub-volumes in the atom probe tomography data set; performing a fast Fourier transform (FFT) on each of the analysis sub-volumes to obtain a signal in a Fourier domain; identifying at least one FFT peak in the signal in the Fourier domain, each FFT peak being indicative of an expected crystal feature in the corresponding analysis sub-volume; continuously and automatically calculating an image compression factor and a radius of the tip-shaped sample, based on identified crystal features, the identified crystal features being obtained from a collection of expected crystal features; and reconstructing a three-dimensional model of the tip-shaped sample. Said reconstructing includes comparing the identified crystal features with calibration data; and dynamically adjusting the image compression factor and the radius of the tip-shaped sample.

There are provided techniques for analyzing an atom probe tomography data set obtained from a tip-shaped sample. The techniques include defining analysis sub-volumes in the atom probe tomography data set; performing a fast Fourier transform (FFT) on each of the analysis sub-volumes to obtain a signal in a Fourier domain; identifying at least one FFT peak in the signal in the Fourier domain, each FFT peak being indicative of an expected crystal feature in the corresponding analysis sub-volume; continuously and automatically calculating an image compression factor and a radius of the tip-shaped sample, based on identified crystal features, the identified crystal features being obtained from a collection of expected crystal features; and reconstructing a three-dimensional model of the tip-shaped sample. Said reconstructing includes comparing the identified crystal features with calibration data; and dynamically adjusting the image compression factor and the radius of the tip-shaped sample.

A structured 3D model of a real-world object is generated from a series of 2D photographs of the object, using photogrammetry, a keypoint detection deep learning network (DLN), and retopology. In addition, object parameters of the object are received. A pressure map of the object is then generated by a pressure estimation DLN based on the structured 3D model and the object parameters. The pressure estimation DLN was trained on structured 3D models, object parameters, and pressure maps of a plurality of objects belonging to a given object category. The pressure map of the real-world object can be used in downstream processes, such as custom manufacturing.

Provided are a body size estimation apparatus, a body size estimation method, and a program that enable the estimation of the body size of a user even when the user has not taken a T-pose in advance. A body size data storage unit (50) stores body size data indicating a body size of a user. A posture data acquisition unit (52) acquires position data indicating positions of a plurality of body parts away from each other of the user. A body size estimation unit (54) estimates a body size of the user based on positions of two or more body parts indicated by the position data. A body size update unit (56) updates, in a case where the estimated body size is larger than the body size indicated by the body size data stored in the body size data storage unit (50), the body size indicated by the body size data to the estimated body size.

A situationally aware surgical system configured for use during a surgical procedure performed on a patient by an operating clinician is disclosed including a surgical instrument configured to generate a signal and a cloud-based analytics subsystem including a memory and a control circuit. The memory is configured to store a plurality of baseline variables. The control circuit is configured to receive the signal, determine a type of surgical procedure being performed, at least in part, on the received signal, determine that a baseline variable of the plurality of baseline variables corresponds to the determined type of surgical procedure, determine a procedural variable of the surgical procedure based, at least in part, on the received signal, compare the determined procedural variable to the corresponding baseline variable, and generate an alert for the operating clinician based, at least in part, on the comparison.

A situationally aware surgical system configured for use during a surgical procedure performed on a patient by an operating clinician is disclosed including a surgical instrument configured to generate a signal and a cloud-based analytics subsystem including a memory and a control circuit. The memory is configured to store a plurality of baseline variables. The control circuit is configured to receive the signal, determine a type of surgical procedure being performed, at least in part, on the received signal, determine that a baseline variable of the plurality of baseline variables corresponds to the determined type of surgical procedure, determine a procedural variable of the surgical procedure based, at least in part, on the received signal, compare the determined procedural variable to the corresponding baseline variable, and generate an alert for the operating clinician based, at least in part, on the comparison.

The present application relates to an object prediction method and apparatus, an electronic device, and a storage medium. The method is applied to a neural network and includes: performing feature extraction processing on a to-be-predicted object to obtain feature information of the to-be-predicted object; determining multiple intermediate prediction results for the to-be-predicted object according to the feature information; performing fusion processing on the multiple intermediate prediction results to obtain fusion information; and determining multiple target prediction results for the to-be-predicted object according to the fusion information. According to embodiments of the present application, feature information of a to-be-predicted object may be extracted; multiple intermediate prediction results for the to-be-predicted object are determined according to the feature information; fusion processing is performed on the multiple intermediate prediction results to obtain fusion information; and multiple target prediction results for the to-be-predicted object are determined according to the fusion information. The method facilitates improving the accuracy of multiple target prediction results.

The present application relates to an object prediction method and apparatus, an electronic device, and a storage medium. The method is applied to a neural network and includes: performing feature extraction processing on a to-be-predicted object to obtain feature information of the to-be-predicted object; determining multiple intermediate prediction results for the to-be-predicted object according to the feature information; performing fusion processing on the multiple intermediate prediction results to obtain fusion information; and determining multiple target prediction results for the to-be-predicted object according to the fusion information. According to embodiments of the present application, feature information of a to-be-predicted object may be extracted; multiple intermediate prediction results for the to-be-predicted object are determined according to the feature information; fusion processing is performed on the multiple intermediate prediction results to obtain fusion information; and multiple target prediction results for the to-be-predicted object are determined according to the fusion information. The method facilitates improving the accuracy of multiple target prediction results.