The present disclosure relates to a scene detection method, a chip, an electronic device, and a storage medium, the present disclosure detects periodic scenes, which helps to improve the accuracy of motion estimation.

Systems and methods for eye image set selection, eye image collection, and eye image combination are described. Embodiments of the systems and methods for eye image collection can include displaying a graphic along a path connecting a plurality of eye pose regions. Eye images at a plurality of locations along the path can be obtained, and an iris code can be generated based at least partly on at least some of the obtained eye images.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a computing system that receives images that each have a predefined exposure attribute. For each image, a first set of features of the image are extracted. The first set of features are associated with a label indicating no modification of the image is required. A luminosity characteristic of the image is adjusted to form an adjusted image. A second set of features of the adjusted image are extracted. A neural network is trained to adjust luminosity characteristics of images using the first set of features and the second set of features of the adjusted image. An exposure adjustment model adjusts luminosity characteristics of images based on correction values determined using the trained neural network.

A medical scan interface feature evaluator system is operable to generate an ordered image-to-prompt mapping by selecting a set of user interface features to be displayed with each of an ordered set of medical scans. The set of medical scans and the ordered image-to-prompt mapping are transmitted to a set of client devices. A set of responses are generated by each client device in response to sequentially displaying each of the set of medical scans in conjunction with a mapped user interface feature indicated in the ordered image-to-prompt mapping via a user interface. Response score data is generated by comparing each response to truth annotation data of the corresponding medical scan. Interface feature score data corresponding to each user interface feature is generated based on aggregating the response score data, and is used to generate a ranking of the set of user interface features.

The disclosure relates to the technical field of data processing. Disclosed are a pedestrian flow funnel generation method and apparatus, a storage medium and an electronic device. The method can comprise: acquiring a current frame of image, and tracking and updating, according to a multi-target tracking algorithm, a head and shoulder area in a tracking sequence set in the current frame of image; acquiring, according to a head and shoulder recognition model, a head and shoulder area in the current frame of image, and updating, according to the head and shoulder area in the current frame of image, the tracking sequence set; analysing a motion track of each head and shoulder area in the tracking sequence set so as to count pedestrians, and when the current frame of image is the last frame of image, generating a pedestrian flow funnel based on a counting result of pedestrians.

Systems and methods for automatic enrollment and identity verification based upon processing a captured image of a document are disclosed herein. Various embodiments enable, for example, a user to enroll in a particular service by taking a photograph of a particular document (e.g., his driver license) with a mobile device. One or more algorithms can then extract relevant data from the captured image. The extracted data (e.g., the person's name, gender, date of birth, height, weight, etc.) can then be used to automatically populate various fields of an enrollment application, thereby reducing the amount of information that the user has to manually input into his mobile device in order to complete the enrollment process. In some embodiments, a set of internal and/or external checks can be run against the data to ensure that the data is valid, has been read correctly, and is consistent with other data.

Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving a call to an API node. The operations may include determining that the call is associated with the first version of the API. The operations may include determining that the API node is associated with a second version of the API. The operations may include translating the call into a translated call using a translation model, the translated call being associated with the second version of the API.

Disclosed herein are computer-implemented methods; computer-implemented systems; and non-transitory, computer-readable media, for quantifying text similarity. One computer-implemented method includes obtaining a plurality of shortest operation paths including one or more edit pairs for correcting an optical correction recognition (OCR) text string with an edit text string, where each of the one or more edit pairs denotes an operation performable to a character of the OCR text string during correction by the edit text string. A plurality of similarity scores is determined, each corresponding to one of the plurality of shortest operation paths and determined by summing historical similarity scores of the one or more edit pairs of each of the plurality of shortest operation paths. A minimum one of the plurality of similarity scores is selected to quantify text similarity between the OCR text string and the edit text string.

There are provided system and method of examining a semiconductor specimen. The method comprises: upon obtaining a Deep Neural Network (DNN) trained for a given examination-related application within a semiconductor fabrication process, processing together one or more fabrication process (FP) images using the obtained trained DNN, wherein the DNN is trained using a training set comprising ground truth data specific for the given application; and obtaining examination-related data specific for the given application and characterizing at least one of the processed one or more FP images. The examination-related application can be, for example, classifying at least one defect presented by at least one FP image, segmenting the at least one FP image, detecting defects in the specimen presented by the at least one FP image, registering between at least two FP images, regression application enabling reconstructing the at least one FP image in correspondence with different examination modality, etc.

The present subject matter is related in general to the field of image processing, disclosing method and system for evaluating an image quality for Optical Character Recognition (OCR) Image evaluation system receives image comprising optical character data. The image evaluation system determines image parameter value for each of one or more image parameters of the image. The image parameter value for each of the one or more image parameters is determined for plurality of binary image segments identified in the image. The image evaluation system determines suitability value and impact value of the image, based on the image parameter value for each of the image parameters determined for the image. The image evaluation system determines quality score for the image, based on the suitability value and the impact value. The image is transmitted for processing before the OCR, upon determining the quality score to be above overall pre-defined threshold value.