A system identifies strong features in conjugate digital images and correlates the conjugate digital images for an identification of candidate tie points using only portions of the conjugate digital images that include the strong features.

A device for verifying a subject includes: a device body comprising a processor and a biometric system; wherein the biometric system comprises a first image capture device and a second image capture device, in which the first image capture device is configured to define a spatial region and the second image capture device is configured to capture an image of a subject within said spatial region, and the processor is configured to conduct an identification process on the captured image of the subject within the spatial region.

The method includes: receiving an image to be detected, wherein the image to be detected includes an object to be detected; detecting the image to be detected based on a trained multi-task network model to obtain a region where the object wears a mask; comparing an area of the region where the object wears the mask to a predetermined threshold to determine a first detection result; in response to the object being determined as wearing the mask, determining comparison information of a mask area to be compared, and determining a target region in the image to be detected corresponding to the comparison information based on a face feature point template; and obtaining a second detection result based on an overlapping degree between the target region and the region where the object wears the mask.

An imaging system includes first camera having negative distortion; second camera, second field of view of second camera being wider than first field of view of first camera, wherein first field of view fully overlaps with portion of second field of view, second camera having negative distortion at said portion and positive distortion at remaining portion; and processor(s) configured to: capture first image and second image; determine overlapping image segment and non-overlapping image segment of second image; and generate output image from first image and second image, wherein: inner image segment of output image is generated from at least one of: first image, overlapping image segment, and peripheral image segment of output image is generated from non-overlapping image segment.

Embodiments of the present disclosure provide a method, a system and a non-transitory computer readable medium for imaging. The method may include: obtaining historical configuration information; adjusting the historical configuration information to generate target configuration information matching a current scanning condition; and performing, using a first device, an imaging operation on the detection object based on the target configuration information, wherein an account, a current scanning device, an operator, or a detection object under the current scanning condition is different from an account, the historical scanning device, an operator, or a detection object under the historical scanning condition, respectively.

The feature extraction device according to one aspect of the present disclosure comprises: a reliability determination unit that determines a degree of reliability with respect to a second region, which is a region that has been extracted as a foreground region of an image and is within a first region that has been extracted from the image as a partial region containing a recognition subject, said degree of reliability indicating the likelihood of being the recognition subject; a feature determination unit that, on the basis of the degree of reliability, uses a first feature which is a feature extracted from the first region and a second feature which is a feature extracted from the second region to determine a feature of the recognition subject; and an output unit that outputs information indicating the determined feature of the recognition subject.

A learning data generation device of an embodiment includes a detection unit that detects a changed region in a space between a first map and a second map based on first map data which indicate the first map and second map data which indicate the second map which indicates a substantially same space as the first map and represents the space at a different time point from the first map and a changed region image acquisition unit that acquires images which correspond to the changed region detected by the detection unit from a first image in which the space represented by the first map is captured and a second image in which the space represented by the second map is captured.

A method includes acquiring information on an in-image coordinate point of a region of interest contained in each of a plurality of images respectively photographed by a plurality of image modules; specifying, with reference to information on a position where at least one of the plurality of image modules is installed and information on an in-image coordinate point of a target region of interest contained in an image photographed by the at least one image module, a candidate figure containing a position where the target region of interest is located in a reference space; and specifying the position where the target region of interest is located in the reference space, with reference to a positional relationship between a first candidate figure of the target region of interest corresponding to a first image module and a second candidate figure of the target region of interest corresponding to a second image module.

The present disclosure relates to a target tracking method, device, system, and a non-transitory computer-readable storage medium. The method includes: determining an area where a current target is located by performing target detection on a current frame image; extracting a current position information of the area where the current target is located; predicting a position information of an area where each historical target is located at a corresponding moment of the current frame image based on historical position information of an area where each historical target is located in one or more historical frame images; determining a historical target that is the same target as the current target based on a position difference between the current position information and the predicted position information, and tracking the current target.

This disclosure relates generally to a system and method to identify various products on a plurality of images of various shelves of a retail store to facilitate compliance with respect to planograms. Planogram is a visual plan, which designates the placement of products on shelves and merchandising display fixtures of a retail store. Planograms are used to create consistency between store locations, to provide proper shelf space allocation, to improve visual merchandising appeal, and to create product-pairing suggestions. There are a few assumptions considering one instance per product class is available beforehand and the physical dimension of each product template is available in some suitable unit of length. In case of absence of physical dimension of the products, a context information of the retail store will be used. The context information is that the products of similar shapes or classes are arranged together in the shelves for consumers' convenience.