A computer vision processor of a camera extracts attributes of persons or vehicles from hyperzooms generated from image frames. The hyperzooms represent traffic patterns. The extracting is performed using a feature extractor of an on-camera convolutional neural network (CNN) including an inverted residual structure. The attributes include at least colors of clothing of the persons or colors of the vehicles. Mobile semantic segmentation models of the CNN are generated using the hyperzooms and the attributes. Attribute analytics are generated by executing the mobile semantic segmentation models while obviating network usage by the camera. The attribute analytics are stored in a key-value database located on a memory card of the camera. A query is received from the server instance specifying one or more of the attributes. The attribute analytics are filtered using the one or more of the attributes to obtain a portion of the traffic patterns.

A computer vision processor of a camera extracts attributes of persons or vehicles from hyperzooms generated from image frames. The hyperzooms represent traffic patterns. The extracting is performed using a feature extractor of an on-camera convolutional neural network (CNN) including an inverted residual structure. The attributes include at least colors of clothing of the persons or colors of the vehicles. Mobile semantic segmentation models of the CNN are generated using the hyperzooms and the attributes. Attribute analytics are generated by executing the mobile semantic segmentation models while obviating network usage by the camera. The attribute analytics are stored in a key-value database located on a memory card of the camera. A query is received from the server instance specifying one or more of the attributes. The attribute analytics are filtered using the one or more of the attributes to obtain a portion of the traffic patterns.

The present disclosure provides an image acquisition method, image acquisition device, electronic device, and computer-readable storage medium. The method includes: acquiring an image retrieval text input by a user and a screen display status of an image display device; determining retrieval intention information and a retrieval keyword according to the image retrieval text, wherein the retrieval intention information comprises information reflecting the user's retrieval intention, and the retrieval keyword comprises a keyword used to retrieve images; acquiring at least one candidate image according to the retrieval intention information and the retrieval keyword; and selecting a target image from the at least one candidate image according to the screen display status.

The present disclosure provides an image acquisition method, image acquisition device, electronic device, and computer-readable storage medium. The method includes: acquiring an image retrieval text input by a user and a screen display status of an image display device; determining retrieval intention information and a retrieval keyword according to the image retrieval text, wherein the retrieval intention information comprises information reflecting the user's retrieval intention, and the retrieval keyword comprises a keyword used to retrieve images; acquiring at least one candidate image according to the retrieval intention information and the retrieval keyword; and selecting a target image from the at least one candidate image according to the screen display status.

Large scale instance recognition is provided that can take advantage of channel-wise pooling. A received query image is processed to extract a set of features that can be used to generate a set of region proposals. The proposed regions of image data are processed using a trained classifier to classify the regions as object or non-object regions. Extracted features for the object regions are processed using feature correlation against extracted features for a set of object images, each representing a classified object. Matching tensors generated from the comparison are processed using a spatial verification network to determine match scores for the various object images with respect to a specific object region. The match scores are used to determine which objects, or types of objects, are represented in the query image. Information or content associated with the matching objects can be provided as part of a response.

Provided are a mediating apparatus and a mediating method, and a computer-readable recording medium thereof. The mediating method includes: receiving a plurality of images from a first user; generating at least one new image by referring to the plurality of received images; extracting a feature of a face included in the at least one generated new image; searching for a second user corresponding to the feature that has been extracted; and providing the first user with information about the second user.

Provided are a mediating apparatus and a mediating method, and a computer-readable recording medium thereof. The mediating method includes: receiving a plurality of images from a first user; generating at least one new image by referring to the plurality of received images; extracting a feature of a face included in the at least one generated new image; searching for a second user corresponding to the feature that has been extracted; and providing the first user with information about the second user.

Disclosed herein are methods and systems of metadata management for reviewing data from microscopy experimental sessions. Image data from an experimental session is stored in an archive at one or more filepath locations, either locally or on a network. Metadata associated with the image data is stored in a database with a reference to the filepath where the raw image is stored, such that the metadata is associated in the database with the image data. A user can perform post-experimental filtering, sorting, and searching of the underlying image data using the metadata, which allows the image data to be analyzed without duplication of the image data and without manual review of each individual image. The filtered data is presented in an interactive timeline format.

Disclosed herein are methods and systems of metadata management for reviewing data from microscopy experimental sessions. Image data from an experimental session is stored in an archive at one or more filepath locations, either locally or on a network. Metadata associated with the image data is stored in a database with a reference to the filepath where the raw image is stored, such that the metadata is associated in the database with the image data. A user can perform post-experimental filtering, sorting, and searching of the underlying image data using the metadata, which allows the image data to be analyzed without duplication of the image data and without manual review of each individual image. The filtered data is presented in an interactive timeline format.

A method and apparatus for generating an unordered list, a method for managing images and a terminal device are disclosed. The method for generating the unordered list includes: randomly acquiring a first element from an ordered list and inserting the first element into the unordered list; cycling the execution of the following steps in a case where a number of current elements in the unordered list is smaller than a sum of elements in the ordered list: determining whether a position at which the first element is located is an edge position in the ordered list and randomly acquiring a second element from the ordered list based on a determining result, and randomly acquiring a target position from the unordered list and inserting the second element into the target position in the unordered list; and ending the cyclic execution in a case where the number of the elements in the unordered list is equal to the sum of the elements in the ordered list.