Provided are a method for processing multimodal images, an apparatus, a device and a storage medium. Multiple types of vision sensors are disposed in first preset identity recognition scenario. The method includes: if it is determined that a first vision sensor detects a biometric part of a target object, controlling each vision sensor to separately perform image acquisition for the biometric part in accordance with a preset acquisition strategy to obtain a target visual image of corresponding type and acquisition time information of the target visual image; performing identity recognition for the target object according to first target visual image to determine object identification information corresponding to first target visual image; determining object identification information corresponding to a target visual image of other type other than first target visual image according to acquisition time information of each target visual image and object identification information corresponding to first target visual image.

A method and apparatus for locating an express package. The method includes: receiving an express locating request, where the express locating request is used to locate an express waybill corresponding to a target express package from an express image presenting express packages, and the express locating request includes express information; searching for pre-stored location information of the express waybill corresponding to the express information; generating an express locating result image by means of the location information, where the express locating result image includes a locating identifier, and the locating identifier is used to indicate the express waybill corresponding to the target express package from the express image; and outputting the express locating result image.

A process for identifying a road feature in an image includes receiving an image at a controller, identifying a region of interest within the image and converting the region of interest from red-green-blue (RGB) to a single color using the controller. A set edges is detected within the region of interest, and at least one line within the set of edges is identified using the controller. The at least one line is compared with a set of known and expected road marking features, and the set of at least one first line in the at least one line is identified as corresponding to a road feature in response to the at least the first line matching the set of known and expected road marking features.

A defect inspection system is disclosed, and comprises a linear light source, N number of cameras, a display device, a tag reader, and a modular electronic device, in which the linear light source, the cameras and the modular electronic device are used for conducting a defect inspection of an article. On the other hand, the display device, the tag reader and the modular electronic device are adopted for conducting in production of at least one labeled example. Therefore, the modular electronic device is allowed to apply a machine learning process to an image classifier under using a training dataset containing the labeled examples, thereby producing at least one new defect recognition model or updating the existing defect recognition model.

A method and system is provided for determining the denomination and related data for a currency item using a personal computing device, such as a mobile phone. The device includes or is connected to an image capture device that is preferably a digital video camera. At least one image of a target currency item is captured then processed for image quality. A further processing of the image includes a coordinate mapping. A comparison is made between individual pixels of the processed image based on the assigned coordinate mapping with a database of reference currency images to determine the currency denomination. Additional processing of the currency image provides the date and other data regarding the target currency item. A market value for the target currency item is identified by reference to a valuation database using the data determined for the currency item.

A hub image retrieval method and a device thereof for improving the retrieval accuracy of an image containing a hub. The method includes: performing feature extraction on a hub image to be processed containing a target hub, and obtaining a hub feature to be processed containing N sub-hub features, wherein the N sub-hub features at least include information characterizing the features of a hub window of the target hub; determining similarities between candidate hub features of each candidate hub image and the hub features to be processed; and selecting a candidate hub image matching the hub image to be processed from the plurality of candidate hub images based on the determined respective similarities.

A method for training a model, the method including: defining a primary model for identifying a class of input data based on a first characteristic of the input data; defining a secondary model for detecting a change to a second characteristic between multiple input data captured at different times; defining a forward link from an output of an intermediate layer of the secondary model to an input of an intermediate layer of the primary model; and training the primary model and the secondary model in parallel based on a training set of input data.

A measurement image acquisition unit 72 acquires a measurement image indicating a measurement value of a predetermined physical quantity for a measurement target including a plurality of types of body tissues. A body tissue image generation unit 74 generates a body tissue image associated with each of the plurality of types of body tissues by executing, for the each of the plurality of types of body tissues, a filtering process corresponding to the each of the plurality of types of body tissues with respect to the measurement image. A masked body tissue image generation unit 78 generates a masked body tissue image associated with a specific type of body tissue by executing, with respect to the body tissue image associated with the specific type of body tissue, a masking process which is based on the body tissue image associated with a different type of body tissue.

An object association method, apparatus and system, and a storage medium are provided. The method includes: obtaining a first image and a second image; and determining an association relationship between objects in the first image and objects in the second image based on surrounding information of the objects in the first image and surrounding information of the objects in the second image, where the surrounding information of one object is determined according to pixels within a set range around a bounding box of the object in the image where the object is located.

The present invention provides a method of angiography for cerebrovascular obliteration includes: using a classifier to obtain a 2D medical image using from a plurality of Multiphase CTA images; using a gray-scale conversion to obtain a N*M pixels grayscale image; filtering the grayscale image not being meet a condition of grayscale threshold and performing an image binarization to obtain a binarized image; confirming at least one vascular region and performing an image skeletonization; filtering according to a vascular image features of a vascular region to obtain a vascular-enhanced image; using a fracture analysis to obtain an analysis report related to a plurality of quantifying parameters of vascular characteristics; wherein the quantifying parameters of vascular characteristics comprises a quantitative value of fractal dimension (FD), vessel density (VD), skeleton density (SD) and vascular diameter index (VDI). Therefore, improve the accuracy of clinician diagnosis and the survival rates of patients with ischemic stroke