A method for image processing includes obtaining a mask of a stroke from an image; determining a plurality of cross edges for the stroke based on the mask; generating a histogram comprising a plurality of widths of the cross edges and a plurality of frequencies of the plurality of widths from the cross edges; estimating a lower bound of a width range for the stroke based on a mode width of the plurality of widths, a first subset of the plurality of widths below the mode width, and a first plurality of weights assigned to the first subset of the plurality of widths; and estimating an upper bound of the width range for the stroke based on the mode width, a second subset of the plurality of widths above the mode width, and a second plurality of weights assigned to the second subset of the widths.

This method includes: generating a backscattered-electron image of a multilayered structure (400) including a plurality of patterns formed in a plurality of layers by a scanning electron microscope (50); classifying a plurality of regions of a virtual multilayered structure (300) including a CAD pattern created from design data of the plurality of patterns into a plurality of groups according to CAD pattern arrays in a depth direction of the virtual multilayered structure (1300); performing a matching between at least one of the plurality of patterns on the backscattered-electron image and a corresponding CAD pattern; calculating a brightness index value of a region on the backscattered-electron image corresponding to a region belonging to each group; and determining that there is a pattern defect in the region on the backscattered-electron image when the brightness index value is out of a standard range.

Provided is a second-part-position estimating unit (120) that calculates a plurality of second-part estimated positions by estimating a position of a second part in a target image from a plurality of first-part position candidates, each of which is a candidate of a position of a first part in the target image; and a first-part-position-candidate confidence-level calculating unit (130) that calculates a plurality of first-part-position candidate confidence levels indicating confidence levels of the first-part position candidates so that the longer the distance between a second-part position, which is the position of the second part in the target image, and one of the second-part estimated positions, the lower the confidence level of the first-part position candidate used for the estimation of the one of the second-part estimated position out of the first-part position candidates.

An apparatus configured for head-worn by a user, includes: a screen configured to present graphics for the user; a camera system configured to view an environment in which the user is located; and a processing unit coupled to the camera system, the processing unit configured to: obtain a first image with a first resolution, the first image having a first corner, determine a second image with a second resolution, the second image having a second corner that corresponds with the first corner in the first image, wherein the second image is based on the first image, the second resolution being less than the first resolution, detect the second corner in the second image, determine a position of the second corner in the second image, and determine a position of the first corner in the first image based at least in part on the determined position of the second corner in the second image.

An event detection apparatus includes an input unit configured to input a plurality of time-sequential images, a first extraction unit configured to extract sets of first image samples according to respective different sample scales from a first time range of the plurality of time-sequential images based on a first scale parameter, a second extraction unit configured to extract sets of second image samples according to respective different sample scales from a second time range of the plurality of time-sequential images based on a second scale parameter, a dissimilarity calculation unit configured to calculate a dissimilarity between the first and second image samples based on the sets of the first and second image samples, and a detection unit configured to detect an event from the plurality of time-sequential images based on the dissimilarity.

The present disclosure applies to the field of biometric identification technologies and provides a method and an apparatus for fingerprint identification. The method includes: extracting a minutia of the input fingerprint image by using a statistical method; performing fingerprint matching according to the extracted minutia to obtain a fingerprint identification result. According to the method provides in embodiments of the present disclosure, the direction of the minutia is calculated by using statistical information, a descriptor with statistical significance is added for the minutia, and during the matching process, calculation of the similarity of the minutia by using the descriptor and region matching by using information of the direction field and the gradient field of the overlapping region are added, therefore, instability and weak specificity of expression of fingerprint characteristics in a conventional algorithm are avoided, and accuracy of the fingerprint identification is improved.

A computer-implemented method of identifying a payment card design includes partitioning an image of a detected payment card into a plurality of blocks, the image comprising predetermined portions irreversibly masked; and generating an individual numerical representation of each respective one of the plurality of blocks, thereby generating a collective numerical representation of the design of the detected payment card. The method also includes selecting, from a database storing a plurality of payment card designs, one or more payment card designs based on the collective numerical representation of the design of the detected payment card. The method further includes generating a similarity score between the design of the detected payment card and each of the one or more selected payment card designs; and associating the design of the detected payment card with one of the one or more selected payment card designs based on the similarity score.

A method and system for controlling a home security system. A processor may be trained to recognize an image standard for a scene, wherein the training comprises creating a profile of the image standard. Operational imaging of the scene may be performed to create an operational image. A profile of the operational image may be created. Profiles of the image standard and the operational image may be compared. A state of a security system may be changed as a result of a comparison of the profiles of the image standard and the operational image.

Described are systems and methods for generating high dynamic range (“HDR”) images based on image data obtained from different image sensors for use in detecting events and monitoring inventory within a materials handling facility. The different image sensors may be aligned and calibrated and the image data from the sensors may be generated at approximately the same time but at different exposures. The image data may then be preprocessed, matched, aligned, and blended to produce an HDR image that does not include overexposed regions or underexposed regions.

A method of identifying a historical alert that is similar to an alert associated with a detected deviation from an operational state of a device includes receiving feature data including time series data for multiple sensor devices associated with the device and receiving an alert indicator for the alert. The method includes processing a portion of the feature data that is within a temporal window associated with the alert indicator to generate feature importance data for the alert. The feature importance data includes values indicating relative importance of each of the sensor devices to the alert. The method also includes identifying one or more historical alerts that are most similar, based on the feature importance data and stored feature importance data, to the alert.