Systems and methods are provided for identifying and recommending electronic content to consumers. In accordance with an implementation, one or more elements of electronic content are identified based on video graph data. In an exemplary method, information associated with a first element of video content is received, and corresponding video graph data is obtained. One or more second elements of video content that are similar to the first element of video content are identified based on the obtained video graph data. A subset the first and second elements of video content is subsequently identified for delivery to the user.

An electronic apparatus and a method for controlling the same are disclosed. The method for controlling an electronic apparatus includes acquiring multimedia content including a plurality of image frames, acquiring information related to the multimedia content, selecting at least one image frame including an object related to the acquired information among objects included in the plurality of image frames, generating description information for the at least one selected image frame based on the acquired information, and acquiring description information for the multimedia content based on the generated description information. Thus, the electronic apparatus may generate description information for more elaborate scene analysis regarding multimedia content.

The invention relates to a method for processing large multiplexed image data of a biological sample, the method comprising the steps of, recording a plurality of images of a biological sample, wherein the plurality of images comprises images having a different entity of the biological sample targeted with a predefined stain, determining spatially corresponding image pixels in the plurality of registered images, associating the spatially corresponding image pixels to a pixel profile, wherein each pixel profile comprises the pixel values of the spatially corresponding pixels and wherein the pixel profile is associated with the respective image coordinate of the spatially corresponding pixels, pooling the pixel profiles by means of a clustering method configured to determine pixel profiles with similar values, and thereby generating a plurality of clusters, each comprising pixel profiles with similar pixel values, for each cluster assigning a cluster value to the image coordinate of the pixel profiles comprised by said cluster and thereby generating a cluster image with cluster pixels.

An electronic device and a method for controlling thereof are provided. A method for controlling an electronic device according to the disclosure includes obtaining a plurality of images for performing clustering, obtaining a plurality of target areas corresponding to each of the plurality of images, obtaining a plurality of feature vectors corresponding to the plurality of target areas, obtaining a plurality of central nodes corresponding to the plurality of feature vectors, obtaining neighbor nodes associated with each of the plurality of central nodes, obtaining a subgraph based on the plurality of central nodes and the neighbor nodes, identifying the connection probabilities between the plurality of central nodes of the subgraph and the neighbor nodes of each of the plurality of central nodes based on a graph convolutional network, and clustering the plurality of target areas based on the identified connection probabilities.

An image processing technique is presented using a hierarchical image model. The technique may be used as a precursor to subsequent image processing, to fix detected images in a post processing stage or as a segmentation or classification stage. The techniques may also be applied to super resolution. In a first layer of the hierarchical image model, each observed pixel of the image has a representation allocated to one or more input node. A set of the input nodes are assigned to a hidden node of a second layer, and a duplicate set of input nodes of the first layer is assigned to a duplicate of the hidden node in the second layer. In this way, a dense latent tree is formed in which a subtree is duplicated. Variables are assigned to the input nodes, the hidden node and the duplicate nodes and recurringly modified to process the image. Belief propagation messages may be utilised to recursively modify the variables. An image processing system using the method is described. A planning system for an autonomous vehicle comprising the image processing system is described.

An image processing apparatus according to the present invention includes a first classification unit configured to classify a plurality of pixels in two-dimensional image data constituting first three-dimensional image data including an object into a first class group by using a trained classifier, and a second classification unit configured to classify a plurality of pixels in second three-dimensional image data including the object into a second class group based on a result of classification by the first classification unit, the second class group including at least one class of the first class group. According to the image processing apparatus according to the present invention, a user's burden of giving pixel information can be reduced and a region can be extracted with high accuracy.

Techniques for processing multiplexed immunofluorescence (MxIF) images. The techniques include: obtaining at least one MxIF image of a same tissue sample; obtaining information indicative of locations of cells in the at least one MxIF image; identifying multiple groups of cells in the at least one MxIF image at least in part by: determining feature values for at least some of the cells using the at least one MxIF image and the information indicative of locations of the at least some cells in the at least one MxIF image; and grouping the at least some of the cells into the multiple groups using the determined feature values; and determining at least one characteristic of the tissue sample using the multiple cell groups.

The present invention provides a method of generating a robust global map using a plurality of limited field-of-view cameras to capture an environment.

Provided is a method for generating a three-dimensional map comprising: receiving a plurality of sequential image data wherein each of the plurality of sequential image data comprises a plurality of sequential images, further wherein the plurality of sequential images is obtained by a plurality of limited field-of-view image sensors; determining a pose of each of the plurality of sequential images of each of the plurality of sequential image data; determining one or more overlapping poses using the determined poses of the sequential image data; selecting at least one set of images from the plurality of sequential images wherein each set of images are determined to have overlapping poses; and constructing one or more map portions derived from each of the at least one set of images.

An automatic calibration and validation pipeline is disclosed to estimate and evaluate the accuracy of extrinsic parameters of a camera-to-LiDAR coordinate transformation. In an embodiment, an automated and unsupervised calibration procedure is employed where the computed rotational and translational parameters (“extrinsic parameters”) of the camera-to-LiDAR coordinate transformation are automatically estimated and validated, and upper bounds on the accuracy of the extrinsic parameters are set. The calibration procedure combines three-dimensional (3D) plane, vector and point correspondences to determine the extrinsic parameters, and the resulting coordinate transformation is validated by analyzing the projection of a filtered point cloud including a validation target in the image space. A single camera image and LiDAR scan (a “single shot”) are used to calibrate and validate the extrinsic parameters. In addition to only requiring a single shot, the complete procedure solely relies on one or more planar calibration targets and simple geometrical validation targets.

A method includes accessing an audiovisual composition comprising a target video segment and a source video segment. The method also includes, in response to presence of the target video segment and the source video segment in the audiovisual composition: accessing a first keyword associated with the source video segment; and calculating a first relevance score for the first keyword relative to the target video segment based on a temporal position of the source video segment in the audiovisual composition and a temporal position of the target video segment in the audiovisual composition; accessing a textual query comprising the first keyword. The method additionally includes: generating a first query result based on the textual query, the first query result comprising the target video segment based on the first relevance score; and at a native composition application, rendering a representation of the first query result.