The present disclosure provides a training method and device of a neural network for medical image processing, a medical image processing method and device, and an electronic apparatus for medical image processing based on a neural network. The training method includes performing a pre-processing process on an original image to obtain a pre-processed image, performing a data-augmenting process on the pre-processed image to obtain an augmented image retaining a pathological feature, the augmented image including at least one image with first resolution and at least one image with second resolution being higher than the first resolution, and training the neural network by selecting the image with first resolution and a part-cropping image from the image with second resolution as training samples.

A method may include obtaining a breast image of an object that is acquired by an imaging device; determining a projection curve based on the breast image; determining a first valley point and a second valley point of the projection curve; determining a peak point of the projection curve based the first valley point and the second valley point of the projection curve; determining a first valley location, a second valley location, and a peak location in the breast image based on the peak point, the first valley point, and the second valley point of the projection curve; and determining a breast region in the breast image based on the first valley location, the second valley location, and the peak location.

An example embodiment includes: an extraction unit that extracts a determination object image including a side part of an outer circumference of an iris from an image including an eye; and a determination unit that determines whether or not a colored contact lens is worn based on the determination object image.

An example embodiment includes: an extraction unit that extracts a determination object image including a side part of an outer circumference of an iris from an image including an eye; and a determination unit that determines whether or not a colored contact lens is worn based on the determination object image.

Provided are a face feature point detection method, applied to an image processing device, where the image processing device stores a feature area detection model and a feature point detection model. The method includes: preprocessing a face image to be detected to obtain a preprocessed target face image; performing feature point extraction on the target face image according to the feature area detection model and the feature point detection model to obtain a target feature point coordinate located within a face feature area in the target face image; and performing coordinate transformation on the target feature point coordinate to obtain a face feature point coordinate corresponding to the face image to be detected. Further provided are a face feature point detection device, an equipment and a storage medium.

Real-time evaluation and enhancement of image quality prior to capturing an image of a document on a mobile device is provided. An image capture process is initiated on a mobile device during which a user of the mobile device prepares to capture the image of the document, utilizing hardware and software on the mobile device to measure and achieve optimal parameters for image capture. Feedback may be provided to a user of the mobile device to instruct the user on how to manually optimize certain parameters relating to image quality, such as the angle, motion and distance of the mobile device from the document. When the optimal parameters for image capture of the document are achieved, at least one image of the document is automatically captured by the mobile device.

Real-time evaluation and enhancement of image quality prior to capturing an image of a document on a mobile device is provided. An image capture process is initiated on a mobile device during which a user of the mobile device prepares to capture the image of the document, utilizing hardware and software on the mobile device to measure and achieve optimal parameters for image capture. Feedback may be provided to a user of the mobile device to instruct the user on how to manually optimize certain parameters relating to image quality, such as the angle, motion and distance of the mobile device from the document. When the optimal parameters for image capture of the document are achieved, at least one image of the document is automatically captured by the mobile device.

A control device for a vehicle for determining perceptual load of a visual and dynamic driving scene, the control device being configured to: receive an image sequence representing the driving scene, extract a set of scene features from the image sequence, the set of scene features representing static and/or dynamic information of the driving scene, calculate a time-aggregated representation of the image sequence based on the extracted set of scene features, calculate an attention map of the driving scene by attentional pooling of the time-aggregated representation of the image sequence, and determine the perceptual load of the driving scene based on the attention map. The invention further relates to a corresponding method.

A method and apparatus for image searching based on artificial intelligent (AI) are provided. The method includes obtaining first feature information by extracting features from an image based on a first neural network, obtaining second feature information corresponding to a target area of a query image by processing the first feature information based on a second neural network and at least two filters having different sizes, and identifying an image corresponding to the query image according to the second feature information.

Aspects described herein may allow for generating a set of customized artificial fingernails based on the shape and size of the fingernails of an individual user. A computing system may generate an output file to produce the customized artificial fingernails based on a set of images displaying top-down views of a set of fingernails of a user. A reference table may be used to predict the curvature factor of each of the user's fingernails to provide information for the system regarding the three-dimensional size and shape of the user's fingernail simply based on the two-dimensional top-down views of fingernails in the set of images. That may help the user to get customized artificial fingernails in an easy and convenient way without the burden to display the user's fingernails from multiple angles.