The disclosure pertains to techniques for image processing. One such technique comprises a method for image selection, comprising obtaining a sequence of images, detecting one or more objects in one or more images of the sequence of images, determining a location for each detected object in the one or more images, determining a trajectory for each detected object based on a determined location for each respective detected object in two or more images of the sequence of images, determining a trajectory waypoint score for the trajectory of each detected object, determining a set of selected images based on an aggregation of trajectory waypoint scores for each detected object in each respective image, and outputting the set of selected images for presentation.

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 electronic device for smoke estimation is provided. The electronic device receives a first image of a plurality of images of a physical space. The electronic device detects smoke in the physical space based on an application of a trained neural network model on the received first image. The electronic device generates a heatmap of the physical space based on the detected smoke in the physical space, and further based on an output of the trained neural network model corresponding to the detection of the smoke. The electronic device estimates a level of the smoke in the physical space based on a normalization of the generated heatmap.

Provided is a method for performing accurate object recognition in a stable manner in consideration of changes in a shooting environment. In such a method, a camera captures an image of a shooting location where an object is to be placed and an object included in an image of the shooting location is recognized utilizing a machine learning model for object recognition. The method further involves: determining necessity of an update operation on the machine learning model for object recognition at a predetermined time; when the update operation is necessary, causing the camera to capture an image of the shooting location where no object is placed to thereby re-acquire a background image for training; and causing the machine learning model to be trained using a composite image of a backgroundless object image and the re-acquired background image for training as training data.

A surgical system includes a detector that includes an array of pixels configured to detect light reflected by a surgical device and generate a first signal. The first signal includes a first dataset representative of a visible image of the surgical device. The surgical system also includes a processor configured to receive the first signal and a second signal representative of one or more operating parameters of the surgical device. The processor is also configured to generate a modified image of the surgical device that includes information related to one or more operating parameters.

One or more example embodiments provides a system and a method for differentiating a tissue of interest from another part of a medical scanner image, in particular pectoral muscle tissue from breast tissue in an X-ray mammography image. The method comprises providing a medical scanner image; inputting input data into a trained artificial neural network, the input data being based on the provided medical scanner image; generating, by the trained artificial neural network, output data based on the input data, the output data indicating a one-dimensional borderline between at least a part of the tissue of interest and the at least one other part of the medical scanner image; and outputting an output signal comprising or based on the generated output data.

A method for processing an image is presented. The method locates a subject and an object of a subject-object interaction in the image. The method determines relative weights of the subject, the object, and a context region for classification. The method further classifies the subject-object interaction based on a classification of a weighted representation of the subject, a weighted representation of the object, and a weighted representation of the context region.

In accordance with various embodiments, a method (100) for the computer-aided recognition of a transport container (402) being empty can comprise: determining (101) one or more than one segment of image data of the transport container (402) using depth information assigned to the image data; assigning (103) the image data to one from a plurality of classes, of which a first class represents the transport container (402) being empty, and a second class represents the transport container (402) being non-empty, wherein the one or more than one segment is not taken into account in the assigning; outputting (105) a signal which represents the filtered image data being assigned to the second class.

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.

System, processes and devices for monitoring betting activities using bet recognition devices and a server. Each bet recognition device has an imaging component for capturing image data for a gaming table surface. The bet recognition device receives calibration data for calibrating the bet recognition device. A server processor coupled to a data store processes the image data received from the bet recognition devices over the network to detect, for each betting area, a number of chips and a final bet value for the chips.