A computer-implemented method includes receiving a 3D model of upper teeth (U1) of a patient (P) and a 3D model of lower teeth (L1) of the patient (P1), and receiving a plurality of 2D images, each image representative of at least a portion of the upper teeth (U1) and lower teeth (L1) of the patient (P). The method also includes determining, based on the 2D images, a spatial relationship between the upper teeth (U1) and lower teeth (L1) of the patient (P).

A computer-implemented method includes receiving a 3D model of upper teeth (U1) of a patient (P) and a 3D model of lower teeth (L1) of the patient (P1), and receiving a plurality of 2D images, each image representative of at least a portion of the upper teeth (U1) and lower teeth (L1) of the patient (P). The method also includes determining, based on the 2D images, a spatial relationship between the upper teeth (U1) and lower teeth (L1) of the patient (P).

The present disclosure provides an image processing method, an image processing device, an electronic apparatus and a readable storage medium. The image processing method includes: obtaining feature map data of an input image; extracting a feature region in the feature map data in accordance with a size of a convolution kernel; performing windowing processing on the feature region; and obtaining a windowed feature map of the input image in accordance with the feature region obtained after the windowing processing.

The present disclosure provides an image processing method, an image processing device, an electronic apparatus and a readable storage medium. The image processing method includes: obtaining feature map data of an input image; extracting a feature region in the feature map data in accordance with a size of a convolution kernel; performing windowing processing on the feature region; and obtaining a windowed feature map of the input image in accordance with the feature region obtained after the windowing processing.

The invention relates to a method of encoding a digital image in order to compress same, the digital image being defined as a point cloud associating a set of N pixels, designated as vertices, to a scalar intensity value. The method aims at establishing triangulation vertices of the digital image and implements the principles of algorithmic topology.

A facial verification method includes separating a query face image into color channel images of different color channels, obtaining a multi-color channel target face image with a reduced shading of the query face image based on a smoothed image and a gradient image of each of the color channel images, extracting a face feature from the multi-color channel target face image, and determining whether face verification is successful based on the extracted face feature.

A facial verification method includes separating a query face image into color channel images of different color channels, obtaining a multi-color channel target face image with a reduced shading of the query face image based on a smoothed image and a gradient image of each of the color channel images, extracting a face feature from the multi-color channel target face image, and determining whether face verification is successful based on the extracted face feature.

Aspects of the present disclosure relate to machine learning techniques for continuous implementation and training of a machine learning system for identifying the natural language meaning of visual content. A computer vision model or other suitable machine learning model can predict whether a given descriptor is associated with the visual content. A set of such models can be used to determine whether particular ones of a set of descriptors are associated with the visual content, with the determined descriptors representing a meaning of the visual content. This meaning can be refined based on a multi-armed bandit tracking and analyzing interactions between the visual content and users associated with certain personas related to the determined descriptors.

A follicle identifying device includes an image acquiring unit configured to acquire an image of a follicle and a hair included in the follicle for each follicle separated from a scalp cut from back of a head of an alopecic patient in an incisional hair transplant or each follicle directly extracted from back of a head of an alopecic patient in a non-incisional hair transplant, an image processing unit configured to extract edges of a follicle and a hair from the image of the follicle and the hair acquired by the image acquiring unit, a hair count determining unit configured to determine a hair count in the follicle based on the edges of the follicle and the hair extracted by the image processing unit, and a control unit configured to output the hair count in the follicle determined by the hair count determining unit.

Aspects of the present disclosure include methods for generating a heatmap including a plurality of sampling points having a plurality of characteristic values associated with the detected non-visible light, identifying one or more macroblocks each includes a subset of the plurality of sampling points, calculating a number of occurrences of the local pattern value within each subset of the plurality of the sampling points for each of the one or more macroblocks, generating a first array including a plurality of weighted values by calculating the plurality of weighted values based on the numbers of occurrences of the local pattern value and corresponding sizes of the one or more macroblocks, assigning a unique index to each of the plurality of weighted values, generating a second array of the unique index by ranking the plurality of weighted values, and generating a third array including a plurality of ranking distances.