A method of barrier detection in an imaging controller includes: obtaining an image of a support structure configured to support a plurality of items on a support surface extending between a shelf edge and a shelf back; extracting frequency components representing pixels of the image; based on the extracted frequency components, identifying a barrier region of the image, the barrier region containing a barrier adjacent to the shelf edge; and detecting at least one empty sub-region within the barrier region, wherein the empty sub-region is free of items between the barrier and the shelf back.

A fingerprint verification method and apparatus is disclosed. The fingerprint verification method may include obtaining an input fingerprint image, determining a matching region between the input fingerprint image and a registered fingerprint image, determining a similarity corresponding to the matching region, representing a determined indication of similarities between the input fingerprint image and the registered fingerprint image, relating the determined similarity to the matching region as represented in a matching region-based similarity, determining a result of a verification of the input fingerprint image based on the matching region-based similarity, and indicating the result of the verification.

A method and device for identifying a state, electronic device and computer-readable storage medium are provided. The method includes: acquiring a to-be-detected image for a specific scene and determining a region of interest in the to-be-detected image; the region of interest being a region obtained by subtracting an occlusion range of the storage container in a closed state from an occlusion range in an open state; determining a positional relation between the region of interest and specific object regions where the specific objects are positioned in the to-be-detected image; determining at least one value based on values of pixels in the region of interest, when the positional relation represents that there is no specific object region partially overlapping the region of interest; and determining whether the storage container is in an open state or closed state based on the at least one value and a preset value range.

In an exemplary embodiment, a tomography device comprises a scanner that obtains image slices. The device additionally comprises at least one processor configured to: perform a Hermetic Transform on the image slices to obtain hermetically transformed data using; filter and perform an Inverse Hermetic Transform on the Hermetic Transform data to obtain filtered inverse Hermetic Transform data; and perform back projection and angle integration on the filtered inverse Hermetic Transform data.

Various embodiments are described that relate to classification of an unknown object. A time series signal associated with an unknown object can be obtained from a sensor. The time series signal can be subjected to a transform set, such as a Fourier transform and a discrete cosine transform, to produce a transform outcome. Based, at least in part, on the transform outcome, the unknown object can be classified.

A method and device for identifying a state, electronic device and computer-readable storage medium are provided. The method includes: acquiring a to-be-detected image for a specific scene and determining a region of interest in the to-be-detected image; the region of interest being a region obtained by subtracting an occlusion range of the storage container in a closed state from an occlusion range in an open state; determining a positional relation between the region of interest and specific object regions where the specific objects are positioned in the to-be-detected image; determining at least one value based on values of pixels in the region of interest, when the positional relation represents that there is no specific object region partially overlapping the region of interest; and determining whether the storage container is in an open state or closed state based on the at least one value and a preset value range.

Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.

The present disclosure discloses a method and apparatus for detecting an obstacle, and relates to the technical field of intelligent transportation. A specific implementation plan is: acquiring a current image acquired by a camera; inputting the current image into a pre-trained detection model to obtain a position of a detection frame of an obstacle and determine a first pixel coordinate of a grounding point in the current image; determining an offset between the current image and a template image; converting the first pixel coordinate into a world coordinate of the grounding point based on the offset; and outputting the world coordinate of the grounding point as a position of the obstacle in a world coordinate system. This embodiment solves the problem of camera jitter from an image perspective, greatly improves the robustness of the roadside perception system, and saves computing resources.

Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.

An image matching apparatus according to the present invention includes: a common region specification unit configured to specify a common region between a first image and a second image; a date replacement unit configured to generate a first replaced image in which a brightness value of the common region of the first image is replaced based on a pixel in the first image, and a second replaced image in which a brightness value of the common region of the second image is replaced based on a pixel in the second image; and a matching unit configured to perform matching between the first image and the second image based on frequency characteristics of the first replaced image and the second replaced image.