An example apparatus for selecting priors includes a training set receiver to receive a training dataset. The apparatus includes a prior generator to generate a set of redundant priors based on the training dataset. The apparatus includes an intermediate trainer to train a detection CNN using the set of redundant priors. The apparatus includes a score and location receiver to send all training samples of the training dataset to the trained detection CNN and receive responses for all of the redundant priors in the set of redundant priors. The apparatus includes a subset selector to select a subset of the set of redundant priors based on the responses.

Methods and systems are disclosed for performing operations for recoloring a fashion item. The operations include receiving an image that includes a depiction of a person wearing a fashion item. The operations include generating a segmentation of the fashion item worn by the person depicted in the image. The operations include extracting a portion of the image corresponding to the segmentation of the fashion item. The operations include estimating lights and shadows being cast on the fashion item in the portion of the image. The operations include applying one or more augmented reality elements to the fashion item in the image based on the lights and shadows being cast on the fashion item.

Methods, systems and apparatuses, including computer programs encoded on computer storage media, are provided for generating prediction models related to targeting and acquiring customers. Thousands of variables of historical data, including data for prospects and external data, are used to train the prediction models. The variables are pre-processed, then sensitivity analysis is performed on the input variables with respect to the target. The variables with the most influence on the target are selected and added to the feature set used for training a prediction model.

Systems and methods for automated detection of changes in extent of structures using imagery are disclosed, including a non-transitory computer readable medium storing computer executable code that when executed by a processor cause the processor to: align an outline of a structure at a first instance of time to pixels within an image depicting the structure, the image captured at a second instance of time; assess a degree of alignment between the outline and the pixels within the image depicting the structure, using a machine learning model to generate an alignment confidence score; determine an existence of a change in extent of the structure based upon the alignment confidence score indicating that the outline and the pixels within the image are not aligned; identify a shape of the change in extent of the structure; and store the shape of the change in extent of the structure.

There is provided a method for generating a personalized Head Related Transfer Function (HRTF). The method can include capturing an image of an ear using a portable device, auto-scaling the captured image to determine physical geometries of the ear and obtaining a personalized HRTF based on the determined physical geometries of the ear.

There is provided a method for generating a personalized Head Related Transfer Function (HRTF). The method can include capturing an image of an ear using a portable device, auto-scaling the captured image to determine physical geometries of the ear and obtaining a personalized HRTF based on the determined physical geometries of the ear.

The present invention aims to provide an information processing device, an information processing method, and the like capable of reducing labor of associating an item for sale with a category. The information processing device acquires an image specified by a user. The information processing device extracts a feature value of the specified image. The information processing device acquires category information corresponding to an item for sale represented by the specified image. The information processing device searches for images similar to the specified image, based on the extracted feature value. The information processing device causes at least one of the found images to be displayed as a search result. The information processing device, when the user has selected any one image from the displayed search result, makes a storage unit store the acquired category information in association with sale item identification information corresponding to the selected image.

Methods and systems for artificial intelligence based medical image segmentation are disclosed. In a method for autonomous artificial intelligence based medical image segmentation, a medical image of a patient is received. A current segmentation context is automatically determined based on the medical image and at least one segmentation algorithm is automatically selected from a plurality of segmentation algorithms based on the current segmentation context. A target anatomical structure is segmented in the medical image using the selected at least one segmentation algorithm.

An information processing apparatus including at least one processor, wherein the processor is configured to: acquire a first image obtained by imaging a subject; extract a first region of interest from the first image; and determine whether or not an unsuitable region that is unsuitable for extraction exists, for the extracted first region of interest.

An image processing method and an information processing device are provided. The information processing device may include a processor, which may be configured to: construct a three-dimensional face model based on a face object in a two-dimensional image; determine a correspondence between the constructed three-dimensional face model and the face object in the two-dimensional image; determine, based on the correspondence, an area of interest of a key point in the two-dimensional image using an area of interest near a corresponding key point in the three-dimensional face model; and extract an image feature from the determined area of interest in the two-dimensional image as an image feature of the key point.