A computing system receives, from a client device, an image of a content item uploaded by a user of the client devices. The computing system divides the image into one or more overlapping patches. The computing system identifies, via a first machine learning model, one or more distortions present in the image based on the image and the one or more overlapping patches. The computing system determines that the image meets a threshold level of quality. Responsive to the determining, the computing system corrects, via a second machine learning model, the one or more distortions present in the image based on the image and the one or more overlapping patches. Each patch of the one or more overlapping patches are corrected. The computing system reconstructs the image of the content item based on the one or more corrected overlapping patches.

An image processing device 100 of the present invention includes an image generation means 121 for generating a difference image representing a difference between an object image that is an image including an area from which a mobile body is to be detected and a corresponding image that is another image including an area corresponding to the area of the object image, and a detection means 122 for detecting the mobile body from the object image on the basis of the object image, the corresponding image, and the difference image.

A method and apparatus for object tracking are provided, where the object tracking method includes determining box information of candidate boxes in a current image frame and similarity scores of the candidate boxes based on including a search region of the current image frame with a template image corresponding to a target object, adjusting the similarity scores of the candidate boxes using a distractor map including distractor information of a previous image frame, determining a target box corresponding to the target object and a distractor box corresponding to a distractor of the target object from the candidate boxes based on the adjusted similarity scores, and updating the distractor map based on distractor information of the current image frame according to the distractor box.

Disclosed frameworks for generating an image including a salient object and a staged background include extracting a salient object from a source image and applying a generative model to the salient object to generate the image. According to some embodiments, extracting a salient object from a source image involves using salient object detection method to identify the relevant portions of the source image corresponding to the salient object. In some embodiments, the generative model is a generative adversarial network trained using a domain relevant dataset.

The present disclosure is related to systems and methods for image processing. The method includes obtaining an original image. The original image includes at least one blood vessel region and at least one scalp region. The method includes determining an intermediate image by removing the at least one scalp region from the original image. The method includes generating at least one target image by performing a maximum intensity projection operation on the intermediate image. The at least one target image represents the at least one blood vessel region in the original image.

A method for creating a composite image includes receiving a group of images indexed according to a time-consecutive capture sequence. Each image is evaluated for inclusion in the composite image, and the evaluation of each image entails determining whether a spatial footprint of the image is entirely internal to a polygon formed based on a union of spatial footprints corresponding to images positioned on a same side of the image within the time-consecutive capture sequence. If the spatial footprint of the image is not entirely internal to the polygon, an identifier for the image is added to a composite image array. Otherwise, the identifier for the image is excluded from the composite image array. After all images have been evaluated, the composite image is created by stitching together images with respective identifiers included in the composite image array.

A fingerprint image processing device includes a memory, and a processor coupled to the memory. The processor performs operations. The operations include reading a tenprint card image which includes a plurality of fingerprint patterns and at least one ruled line to separate one fingerprint imprint area from another fingerprint imprint area, and extracting from the tenprint card image a fingerprint image which includes at least one of the fingerprint patterns, apart of a fingerprint imprint area, and a part of a next fingerprint imprint area.

The invention relates to a method for determining the performance of a welding method carried out on a metal workpiece, in particular an electric arc welding or laser welding method, with the following steps: introducing one or more extracts of the initial image each having at least one presumed projection, as input to at least one neural network, in particular a convolutional neural network, so as to classify the presumed projections as confirmed or unconfirmed projections, carrying out a second digital processing operation on the initial image comprising the previously classified projections so as to determine at least one parameter representative of the quantity of confirmed projections chosen from the surface of one or more projections.

Disclosed is a method for recognizing a marine object based on hyperspectral data including collecting target hyperspectral data; preprocessing the target hyperspectral data; and detecting and identifying an object included in the target hyperspectral data based on a marine object detection and identification model, trained through learning of the detection and identification of the marine object. According to the present invention, the preprocessing and processing of the hyperspectral data collected in real time according to a communication state may be performed in the sky or on the ground.

A gas detection device that gives a notification of a detected gas on the basis of a captured image obtained by capturing an image of a monitoring target, the gas detection device including: a gas detection unit that detects gas on the basis of the captured image and gives a notification of the detected gas; an input unit that receives input information from a user; a mask candidate region extraction unit that extracts a mask candidate region that is a candidate region of a mask region for which a notification of gas detection is suppressed; and a mask generation unit that generates mask data indicating the mask region, in which the gas detection unit gives a notification of a gas detected outside the mask region, and the mask generation unit generates, as the mask data, a region in which first mask candidate region information input from the input unit matches second mask candidate region information extracted by the mask candidate region extraction unit.