In an example, a first set of text may be received from a client device. A set of content items may be selected from among content items based upon the first set of text and a plurality of sets of content item text associated with the content items. A set of terms may be determined based upon the first set of text and the set of content items. A similarity profile associated with the set of terms may be generated. The similarity profile is indicative of similarity scores associated with similarities between terms of the set of terms. Relevance scores associated with the set of terms may be determined based upon the similarity profile. One or more search terms may be selected from among the set of terms based upon the relevance scores. A search may be performed based upon the one or more search terms.

The present application related to a method for detecting an object image using a convolutional neural network. Firstly, obtaining feature images by Convolution kernel, and then positioning an image of an object under detected by a default box and a boundary box from the feature image. By Comparing with the sample image, the detected object image is classifying to an esophageal cancer image or a non-esophageal cancer image. Thus, detecting an input image from the image capturing device by the convolutional neural network to judge if the input image is the esophageal cancer image for helping the doctor to interpret the detected object image.

The present application related to a method for detecting an object image using a convolutional neural network. Firstly, obtaining feature images by Convolution kernel, and then positioning an image of an object under detected by a default box and a boundary box from the feature image. By Comparing with the sample image, the detected object image is classifying to an esophageal cancer image or a non-esophageal cancer image. Thus, detecting an input image from the image capturing device by the convolutional neural network to judge if the input image is the esophageal cancer image for helping the doctor to interpret the detected object image.

A method and apparatus for processing human body model data, an electronic device and a storage medium are provided. The method includes: obtaining 3D human body model data, and classifying the 3D human body model data into multiple data sets according to a predetermined classification condition, wherein the predetermined classification condition includes medical anatomy category information and art resource category information; determining, according to each of the data sets, a duplicate resource in the data set, and reorganized data sets where the duplicate resource is removed; and packing each of the duplicate resource and the reorganized data sets into a respective data package, and storing all of the data packages.

A method and apparatus for processing human body model data, an electronic device and a storage medium are provided. The method includes: obtaining 3D human body model data, and classifying the 3D human body model data into multiple data sets according to a predetermined classification condition, wherein the predetermined classification condition includes medical anatomy category information and art resource category information; determining, according to each of the data sets, a duplicate resource in the data set, and reorganized data sets where the duplicate resource is removed; and packing each of the duplicate resource and the reorganized data sets into a respective data package, and storing all of the data packages.

Present disclosure provides a method for determining if an event relates to an unauthorized subject, the method comprising: determining a likelihood of how the event is similar to at least one of: (i) at least one event of a list of events relating to the unauthorized subject and (ii) at least one of a list of events relating to an authorized subject, each event of the list of events comprising data identifying the unauthorized subject, the determination of likelihood being based on the data identifying the unauthorized subject; and determining the event to relate to the unauthorized subject in response to the determination of the likelihood.

Three-dimensional (3D) modeling systems and methods are described for automatically generating photorealistic, virtual 3D package and product models from two-dimensional (2D) imaging assets and dimensional data. The 3D modeling systems and methods include storing, by a memory with one or more processors, 2D imaging assets and dimensional datasets, obtaining, with an imaging asset manipulation script, a shape classification defining a real-world product or product package to be virtually modeled in 3D space, generating, with the imaging asset manipulation script, a spline based on an alpha channel extracted from a 2D imaging asset depicting the real-world product or package, and generating, with the imaging asset manipulation script, a parametric model based on the spline, the dimensional dataset, and the shape classification. A virtual 3D model is generated based on the parametric model and rendered, via a graphical display or environment, as a photorealistic image representing the real-world product or product package.

Three-dimensional (3D) modeling systems and methods are described for automatically generating photorealistic, virtual 3D package and product models from two-dimensional (2D) imaging assets and dimensional data. The 3D modeling systems and methods include storing, by a memory with one or more processors, 2D imaging assets and dimensional datasets, obtaining, with an imaging asset manipulation script, a shape classification defining a real-world product or product package to be virtually modeled in 3D space, generating, with the imaging asset manipulation script, a spline based on an alpha channel extracted from a 2D imaging asset depicting the real-world product or package, and generating, with the imaging asset manipulation script, a parametric model based on the spline, the dimensional dataset, and the shape classification. A virtual 3D model is generated based on the parametric model and rendered, via a graphical display or environment, as a photorealistic image representing the real-world product or product package.

Aspects described provide systems and methods that relate generally to image analysis and, more specifically, identifying individual components and elements in an image. The systems and methods include a valuation application executing one or more application program interfaces (APIs) communicating with one or more websites via a network, where the user is prompted to enter information and/or take pictures or videos of their vehicle that they would like to sell. The valuation application utilizes a machine learning model to identify and value the various vehicle components within the images and videos. Based on the machine learning model, the valuation application identifies each component according to the images and videos and performs a search to determine the value of the components identified. The valuation application tabulates and summarizes the vehicle component resale values and resell information for the user to view.

An apparatus for determining defective hair follicles includes an image acquiring unit for acquiring an image of a follicle and a hair for each follicle separated from a scalp of an alopecic patient in an incisional hair transplant or each follicle directly extracted from an alopecic patient in a non-incisional hair transplant, an image processing unit for extracting an outline pattern of the image of the follicle and the hair by performing a contour detection process or an edge detection process on the image, a follicle shape database for storing hair pixel patterns related to various shapes of hairs and follicle pixel patterns related to various shapes of follicles, and a follicle determining unit for determining whether a follicle is normal follicle or defective follicle by comparing the outline pattern of the image with the hair pixel patterns and follicle pixel patterns stored in the follicle shape database.