A deep learning based license plate identification method, device, equipment, and storage medium. The deep learning based license plate identification method comprises: extracting features of an original captured image by using a single shot multi-box detector to obtain a target license plate image; correcting the target license plate image to obtain a corrected license plate image; identifying the corrected license plate image by using a bi-directional long short-term memory model to obtain target license plate information. When the deep learning based license plate identification method performs license plate identification, the identification efficiency is high and the accuracy is higher.

The invention relates to a computer-implemented method (10) for determining the blood volume flow (I.sub.BI) through a portion (90.sub.i, i=1, 2, 3, . . . ) of a blood vessel (88) in an operating region (36) using a fluorophore. A plurality of images (80.sub.1, 80.sub.2, 80.sub.3, 80.sub.4, . . . ) are provided, which are based on fluorescent light in the form of light having wavelengths lying within a fluorescence spectrum of the fluorophore, and which show the portion (90.sub.i) of the blood vessel (88) at different recording times (t.sub.1, t.sub.2, t.sub.3, t.sub.4, . . . ). By processing at least one of the provided images (80.sub.1, 80.sub.2, 80.sub.3, 80.sub.4, . . . ), a diameter (D) and a length (L) of the portion (90.sub.i) of the blood vessel (88) and also a time interval for a propagation of the fluorophore through the portion (90.sub.i) of the blood vessel (88) are determined, which time interval describes a characteristic transit time (τ) for the fluorophore in the portion (90.sub.i) of the blood vessel (88), in which a blood vessel model (M.sub.B.sup.Q) for the portion (90.sub.i) of the blood vessel (88) is specified, which blood vessel model describes the portion (90.sub.i) of the blood vessel (88) as a flow channel (94) having a length (L), having a wall (95) with a wall thickness (d), and having a free cross section Q. A fluid flow model M.sub.F.sup.Q for the blood vessel model (M.sub.B.sup.Q) is assumed, which fluid flow model describes a local flow velocity (122) at different positions over the free cross section Q of the flow channel (94) in the blood vessel model (M.sub.B.sup.Q), and a fluorescent light model M.sub.L.sup.Q is assumed, which describes a spatial probability density for the intensity of the remitted light at different positions over the free cross section Q of the flow channel (94) in the blood vessel model (M.sub.B.sup.Q), which light is emitted by a fluid, which is mixed with fluorophore and flows through the free cross section Q of the flow channel (94) in the blood vessel model (M.sub.B.sup.Q), when said fluid is irradiated with fluorescence excitation light. The blood volume flow (I.sub.BI) is determined as a fluid flow guided through the flow channel (94) in the blood vessel model (M.sub.B.sup.Q), which fluid flow is calculated from the length (L) and the diameter (D) of the portion (90.sub.i) of the blood vessel (88) and from the characteristic transit time (τ) for the fluorophore in t

A vehicle control device includes: detecting at least one of a position or a state of occupant in a vehicle; determining an operation inputting part that is most easily operable for the occupant from among a plurality of operation inputting parts in the vehicle based on the at least one of the position or the state of the occupant which is detected in the detecting; notifying a consent request in the vehicle with respect to a processing content scheduled to be performed in response to an occurrence of an event; and validating consent operation to the operation inputting part that is most easily operable.

An image capture and processing device captures an image. Based on the image and/or one or more additional images, the image capture and processing device generates and outputs guidance for optimizing image composition, image capture settings, and/or image processing settings. The guidance can be generated based on determination of a direction that a subject of the image is facing, based on sensor measurements indicating that a horizon may be skewed, another image of the same scene captured using a wide-angle lens, another image of the same subject, another image of a different subject, and/or outputs of a machine learning model trained using a set of images. The image capture and processing device can automatically apply certain aspects of the generated guidance, such as image capture settings and/or image processing settings.

Disclosed is a ship identity recognition method based on the fusion of AIS data and video data, comprising: collecting a ship sample to train a ship target classifier; performing, using the ship target classifier, ship target detection on a video frame collected by a gimbal camera; performing a comparison with a recognized ship library to filter a recognized ship; acquiring AIS data and filtering same across time and spatial scales; predicting the current position of an AIS target using a linear extrapolation method and converting the current position to an image coordinate system; performing position matching between a target to be matched and the converted AIS target; and performing feature extraction on the successfully matched target and storing the extracted feature, together with ship identity information, into the recognized ship library. Experimental results show that the present invention can quickly and accurately extract a surveillance video and perform identity recognition on the ship target, effectively reduces labor costs, and has a broad application prospect in the fields such as ship transportation and port management.

Disclosed is face tracking method and device. The method includes: acquiring an initial facial image in a to-be-tracked picture; performing binarization processing on the initial facial image according to a standard range of color parameter and an actual value of the color parameter of each pixel in the initial facial image, to obtain a binarized facial image; acquiring a position of a preset organ in the binarized facial image; and acquiring a position of a final facial image according to the position of the preset organ and a position of the initial facial image.

Implementations described herein relate to methods, systems, and computer-readable media to render a foreground video. In some implementations, a method includes receiving a plurality of video frames that include depth data and color data. The method further includes downsampling the frames of the video. The method further includes, for each frame, generating an initial segmentation mask that categorizes each pixel of the frame as foreground pixel or background pixel. The method further includes determining a trimap that classifies each pixel of the frame as known background, known foreground, or unknown. The method further includes, for each pixel that is classified as unknown, calculating and storing a weight in a weight map. The method further includes performing fine segmentation to obtain a binary mask for each frame. The method further includes upsampling the plurality of frames based on the binary mask for each frame to obtain a foreground video.

Systems and methods for using machine learning to assess text legibility in an electronic document are disclosed. According to certain aspects, an electronic device may train a machine learning model using training data that includes at least a representation of a text legibility level in the training data. Additionally, the electronic device may input the electronic document into the machine learning model, which may analyze the electronic document and output a representation of the text legibility level of a set of textual content included in the electronic document. The electronic device may display the output for review and assessment by a user, who may use the electronic device to facilitate any modifications to the electronic document.

Aspects of the present disclosure provide an image-based face detection and recognition system that processes and/or analyzes portions of an image using “image strips” and cascading classifiers to detect faces and/or various facial features, such an eye, nose, mouth, cheekbone, jaw line, etc.

A deep learning based license plate identification method, device, equipment, and storage medium. The deep learning based license plate identification method comprises: extracting features of an original captured image by using a single shot multi-box detector to obtain a target license plate image; correcting the target license plate image to obtain a corrected license plate image; identifying the corrected license plate image by using a bi-directional long short-term memory model to obtain target license plate information. When the deep learning based license plate identification method performs license plate identification, the identification efficiency is high and the accuracy is higher.