An information processing apparatus (2000) acquires a shelf rack image (12) in which a product shelf rack on which a product is displayed is imaged. The information processing apparatus (2000) performs image analysis on the shelf rack image (12), and generates information (actual display information) relevant to a display situation of the product on a product shelf rack (20). The information processing apparatus (2000) acquires reference display information representing a reference for display of the product on the product shelf rack (20). The information processing apparatus (2000) compares the actual display information generated by performing the image analysis on the shelf rack image (12) with the acquired reference display information, and generates comparison information representing a result.

A method performed by a mobile device for handling identification of equipment. The mobile device records an image, in a recording direction at a first location, of the equipment. Upon recording the image, the mobile device further obtains one or more radiation indications for determining a direction of radiation from the equipment; and provides the obtained one or more radiation indications associated with the recorded image, to an internal identifying process at the mobile device and/or a network node for identifying the equipment.

Provided is an imaging device including: an imaging unit (130) that generates a one frame image by sequentially receiving each reflected light reflected by a subject by intermittently and sequentially irradiating the subject with each irradiation light having a different wavelength according to a position of the moving subject, temporarily and sequentially holding signal information based on the reflected light of each wavelength, and collectively reading the held signal information; and a combining unit (140) that generates a combined image by cutting a subject image corresponding to the reflected light of each wavelength from the one frame image and superimposing a plurality of the cut subject images.

A first imaging unit 71 generates a first image a first image by taking an object to be inspected. A guide display unit 72 determines the object to be inspected from the first image by using a model for determining an object to be inspected from an image, and displays an illustration representing the object to be inspected as a guide. A second imaging unit 73 generates a second image by superimposing on the guide, and taking the object to be inspected with a recognizable marker regardless of color of an appearance of an object to be inspected, attached in a vicinity of a defect. A defect position determination unit 74 determines a position of the defect included in the object to be inspected based on a positional relationship between the illustration and the marker included in the second image. An information collecting unit 75 collects defect information associated with a type of the object to be inspected and the position of the defect.

Each of a plurality of co-located inspection camera modules captures raw images of objects passing in front of the co-located inspection camera modules which form part of a quality assurance inspection system. The inspection camera modules have either a different image sensor or lens focal properties and generate different feeds of raw images. The co-located inspection camera modules can reside within a single standalone module and be selectively switched amongst to activate the corresponding feed of raw images. The activated feed of raw images is provided to a consuming application or process for quality assurance analysis.

Data is received that includes a feed of images of a plurality of objects passing in front of an inspection camera module forming part of a quality assurance inspection system. Thereafter, it is detected whether there is an object within each image. Based on this detection, images in which each object is detected that meet predefined object representation parameters are identified (on an object-by-object basis, etc.). The identified images are provided to a consuming application or process for quality assurance analysis. Related apparatus, systems, techniques and articles are also described.

Hybrid deep learning systems and methods allow for detecting anomalies in objects, such as electrical printed circuit board (PCB) components, based on image data. In one or more embodiments, a hybrid deep learning model comprises a Graph Attention Network (GAT) that uses spatial properties of the PCB components to extract latent semantic information and generate an output set of hidden representations. The GAT treats each of the electrical components as a node and each connection between them as edges in a graph. The hybrid system further comprises a Convolutional Neural Network (CNN) that uses pixel data to obtain its own output set of hidden representations. The hybrid deep learning model concatenates both sets to detect anomalies that may be present on the PCB.

Provided are a damage diagram creation method, a damage diagram creation device, a damage diagram creation system, and a recording medium capable of detecting damage with high accuracy based on a plurality of images acquired by subjecting a subject to split imaging. In a damage diagram creation method, damage of a subject is detected from each image (each image in a state of being not composed) constituting a plurality of images (a plurality of images acquired by subjecting the subject to split imaging), and thus, damage detection performance is not deteriorated due to deterioration of image quality in an overlapping area. Therefore, it is possible to detect damage with high accuracy based on a plurality of images acquired by subjecting the subject to split imaging. Detection results for the respective images can be composed using a composition parameter calculated based on correspondence points between the images.

There is a need for more effective and efficient printed circuit board (PCB) design. This need can be addressed by, for example, solutions for performing automated PCB component estimation. In one example, a method includes identifying a plurality of initial component estimations for the PCB; performing a shadow detection segmentation using the plurality of initial component estimations, a non-direct-lighting image, and one or more direct-lighting images to generate a first set of detected PCB components; performing a super-pixel segmentation using the plurality of initial component estimations and the non-direct-lighting-image to generate a second set of detected PCB components; and generating a bill of materials for the PCB based at least in part on the first set of detected PCB components and the second set of detected PCB components.

Embodiments are described herein for an electronic device, system and method for remotely detecting whether or not the electronic device has a screen protector placed over its display screen. The electronic device is placed face-down on a flat, opaque surface, such that the display screen and front camera of the electronic device are also face-down and the electronic device takes a series of photos with the camera. The series of photos is analyzed to determine whether or not the screen protector is attached to the display screen of the electronic device, which can be communicated electronically.