Comprehensive 2D learning images are collected for learning subjects. Standardized 2D gallery images of many gallery subjects are collected, one per gallery subject. A 2D query image of a query subject is collected, of arbitrary viewing aspect, illumination, etc. 3D learning models, 3D gallery models, and a 3D query model are determined from the learning, gallery, and query images. A transform is determined for the selected learning model and each gallery model that yields or approximates the query image. The transform is at least partly 3D, such as 3D illumination transfer or 3D orientation alignment. The transform is applied to each gallery model so that the transformed gallery models more closely resemble the query model. 2D transformed gallery images are produced from the transformed gallery models, and are compared against the 2D query image to identify whether the query subject is also any of the gallery subjects.

A non-transitory computer readable storage medium storing computer readable instructions executable by a computer is provided. The computer readable instructions cause the computer to obtain first image data composing a first image and second image data composing a second image; specify first edge pixels in the first image and second edge pixels in the second image using the first image data and the second image data, respectively; specify a first feature point and a second feature point corresponding to each other using an algorithm different from an algorithm to specify the first edge pixels and the second pixels; determine pixels located in vicinity to the first feature pint and pixels located in vicinity to the second feature point as first usable pixels and second usable pixels, respectively; and using the first usable pixels and the second usable pixels, determine positional relation between the first image and the second image.

A processor-implemented method with object pose estimation includes: determining an image feature corresponding to a point cloud of an input image; determining semantic segmentation information, instance mask information, and keypoint information of an object, based on the image feature; and estimating a pose of the object based on the semantic segmentation information, the instance mask information, and the keypoint information.

This disclosure relates generally to a system and method to identify various products on a plurality of images of various shelves of a retail store to facilitate compliance with respect to planograms. Planogram is a visual plan, which designates the placement of products on shelves and merchandising display fixtures of a retail store. Planograms are used to create consistency between store locations, to provide proper shelf space allocation, to improve visual merchandising appeal, and to create product-pairing suggestions. There are a few assumptions considering one instance per product class is available beforehand and the physical dimension of each product template is available in some suitable unit of length. In case of absence of physical dimension of the products, a context information of the retail store will be used. The context information is that the products of similar shapes or classes are arranged together in the shelves for consumers' convenience.

Embodiments of the present disclosure disclose an image processing method and apparatus, and an electronic device. The method includes: obtaining a key frame image that includes a target object, and obtaining a to-be-processed frame image that includes the target object; extracting a feature point in the key frame image and a feature point in the to-be-processed frame image, respectively; determining a matching relationship between each feature point in the key frame image and each feature point in the to-be-processed frame image; determining a transformation relationship based on the feature point in the key frame image and a feature point in the to-be-processed frame image that matches the feature point in the key frame image; and processing the to-be-processed frame image based on the transformation relationship to obtain a target frame image.

In one embodiment, a method includes projecting a source image onto a surface using a lens approximation component, where the surface is associated with sampling points approximating photoreceptors of an eye, where each sampling point has a corresponding photoreceptor type, sampling color information from the projected source image at the sampling points, where the color information sampled at each sampling point depends on the corresponding photoreceptor type, accessing pooling units approximating retinal ganglion cells (RGCs) of the eye, where each pooling unit is associated with groups of one or more of the sampling points, calculating weighted aggregations of the sampled color information associated with the groups of one or more sampling points associated with each pooling unit, and computing a perception profile for the source image based on the weighted aggregations associated with each of the pooling units.

A video similarity detection method and apparatus are provided. In the method, a detection apparatus receives a first video, determines a key frame of the first video based on the first video, and determines a similar key frame and a second video, the similar key frame being determined based on the key frame. The second video includes a video in which the similar key frame is located. The method inputs the key frame and the similar key frame to an editing type recognition model to obtain an editing type. The editing type indicates an editing type used for editing between the first video and the second video.

The disclosure relates to an artificial intelligence (AI) system utilizing a machine learning algorithm, and application thereof. In particular, an electronic apparatus according to the disclosure includes a memory storing a trained artificial intelligence model, and a processor configured to acquire a plurality of feature values by inputting an input image to the artificial intelligence model. The trained artificial intelligence model applies each of a plurality of filters to a plurality of feature maps extracted from the input image and includes a pooling layer for acquiring feature values for the plurality of feature maps to which each of the plurality of filters is applied.

The invention provides for a medical imaging system (100, 400), comprising: The execution of the machine executable instructions (112) causes a processor (104) to: receive (200) a set of input white matter fiber tracts (118): receive (202) the label from a discriminator neural network (116) in response to inputting the set of input w hue matter fiber tracts, generate (204) an optimized feature vector (122) using the set of input white matter fiber tracts and a generator neural network ((114) if the label indicates anatomically incorrect; receive (206) the set of generated white matter fiber tracts from the generator neural network in response to inputting the optimized feature vector, and construct (208) a false positive subset (126) of the set of input white matter fiber tracts using the generated set of white matter fiber tracts.

The present disclosure provides a method, an apparatus, an electronic device and a storage medium for vehicle localization, which relates to the technical fields of autonomous driving, electronic map, deep learning, image processing, and the like. In the method, a computing device obtains an image descriptor map corresponding to a captured image of an external environment of a vehicle and a predicted pose of the vehicle when the captured image is captured; obtains a set of reference descriptors and a set of spatial coordinates corresponding to a set of keypoints of a reference image of the external environment; determines a plurality of sets of image descriptors corresponding to the set of spatial coordinates when the vehicle is in a plurality of candidate poses, respectively; determines a plurality of similarities between the plurality of sets of image descriptors and the set of reference descriptors; and updates the predicted pose based on the plurality of candidate poses and the plurality of similarities. Embodiments of the present disclosure can improve localization accuracy and robustness of the vehicle visual localization algorithm.