Abstract: The present disclosure relates to a localization method and apparatus, and a computer apparatus and a computer-readable storage medium. The localization method includes performing radar mapping and visual mapping by respectively using a radar and a vision sensor, wherein a step of the vision mapping includes determining a pose of a key frame; and combining radar localization with vision localization based on the pose of the key frame, to use vision localization results for navigation on a map obtained by the radar mapping.

Abstract: The present disclosure provides image acquisition methods, apparatuses, systems and electronic devices. One image acquisition method includes: acquiring an initial face image of a user by a first image acquisition apparatus; controlling a second image acquisition apparatus to acquire an eye print image of the user according to an acquisition parameter, the acquisition parameter being determined based on the initial face image; and synthesizing the initial face image and the eye print image into a target face image of the user.

Abstract: The present disclosure provides image acquisition methods, apparatuses, systems and electronic devices. One image acquisition method includes: acquiring an initial face image of a user by a first image acquisition apparatus; controlling a second image acquisition apparatus to acquire an eye print image of the user according to an acquisition parameter, the acquisition parameter being determined based on the initial face image; and synthesizing the initial face image and the eye print image into a target face image of the user.

Abstract: A first deep learning model is trained to classify general facial images. Cropped facial images are extracted from the general facial images. A second deep learning model is trained based on the cropped facial images. Face liveness detection is performed based on the trained first deep learning model and the trained second deep learning model.

Abstract: Embodiments of this specification relate to a three-dimensional living-body face detection method, a face authentication recognition method, and apparatuses. The three-dimensional living-body face detection method includes: acquiring multiple frames of depth images for a target detection object; pre-aligning the multiple frames of depth images to obtain pre-processed point cloud data; normalizing the point cloud data to obtain a grayscale depth image; and performing living-body detection based on the grayscale depth image and a living-body detection model.

Abstract: The present disclosure provides image acquisition methods, apparatuses, systems and electronic devices. One image acquisition method includes: acquiring an initial face image of a user by a first image acquisition apparatus; controlling a second image acquisition apparatus to acquire an eye print image of the user according to an acquisition parameter, the acquisition parameter being determined based on the initial face image; and synthesizing the initial face image and the eye print image into a target face image of the user.

Abstract: Embodiments of this specification relate to a three-dimensional living-body face detection method, a face authentication recognition method, and apparatuses. The three-dimensional living-body face detection method includes: acquiring multiple frames of depth images for a target detection object; pre-aligning the multiple frames of depth images to obtain pre-processed point cloud data; normalizing the point cloud data to obtain a grayscale depth image; and performing living-body detection based on the grayscale depth image and a living-body detection model.

Abstract: The present disclosure provides image acquisition methods, apparatuses, systems and electronic devices. One image acquisition method includes: acquiring an initial face image of a user by a first image acquisition apparatus; controlling a second image acquisition apparatus to acquire an eye print image of the user according to an acquisition parameter, the acquisition parameter being determined based on the initial face image; and synthesizing the initial face image and the eye print image into a target face image of the user.

Abstract: A first deep learning model is trained to classify general facial images. Cropped facial images are extracted from the general facial images. A second deep learning model is trained based on the cropped facial images. Face liveness detection is performed based on the trained first deep learning model and the trained second deep learning model.

Abstract: A method and a device for reducing the waste of the central process unit (CPU) resource during the filling process of a vector graphic, and the method includes: parsing the vector graphic to obtain a series of polygons; mapping the polygons into a new drawing coordinate whose unit is 1/2n pixel using a transform parameter and a transform matrix, while updating an original drawing window into the new drawing coordinate at the same time, wherein the transform parameter is A, A=2n/K, n is a shift parameter, the unit of the vertex coordinate of the polygon is 1/K pixel, and n, K are valued for natural numbers; obtaining the portions of the polygons located in the new drawing window in the new drawing coordinate; transforming the portions of the polygons located in the new drawing window in the new drawing coordinate into a group of scan segments; and shifting the original code of the coordinate of the scan segments to right by n bit, and rendering the pixels of the shifted scan segments.

Abstract: A method for transmitting spectrum information is provided. The method includes: sampling a first video of a scene by a first sampling device and sampling a spectrum video of a sampling point in the scene by a second sampling device, and processing the spectrum video to obtain a spectrum information of the sampling point; calculating estimated transmission ratio coefficients of the spectrum video according to color integral curves of the first sampling device; estimating a location of the sampling point in each frame of the first video; and transmitting the spectrum information of the sampling point to a plurality of scene points in the first video according to the estimated transmission ratio coefficients and the location of the sampling point in each frame of the spectrum video through a trilateral filtering algorithm.

Abstract: A method and a device for reducing the waste of the central process unit (CPU) resource during the filling process of a vector graphic, and the method includes: parsing the vector graphic to obtain a series of polygons; mapping the polygons into a new drawing coordinate whose unit is 1/2n pixel using a transform parameter and a transform matrix, while updating an original drawing window into the new drawing coordinate at the same time, wherein the transform parameter is A, A=2n/K, n is a shift parameter, the unit of the vertex coordinate of the polygon is 1/K pixel, and n, K are valued for natural numbers; obtaining the portions of the polygons located in the new drawing window in the new drawing coordinate; transforming the portions of the polygons located in the new drawing window in the new drawing coordinate into a group of scan segments; and shifting the original code of the coordinate of the scan segments to right by n bit, and rendering the pixels of the shifted scan segments.