An operating method with goods information is applicable to an electronic device. The operation method includes: obtaining image information associated with one or more goods objects on a target electronic shelf among a plurality of electronic shelves in a network; and performing first communicating with a server for controlling the electronic shelves in the network according to either or both of the image information and feature information associated with the one or more goods objects, wherein feature information is extracted from the image information and the first communicating includes wirelessly transmitting either or both of the image information and the feature information associated with the one or more goods objects to the server.

A method for quantifying disorder and extracting a corresponding numerical value of an order parameter from contrast analysis applied to optical images acquired of the solar photosphere. Temporal variation of the order parameter may be utilized to predict events such as solar flares, which have the ability to disrupt both communication systems and satellite orbits. The degree of order of the photosphere may be monitored to predict solar flares and other solar events. The method may utilize a spin-based (Ising/Potts) model of disorder.

An apparatus for analysing a head CT scan, where the head CT scan includes a plurality of cross-sections, includes a processor configured to select a first cross-section from among the plurality of cross-sections in the head CT scan; and analyse the first cross-section to determine whether the whole head CT scan was taken with contrast. The analyzing includes determining whether the first cross-section shows contrast, based on a presence of bright patches in the first cross-section, determining whether an amount of sulci in the first cross-section is below a threshold amount. In response to determining that the first cross-section shows contrast, determine that the whole head CT scan was taken with contrast, and in response to the determining that the first cross-section does not show contrast and that the amount of sulci is below the threshold amount determining that the whole head CT scan was taken without contrast.

An apparatus for image conversion includes a processor configured to classify a reference region representing a predetermined feature into a shadowed region and an unshadowed region, the reference region being in an aerial image represented in RGB color space; determine a tone correction factor so that a difference between an average luminance of the shadowed region and an average luminance of the unshadowed region in the aerial image represented in predetermined color space to which the color space of the aerial image is converted from RGB color space is less than a difference between an average luminance of the shadowed region and an average luminance of the unshadowed region represented in RGB color space; correct tones of the aerial image with the tone correction factor; and convert the color space of the aerial image from RGB color space to the predetermined color space to generate a color conversion image.

A system includes a camera defining an optical axis and a field of view defined about the optical axis. An illuminator is mounted offset from the optical axis and directed to illuminate at least a portion of the field of view, wherein the illuminator is operatively connected to the camera to provide illumination during an image capturing exposure of the camera. An image processer is operatively connected to the camera and includes machine readable instructions configured to receive image data representative of an image captured with the camera, perform facial detection to detect at least one face in the image, perform license plate detection to detect at least one license plate in the image, and to provide a vehicle overview image.

Aspects of the present disclosure relate to multi-spectrum visual object recognition. A first image corresponding to visible light and a second image corresponding to invisible light with respect to an object can be obtained. A first contour of the object can be identified based on the first image. A second contour of the object can be identified based on the second image. The first contour of the object and the second contour of the object can be integrated to generate a multi-spectrum contour of the object. The object can be recognized using the multi-spectrum contour of the object.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining the number of speakers in a video and a corresponding audio using visual context. In one aspect, a method includes detecting within the video multiple speakers, determining a bounding box for each detected speaker that includes the detected person and objects within a threshold distance of the detected person in an image frame, determining a unique descriptor for that person based in part on image information depicting the objects within the bounding box, determining a cardinality of unique speakers in the video, providing to the speaker diarization system the cardinality of unique speakers.

The recognition system for security check and control method thereof. The recognition system for security check is integrated with a reinforcement learning algorithm and an attention region proposal network. The recognition system for security check comprises the following modules: an object feature extraction module (1); a dangerous item region segmentation module (2); a preliminary classification module (3); a preliminary classification result determination module (4); and a fine-grained recognition module (5). In the invention, optimization of a dangerous item region segmentation module and provision of a fine-grained recognition module greatly improve accuracy and efficiency of security check, shorten the duration of security check, alleviate congestion, save labor, and reduce pressure on security check personnel.

Systems and methods are provided for recognizing various sample containers carried in a rack. The systems and methods are performed to identify sample containers in the rack and detect various characteristics associated with the containers and/or the rack, which are evaluated to determine the validity and/or types of the containers in the rack.

An autonomous vehicle uses machine learning based models such as neural networks to predict hidden context attributes associated with traffic entities. The hidden context represents behavior of the traffic entities in the traffic. The machine learning based model is configured to receive a video frame as input and output likelihoods of receiving user responses having particular ordinal values. The system uses a loss function based on cumulative histogram of user responses corresponding to various ordinal values. The system identifies user responses that are unlikely to be valid user responses to generate training data for training the machine learning mode. The system identifies invalid user responses based on response time of the user responses.