The present disclosure provides a system and method for PET image reconstruction. The method may include processes for obtaining physiological information and/or rigid motion information. The image reconstruction may be performed based on the physiological information and/or rigid motion information.

This disclosure relates to improved sketch-based image retrieval (SBIR) techniques. The SBIR techniques utilize a neural network architecture to train a domain migration function and a hashing function. The domain migration function is configured to transform sketches into synthetic images, and the hashing function is configured to generate hash codes from synthetic images and authentic images in a manner that preserves semantic consistency across the sketch and image domains. The hash codes generated from the synthetic images can be used for accurately identifying and retrieving authentic images corresponding to sketch queries, or vice versa.

In one embodiment, a method is provided. The method comprises determining a first value of a coefficient of an edge-determining algorithm in response to a spatial resolution of a first image acquired with an image capture device onboard a vehicle, a spatial resolution of a second image, and a second value of the coefficient in response to which the edge-determining algorithm generated a second edge map corresponding to the second image. The method further comprises determining, with the edge-determining algorithm in response to the coefficient having the first value, at least one edge of at least one object in the first image. The method further comprises generating, in response to the determined at least one edge, a first edge map corresponding to the first image. The method further comprises determining at least one navigation parameter of the vehicle in response to the first and second edge maps.

The present disclosure discloses a photography-based 3D modeling system and method, and an automatic 3D modeling apparatus and method, including: (S1) attaching a mobile device and a camera to the same camera stand; (S2) obtaining multiple images used for positioning from the camera or the mobile device during movement of the stand, and obtaining a position and a direction of each photo capture point, to build a tracking map that uses a global coordinate system; (S3) generating 3D models on the mobile device or a remote server based on an image used for 3D modeling at each photo capture point; and (S4) placing the individual 3D models of all photo capture points in the global three-dimensional coordinate system based on the position and the direction obtained in S2, and connecting the individual 3D models of multiple photo capture points to generate an overall 3D model that includes multiple photo capture points.

Systems and methods for generating feedback for a webpage based on visual interactions on the webpage are provided. In example embodiments, a user interface (UI) displaying the webpage is presented. The system receives an indication of a selection of an edit trigger and configures the webpage to receive feedback (e.g., one or more user inputs applied to webpage) from the user in response. The user inputs are received, whereby each user input is associated with an identifier of the webpage and coordinates of a location within the webpage. The system processes the user inputs including generating a feedback preview that displays each of the user inputs organized based on a corresponding feedback type. The feedback preview is displayed to the user for approval. Approval of at least a portion of the feedback on the feedback preview will cause the approved feedback to be transmitted to a corresponding entity.

A document analysis device that includes a memory operable to store a machine learning model configured to receive a sentence as an input and to output a classification identifier that is associated with a sentence type for the received sentence. The device further includes an artificial intelligence (AI) processing engine configured to receive a document comprising text, to sentences within the document, and to classify the sentences using the machine learning model. The AI processing engine is further configured to identify tagging rules for the document and to annotate one or more sentences from the document with a sentence type that matches a sentence type that is identified by the tagging rules for the document.

A feature learning system (100) includes a similarity definition unit (101), a learning data generation unit (102), and a learning unit (103). The similarity definition unit (101) defines a degree of similarity between two classes related to two feature vectors, respectively. The learning data generation unit (102) acquires the degree of similarity, based on a combination of classes to which a plurality of feature vectors acquired as processing targets belong, respectively, and generates learning data including the plurality of feature vectors and the degree of similarity. The learning unit (103) performs machine learning using the learning data.

Disclosed is a quotation method executed by a computer, comprising: obtaining structure parameters and electrical parameters of a product (S101); constructing an external view of the product by using the structure parameters of the product, and performing similarity comparison on the external view of the product and the external view of a historical product to obtain an appearance similarity sorting (102); performing similarity comparison on the electrical parameters of the product and the electrical parameters of the historical product to obtain an electrical parameter similarity sorting (103); on the basis of the cost weights of a structural member and an electrical component and the appearance similarity sorting and the electrical parameter similarity sorting, obtaining a comprehensive sorting which is based on the structure parameters and the electrical parameters (S104); and determining, based on the comprehensive sorting, a bill of materials of the product, and calculating, based on the bill of the materials of the product, the product quotation (105).

Methods and systems for determining a network embedding include training a network embedding model using training data that includes topology information for networks and attribute information relating to vertices of the networks. An embedded representation is generated using the trained network embedding model to represent an input network, with associated attribute information, in a network topology space. A machine learning task is performed using the embedded representation as input to a machine learning model.

A method for generating a highly distinctive signature of a certain diamond, the method may include generating, based on one or more images of the certain diamond, a certain diamond signature of the certain diamond; finding, out of a group of reference diamonds, other diamonds having other diamond signatures; wherein the finding comprises calculating similarities between the certain diamond signature and reference diamond signatures of the reference diamonds of the group; and generating a new certain diamond signature that significantly differs from signatures of the other diamonds.