An edge detection system is disclosed. The edge detection system includes an imaging device configured for imaging a pattern structure to form a first image, wherein the pattern structure includes a predetermined feature, and the imaging device images the pattern structure to generate measured linescan information that includes image noise. The edge detection system includes a processor, coupled to the imaging device, configured to receive the measured linescan information including image noise from the imaging device, wherein the processor is configured to: apply the measured linescan information to an inverse linescan model that relates the measured linescan information to feature geometry information, determine, from the inverse linescan model, feature geometry information that describes feature edge positions of the predetermined feature corresponding to the measured linescan information, determine from the feature geometry information at least one metric that describes a property of the edge detection system.

An edge detection system is disclosed. The edge detection system includes an imaging device configured for imaging a pattern structure to form a first image, wherein the pattern structure includes a predetermined feature, and the imaging device images the pattern structure to generate measured linescan information that includes image noise. The edge detection system includes a processor, coupled to the imaging device, configured to receive the measured linescan information including image noise from the imaging device, wherein the processor is configured to: apply the measured linescan information to an inverse linescan model that relates the measured linescan information to feature geometry information, determine, from the inverse linescan model, feature geometry information that describes feature edge positions of the predetermined feature corresponding to the measured linescan information, determine from the feature geometry information at least one metric that describes a property of the edge detection system.

Provided is a novel cell sheet evaluation method. A cell sheet evaluation method includes: a step of analyzing, based on an observed image of a cell sheet, a characteristic indicating shape uniformity of cells constituting the cell sheet; and a step of evaluating a binding state between cells in the cell sheet based on the analysis result.

A system is disclosed. The system at least one physical memory device to store image texture analysis logic and one or more processors coupled with the at least one physical memory device, to execute the image texture analysis logic to receive first image data associated with a first nozzle group and second image data associated with a second nozzle group and analyze the first and second nozzle group image data to determine whether a difference between a texture of the first image data and a texture of the second image data exceeds a predetermined threshold, wherein the first nozzle group and second print head are neighboring nozzle groups.

A road surface detecting apparatus comprises a camera sensor 11 for taking images of a region in front of a vehicle, and an electronic control unit (ECU) configured to: calculate, based on a taken image, parallax information including a parallax of each pixel composing the taken image; extract a pixel corresponding to a road surface from the taken image; acquire, based on information of the extracted pixel, information-indicating-a-road-surface; calculate, based on the information-indicating-a-road-surface, a road surface gradient formula by approximating a gradient of the road surface by a quadratic function; determine whether or not an absolute value of a coefficient of a quadratic term of this formula is greater than a coefficient threshold; and invalidate the information-indicating-a-road-surface when the absolute value is determined to be greater than the threshold.

A road surface detecting apparatus comprises a camera sensor 11 for taking images of a region in front of a vehicle, and an electronic control unit (ECU) configured to: calculate, based on a taken image, parallax information including a parallax of each pixel composing the taken image; extract a pixel corresponding to a road surface from the taken image; acquire, based on information of the extracted pixel, information-indicating-a-road-surface; calculate, based on the information-indicating-a-road-surface, a road surface gradient formula by approximating a gradient of the road surface by a quadratic function; determine whether or not an absolute value of a coefficient of a quadratic term of this formula is greater than a coefficient threshold; and invalidate the information-indicating-a-road-surface when the absolute value is determined to be greater than the threshold.

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.

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.

A method is disclosed. The 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, 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, determining, at least in part using the inverse linescan model, feature edge positions of the identified feature, and analyzing the feature edge positions to detect the presence or absence of defects in the pattern structure.

A method is disclosed. The 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, 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, determining, at least in part using the inverse linescan model, feature edge positions of the identified feature, and analyzing the feature edge positions to detect the presence or absence of defects in the pattern structure.