The present disclosure provides a method, apparatus and system for identifying target objects. The method includes: clipping out a target image from an acquired image, wherein the target image involves a plurality of target objects to be identified that are stacked; adjusting a height of the target image to a preset height, wherein a height direction of the target image corresponds to a direction in which the plurality of target objects are stacked; extracting a feature map of the adjusted target image; segmenting the feature map in a dimension corresponding to the height direction of the target image to obtain a preset number of segment features; and identifying the target objects based on each of the preset number of segment features.

Provided is an image splicing method, including the following. A to-be-predicted image is divided into first and second cropped images. An overlap region exists between the first and second cropped images. The overlap region is divided into first and second sub-overlap regions. A first image region that does not include the second sub-overlap region is found in the first cropped image, and a second image region that does not include the first sub-overlap region is found in the second cropped image. First and second prediction result images respectively corresponding to the first and second cropped images are generated. A first prediction image region corresponding to the first image region is found in the first prediction result image, and a second prediction image region corresponding to the second image region is found in the second prediction result image. The first and second prediction image regions are spliced into a spliced image.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for production line conformance monitoring. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform production line conformance monitoring by utilizing categorical validation machine learning models that are generated using a plurality of training production line images associated with a related category subset of a plurality of validation categories for a target validation category.

A method and apparatus to automatically calibrate one or more attributes of a gaming system. For instance, the gaming system determines, in response to analysis by a processor of image data via a machine-learning model, an orientation of an affixed (e.g., printed) fiducial marker positioned in a known location on a planar playing surface of a gaming table. The system also transforms, in response to determining the orientation, first geometric data associated with an object on the planar playing surface to isomorphically equivalent second geometric data. The system also digitally illustrates, via an augmented reality overlay of the image data using the isomorphically equivalent second geometric data, a graphical representation of the object positioned relative to the fiducial marker on the planar playing surface.

A head-counter device (100) comprising a digital camera (1) adapted to provide a first digital image (IM1) representative of a counting zone (STR) of persons, the first image defining a first horizontal dimension (N) and a first vertical dimension (M), and a cropping module (7) configured for: analyzing the first image (IM1) and identifying a noise area (PCR) according to at least one of the following features: pixel light intensity, pixel color and/or presence of predefined patterns, cropping the noise area (PCR) from the first image (IM1) to obtain a second image (IM2) without the noise area, the noise area (PCR) being a peripheral portion of the first image having said first horizontal dimension and having a second vertical dimension (M−DSK+SM) shorter than the first vertical dimension.

Embodiments of the present invention are directed to facilitating region of interest preservation. In accordance with some embodiments of the present invention, a region of interest preservation score using adaptive margins is determined. The region of interest preservation score indicates an extent to which at least one region of interest is preserved in a candidate image crop associated with an image. A region of interest positioning score is determined that indicates an extent to which a position of the at least one region of interest is preserved in the candidate image crop associated with the image. The region of interest preservation score and/or the preserving score are used to select a set of one or more candidate image crops as image crop suggestions.

Conventional methods for developing responsive application screens or UI screens, as per the desired wireframes is a time consuming and erroneous. The embodiments herein provide a method and system for automatically transforming wireframe screens to responsive application screens using a User Interface (UI) editor. The UI editor supported by a model editor enables user to select the controls that are present in the wireframe with its text, text properties, layout, color, background, borders and many more properties which are related to its visual appearance. Further, corresponding specification for the user selected requirements of the wireframe is generated automatically. The user can edit the specifications, for any changes required. Further, the UI editor converts the specifications to a technological independent model, which can be imported into UI models and followed by code conversion to the required technology stack. User can modify the imported specifications before going to code conversion.

An image segmentation system to perform operations that include causing display of an image within a graphical user interface of a client device, receive a set of user inputs that identify portions of a background and foreground of the image, identify a boundary of an object depicted within the image based on the set of user inputs, crop the object from the image based on the boundary, and generate a media item based on the cropped object, wherein properties of the media object, such as a size and a shape, are based on the boundary of the object.

Examples of the present invention provides a method and system for inspection of heat recovery steam generator (HRSG) equipment to identify defects and damages using computer vision and deep learning techniques. The method comprising capturing one or more input frames by one or more input devices, classifying the one or more input frames by a scenario classifier to identify a scenario type based on a first modelled data prepared by training one or more deep neural networks (DNN), selecting at least one damage detector based on the identified scenario type, identifying one or more damage types by the at least one damage detector based on second modelled data prepared by training the one or more DNN and displaying one or more output frame indicating the identified one or more damage types of the HRSG equipment.

Use of improved image acquisition for a medical dose preparation system. The medical dose preparation system may include a work station for capturing medical dose preparation images (e.g., to document preparation of a mediation dose). The medical dose preparation image may be captured by a video data stream processor capable of performing an auto cropping technique on a video data stream received from an image device.