Texture extraction is disclosed.

A method is provided. The method includes: determining, by one or more computers, a name of a target region, wherein the name of the target region is determined based on geometry attribute information of the target region; and determining, by one or more computers, region attribute information of the target region based on the name of the target region

The inventive concept relate to a technology, which recognizes widely deformable markers with high accuracy in an end-to-end manner of message encoding and decoding, and which generates and recognizes deformable fiducial markers based on artificial intelligence, and includes generating, by a marker generator, a unique marker pattern as a fiducial marker in an input binary message, rendering, by an imaging simulator, an image by generating a training dataset of a realistic scene image with the generated fiducial marker, and training a marker detector with the rendered image.

Systems, methods, and instrumentalities are disclosed for computer vision-based surgical workflow recognition using natural language processing (NLP) techniques. Surgical video of surgical procedures may be processed and analyzed, for example, to achieve workflow recognition. Surgical phases may be determined based on the surgical video and segmented to generate an annotated video representation. The annotated video representation of the surgical video may provide information associated with the surgical procedure. For example, the annotated video representation may provide information on surgical phases, surgical events, surgical tool usage, and/or the like.

Systems, computer-readable media, methods, and approaches described herein may identify delivery and/or pickup of packages. For example, packages may be identified within the areas captured by images and/or video. Based on the identification of the packages, it may be determined whether the package was delivered or picked up. A notification may be initiated that indicates that a package has been delivered and/or picked up.

The present disclosure provides methods and an apparatus for imaging and analysing images of presumed protein deposits in the retina, retinal tissue or retinal structures and discloses methods differentiating or classifying these deposits and other optical signals from retinal structures into 1) whether they contain or do not contain classes, of proteins or protein deposits called amyloids or other proteins and/or protein deposits related to neurodegenerative eye and brain disease(s); 2) which type(s) of amyloid or other proteins or protein deposits they contain, as well as 3) whether the form and/or properties of the deposit are associated with a class of diseases or with one or another specific condition(s) (or disease(s)); whether or not this is a disease or class of disease associated with the retina or more generally with the nervous system, including the brain or 4) classified as associated with one or another level of severity of condition(s), or disease(s).

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.

An inspection method is provided for checking the integrity of a combination of a security marking and an identification label, the security marking including at least one contrast field having a comparatively high reflectivity in a first and a second wavelength range, and a security field, having different reflection properties in the first wavelength range compared to the second wavelength range, and the identification label having at least one light background around mark components printed with dark color. The inspection method may include capturing possibly averaged gray values of the contrast field and the identification label background, comparing the gray values, and determining whether the gray value of the contrast field of the security marking deviates from the gray value of the background of the identification label by less than a predefined maximum amount.

An image processing method and apparatus are provided. The method includes: acquiring a target image. The target image is an image obtained by capturing a dynamic image displayed by a first device by means of a second device. The dynamic image is used for indicating configuration information of the first device. The first device has a first attitude. The methods further includes identifying a primary graphic body of a first graphic and a secondary graphic body of the first graphic. The first graphic is a graphic in the target image. The method also includes determining a first character corresponding to the first graphic and identifying the first device based on the first character.

This disclose relates to a data processing method and apparatus. The method includes: acquiring a first prediction region in a target image, the first prediction region being a prediction region corresponding to a maximum prediction category probability in N prediction regions in the target image, a prediction category probability being a probability that an object in a prediction region belongs to a prediction object category; determining a coverage region jointly covered by a second prediction region and the first prediction region; the second prediction region being a prediction region other than the first prediction region in the N prediction regions; and determining a target prediction region in the prediction regions based on an area of the coverage region and a similarity associated with the second prediction region, the similarity being for indicating a similarity between an object in the second prediction region and an object in the first prediction region.