An electronic device and a method for controlling thereof are provided. A method for controlling an electronic device according to the disclosure includes obtaining a plurality of images for performing clustering, obtaining a plurality of target areas corresponding to each of the plurality of images, obtaining a plurality of feature vectors corresponding to the plurality of target areas, obtaining a plurality of central nodes corresponding to the plurality of feature vectors, obtaining neighbor nodes associated with each of the plurality of central nodes, obtaining a subgraph based on the plurality of central nodes and the neighbor nodes, identifying the connection probabilities between the plurality of central nodes of the subgraph and the neighbor nodes of each of the plurality of central nodes based on a graph convolutional network, and clustering the plurality of target areas based on the identified connection probabilities.

Systems and methods for providing additional processing capabilities related to machine-readable symbols. A data collection system (100) may include a scan engine (102), auxiliary image processor (104), auxiliary visualizer (106), and host system (108). The scan engine may output decoded information obtained from a representation of a machine-readable symbol captured by a two-dimensional image processor. The scan engine may also output a set of images related to the machine-readable symbol and an object associated with the machine-readable symbol, in which the set of images may form a streaming set of images or streaming video. The set of images may be used by the auxiliary image processor to obtain further information about the machine-readable symbol and/or associated object, such as OCR or DWM information. The set of images may be stored and made accessible by the auxiliary visualizer. The host system may synchronize a display of the images and decoded data output by the scan engine.

Occlusion of facial features may be detected and assessed in an image captured by a camera on a device. Landmark heat maps may be used to estimate the location of landmarks such as the eyes, mouth, and nose of a user's face in the captured image. An occlusion heat map may also be generated for the captured image. The occlusion heat map may include values representing the amount of occlusion in regions of the face. The estimated locations of the eyes, mouth, and nose may be used in combination with the occlusion heat map to assess occlusion scores for the landmarks. The occlusion scores for the landmarks may be used control one or more operations of the device.

This invention relates to a secure QrCode communication method based on nonlinear spatial frequency characteristics, comprising: camera modeling: modeling according to the spatial frequency of the color filter matrix of the scanning device's camera; QrCode encryption: using the CFA spatial frequency of scanning device and modeling results, as well as the spatial frequency of the display device, generate an encrypted picture of the target QrCode on the display device; QrCode decryption: the camera of the scanning device takes the picture of the display at a specified position and a specified angle, and parses the image to recover the target QrCode. Compared with the prior art, the present invention utilizes the nonlinear characteristics of the optical spatial frequency, and uses the spatial frequency of the camera's own color filter array and the spatial frequency of the display to modulate the target two-dimensional code through phase to achieve the effect of encryption.

A method and an apparatus for processing a screen by using a device are provided. The method includes obtaining, at the second device, a display screen displayed on the first device and information related to the display screen according to a screen display request regarding the first device, determining, at the second device, an additional screen based on the display screen on the first device and the information related to the display screen, and displaying the additional screen near the display screen on the first device.

A system for generating graphical user interfaces. The system may include processors and storage devices storing instructions. The instructions may configure the one or more processors to perform operations including identifying a plurality of attributes from an image captured with a client device, identifying a plurality of first results based on the attributes, generating a first graphical user interface for display in the client device. The first graphical user interface may include a plurality of result icons corresponding to a subset of the first results having confidence scores above a threshold, a plurality of filter icons displaying options, and a search button. The operations may also include receiving a selection of at least one of the result icons or at least one of the filter icons, performing a search, based on the selection, and generating a second graphical user interface.

Methods, systems, and non-transitory computer readable medium are described for automated image measurement for process development and optimization. A method includes receiving an image of a product associated with a manufacturing process; determining, using a trained machine learning model, an image classification for the image; selecting, based on the image classification, one or more image processing algorithms for the image; pre-processing the image based on at least one of the one or more image processing algorithms to generate an enhanced image; measuring, using a first image processing algorithm of the one or more image processing algorithms, one or more attributes of the enhanced image to determine image measurements; and reporting the image measurements. The manufacturing parameters of the manufacturing process are to be updated based on the image measurements.

Embodiments provide systems, methods, and non-transitory computer storage media for providing search result images based on associations of keywords and depth-levels of an image. In embodiments, depth-levels of an image are identified using depth-map information of the image to identify depth-segments of the image. The depth-segments are analyzed to determine keywords associated with each depth-segment based on objects, features, or content in each depth-segment. An image depth-level data structure is generated by matching keywords generated for the entire image with the keywords at each depth-level and assigning the depth-level to the keyword in the image depth-level data structure for the entire image. The image depth-level data structure may be queried for images that contain keywords and depth-level information that match the keywords and depth-level information specified in a search query.

An information processing device performs processing on document image data. The document image data includes first image data of a plurality of images and second image data. The first image data undergoes character recognition processing. The second image data does not undergo character recognition processing. The information processing device includes a detecting section, an extracting section, and a processing section. The detecting section detects the first image data from the document image data. The extracting section extracts the first image data from the document image data. The processing section generates composite image data by compositing the images of the first image data and performs character recognition on the composite image data.

A method includes providing attributes of a manufacturing process and an image of a product associated with the manufacturing process to a trained machine learning model. The method further includes obtaining, from the trained machine learning model, predictive data. The method further includes determining, based on the predictive data, image measurements of the image of the product associated with the manufacturing process. Manufacturing parameters of the manufacturing process are to be updated based on the image measurements.