According to an aspect of an embodiment, operations may comprise obtaining a first point cloud that includes a first point. The operations also comprises obtaining a second point cloud that is a copy of the first point cloud and that includes a second point that is a copy of the first point. The operations also comprises moving the second point cloud with respect to the first point cloud according to a first vector. The operations also comprises identifying a closest point of the first point cloud that is closest to the second point of the second point cloud. The operations also comprises determining a second vector between the closest point and the second point. The operations also comprises determining a measure of usefulness of the first point based on the first vector and the second vector. The operations also comprises indicating the measure of usefulness of the first point.

A shelf space allocation management device manages products allocated on shelves in a store by use of an imaging device. The shelf space allocation management device acquires an image including a position assumed to be changed in allocation status of each product on each shelf; it determines whether each product reflected in the image matches one of pre-recorded images, thus executing a product allocation inspection. Herein, the shelf space allocation management device specifies a position at which a person causes any change in the allocation status of each product on each shelf, and therefore it may control the imaging device to capture an image including the position. It is possible to carry out a product allocation inspection for each period determined in advance depending on the type of each product, or it is possible to carry out a product allocation inspection being triggered by a customer purchasing each product.

Versions of the disclosure relate to methods of imaging and detecting germinated seeds on a soilless growth medium.

An urban remote sensing image scene classification method in consideration of spatial relationships is provided and includes following steps of: cutting a remote sensing image into sub-images in an even and non-overlapping manner; performing a visual information coding on each of the sub-images to obtain a feature image Fv; inputting the feature image Fv into a crossing transfer unit to obtain hierarchical spatial characteristics; performing convolution of dimensionality reduction on the hierarchical spatial characteristics to obtain dimensionality-reduced hierarchical spatial characteristics; and performing a softmax model based classification on the dimensionality-reduced hierarchical spatial characteristics to obtain a classification result. The method comprehensively considers the role of two kinds of spatial relationships being regional spatial relationship and long-range spatial relationship in classification, and designs three paths in a crossing transfer unit for relationships fusion, thereby obtaining a better urban remote sensing image scene classification result.

A method, applied to an apparatus for mammographic multi-view mass identification, includes receiving a main image, a first auxiliary image, and a second auxiliary image. The main image and the first auxiliary image are images of a breast of a person, and the second auxiliary image is an image of another breast of the person. The method further includes detecting the nipple location based on the main image and the first auxiliary image; generating a first probability map of the main image based on the main image, the first auxiliary image, and the nipple location; generating a second probability map of the main image based on the main image, the second auxiliary image, and the nipple location; and generating and outputting a fused probability map based on the first probability map and the second probability map.

An image retrieval apparatus includes a processor, and the processor performs a process including: determining an image in which a first characteristic object is included in a subject to be a first image, and determining an image that is captured after the first image and in which a second characteristic object is included in the subject to be a second image, from among a series of captured images; specifying images as an image group, the images being captured during a period after the first image is captured before the second image is captured from among the series of captured images; and extracting a representative image from the image group.

The present disclosure provides a fingerprint matching method and apparatus, an electronic equipment and a readable storage medium. The method includes: extracting a plurality of to-be-matched feature points from the to-be-identified fingerprint image; performing a first matching between the plurality of to-be-matched feature points and a plurality of template feature points in the template fingerprint image, wherein the first matching includes: identifying true feature points in the plurality of to-be-matched feature points, and determining feature point pairs each of which includes a true feature point and a template feature point corresponding to the true feature point in the template fingerprint image as a first matching result; removing at least one falsely matched feature point pair from the first matching result; and performing a second matching between the to-be-identified fingerprint image and the template fingerprint image based on remaining feature point pairs in the first matching result.

In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

Aspects described herein may provide detection of a physical characteristic of a credential, thereby allowing for authentication of the credential. According to some aspects, these and other benefits may be achieved by detecting the physical characteristic with the credential. An image of a credential may be received. An optical characteristic of a secure feature of the credential may be determined. An expected optical characteristic of the secure feature may be determined based on known properties of the secure feature. A determination as to whether the credential is authentic may be based on a comparison of the determined optical characteristic of the secure feature to the expected optical characteristic of the secure feature.