Apparatuses and methods are disclosed for assessing the texture of skin using images thereof. In exemplary embodiments, a texture map of an area of skin is generated from a combination of a parallel-polarized image and a cross-polarized image of the area of skin. The texture map is then flattened to remove the underlying curvature of the skin. A texture roughness metric is then generated based on the flattened texture map. An image of the texture map and the metric can be displayed to provide visual and alphanumeric representations of the texture of skin, thereby facilitating the comparison of baseline and follow-up images of the skin, such as those taken before and after treatment.

Apparatuses and methods are disclosed for assessing the texture of skin using images thereof. In exemplary embodiments, a texture map of an area of skin is generated from a combination of a parallel-polarized image and a cross-polarized image of the area of skin. The texture map is then flattened to remove the underlying curvature of the skin. A texture roughness metric is then generated based on the flattened texture map. An image of the texture map and the metric can be displayed to provide visual and alphanumeric representations of the texture of skin, thereby facilitating the comparison of baseline and follow-up images of the skin, such as those taken before and after treatment.

A CMOS image sensor for a code reader in an optical code recognition system incorporates a digital processing circuit that applies a calculation process to the capture image data as said data acquired by the sequential readout circuit of the sensor, in order to calculate a macro-image from the capture image data, which corresponds to location information of code(s) in the capture image, and transmit this macro-image in the image frame following the capture image data, in the footer of the frame.

Disclosed are systems, devices and methods for quantifying unique features of an object such as an artistic work to identify and authenticate the object and specific characteristics thereof using multi-spectral diagnostic characterization techniques and analytical algorithms. In some aspects, a method for creating an identification for an object includes acquiring image data of an object in two or more electromagnetic spectrums along a coordinated array of sample regions of the object; analyzing the acquired image data to produce a quantitative data set including specific characteristics of the object associated with the two or more electromagnetic spectrums for each sample region; generating a digital identification associated with a unique data fingerprint, based on the specific characteristics, in which the digital identification solely corresponds to the object; and storing the generated digital identification.

As a technology for an observation device and an inspection device, a technology capable of reducing a work effort related to generation of a recipe including alignment information is provided. An observation device 1 includes an observation unit 103 that obtains an image for observing a sample 101 on a stage 102. A computer system 2 of the observation device 1 acquires the image from the observation unit 103, specifies a period of a pattern-formed unit region repeatedly formed on a surface of the sample 101 from the image, and generates a recipe including observation or inspection alignment positions of the sample 101 using the specified period.

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.

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.

A method for detecting a screen is provided, which may improve detection accuracy of defective sub-pixels in the display screen. The method includes: obtaining an image of a screen to be detected; performing Gabor filtering on the image of the screen to be detected to obtain a plurality of Gabor filtered images; performing image fusion on the plurality of Gabor filtered images to obtain a fused image; segmenting the fused image by using different gray thresholds to obtain segmented images; and performing defect detection according to the segmented images to determine whether there is a defective sub-pixel in the screen to be detected. A value range of different gray thresholds is within a gray value range of the fused image.