Systems, methods, devices, server computers, storage media, and instructions for prioritized device action triggered by device scan data are described. In one embodiment, a mobile device performs a method that involves executing a messaging application with an image capture interface and a scanning input. An associated scanning mode comprises capture of scan data from a plurality of input/output modules of the first client device, analyzes the scan data to identify one or more scan data patterns by matching at least a portion of the scan data against a set of data patterns, and selects a priority system action based on the results of the matching of the portion of the scan data against the set of data patterns. In some embodiments, the priority system action is selected based on a priority ranking for identified scan data types.

An image providing apparatus (10) includes a registration unit (12) for registering a plurality of captured images of a predetermined user taken at a plurality of locations, respectively, in a storage unit (11), an acquisition unit (13) for acquiring a first image of the user captured at a specific location, a specification unit (14) for specifying one or more second images including a face area whose degree of match with a face area of the user included in the first image is greater than or equal to a predetermined value from among the plurality of captured images, a generation unit (15) for generating a composite image including the specified second image, and an output unit (16) for outputting the composite image.

An image processing method whereby data pertaining to an estimated captured image obtained from reference data of a sample is acquired using an input acceptance unit, an estimation unit, and an output unit. The data is used when comparing the estimated image and an actual image of the sample, wherein the method includes: an input acceptance unit accepting input of the reference data, process information pertaining to the sample, and trained model data; the estimation unit using the reference data, the process information, and the model data to calculate captured image statistics representing a probabilistic distribution of values attained by the data of the captured image; and the output unit outputting the captured image statistics, and generating the estimated captured image from the captured image statistics. This permits reducing the time required for estimation and to perform comparison in real time.

A system for identifying markers on flowable-matter substrates, the system comprising a processing circuitry configured to: provide one or more reference images, each associated with (a) a corresponding marker, and (b) a corresponding action; obtain an image including a given marker applied on a flowable-matter substrate; identify a matching reference image of the reference images, the matching reference image being associated with the marker corresponding to the given marker; and upon identifying the matching reference image, perform the action associated with the matching reference image.

Methods, apparatuses and computer program products for providing artificial-intelligence-based indicia data editing are provided. For example, an example computer-implemented method may include determining, based at least in part on a data processing model associated with a scan setting module, a first decoded data string corresponding to a first indicia; determining, based at least in part on user input data, a first input data string corresponding to the first indicia; generating a predictive indicia data editing model based at least in part on providing the first decoded data string and the first input data string to an artificial intelligence algorithm; and updating the scan setting module based at least in part on the predictive indicia data editing model.

A leak detection system is configured to provide vehicle-specific leak detection for a vehicle. The leak detection system includes a memory circuit having a plurality of leak detection instructions matched with vehicle identification information. The system additionally includes an ultraviolet light source and a camera configured to capture an image of an area illuminated by the ultraviolet light source. A processor is in operative communication with the memory circuit and the camera. The processor is configured to facilitate identification of at least one of the plurality of leak detection instructions in response to receipt of vehicle identification information associated with the vehicle, and compare the image captured by the camera to a known image of the vehicle to determine presence and location of a leak.

In some examples, an electronic device comprises an image sensor and a processor. The processor is to detect a feature of a frame of a video received via the image sensor, determine whether the feature indicates an object of interest, and, responsive to a determination that the feature indicates the object of interest, limit a first field of view of the image sensor to the object of interest and overlay the first field of view with a second field of view. The second field of view is unlimited.

The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a deep visual fingerprinting model with parameters learned from robust contrastive learning to identify matching digital images and image provenance information. For example, the disclosed systems utilize an efficient learning procedure that leverages training on bounded adversarial examples to more accurately identify digital images (including adversarial images) with a small computational overhead. To illustrate, the disclosed systems utilize a first objective function that iteratively identifies augmentations to increase contrastive loss. Moreover, the disclosed systems utilize a second objective function that iteratively learns parameters of a deep visual fingerprinting model to reduce the contrastive loss. With these learned parameters, the disclosed systems utilize the deep visual fingerprinting model to generate visual fingerprints for digital images, retrieve and match digital images, and provide digital image provenance information.

Aspects of the disclosure generally relate to computing enabled systems and/or devices and may be generally directed to machine learning for computing enabled systems and/or devices. In some aspects, the system captures one or more digital pictures, receives one or more instruction sets, and learns correlations between the captured pictures and the received instruction sets.

This application discloses image processing methods and systems. One method includes: obtaining a first image, obtaining a template image having a life value that determines whether the template image is valid, comparing the first image with the template image, and storing the first image in a template library as a new template image in response to determining that the first image matches the template image.