A method for analyzing performance of an imaging device including a scanner with a phantom includes receiving image data related to a scanning, by the scanner, of a first part of the phantom including at least part of a first test component. The method also includes obtaining at least one positioning parameter indicative of a positioning manner of the phantom during the scanning. The method further includes generating a first test image based on the received image data and determining a first region of interest (ROI) related to the first test component in the first test image based on the at least one positioning parameter.

An augmented reality (AR) system for aviation generates aviation objects over an image during flight. The AR system may determine a location and an orientation of the image capture device based on the location information and orientation information. The AR system may determine a field of view of the image capture device and identify aviation objects within the field of view. The AR system can then generate graphical user interface elements for identified aviation objects to be overlayed on the image data.

A method for classifying an identification tag on a sample tube containing a sample to be processed in an automated laboratory system having a plurality of laboratory devices, the method comprising providing a sample tube having an identification tag and containing a sample to be analyzed. Each of the plurality of laboratory devices is assigned a tag reader device configured to read the identification tag for recognizing identification tag information. Further, an automated laboratory system for processing a sample tube containing a sample for at least one of pre-analytics and sample analysis is provided.

A system for creating a composite georeferenced, geo-oriented geospatial/realtime image by combining prestored geographic data with realtime video streams and state data characterizing the source of the realtime video stream and visually displaying the composite image under a user's realtime control.

A computer implemented method includes supplying stimuli to an organism, measuring brain activity of the organism responsive to the stimuli and producing a brain feature activity map characterizing the brain activity. The operations of supplying, measuring and producing are repeated for different stimuli to form a brain feature activity map database. New stimuli are received. New stimuli features are mapped to a projected brain activity map. The projected brain activity map is compared to the brain feature activity map database to identify similarities and dissimilarities between the projected brain activity map and entries in the brain feature activity map database to designate a match. The new stimuli are characterized based upon the match.

Methods and systems of generating an image gradient are presented. In one example, image data for generating a two-dimensional digital image is provided to a hardware graphics processor. The image data may include a single pixel along a first direction of the digital image and multiple pixels along a second direction of the digital image. The pixels along the second direction of the digital image may include an image gradient. A one-dimensional stretching operation of the hardware graphics processor is initiated based on the image data to generate the digital image. In response to the stretching operation, the digital image may include multiple pixels along the first direction for each of the single pixels, wherein each of the multiple pixels along the first direction for a corresponding pixel of the single pixels comprises the corresponding pixel.

Provided is a monitoring system, comprising a imaging-side device, and a matching-side device which receives image data from the imaging-side device and matches the image data. The imaging-side device wirelessly transmits, to the matching-side device, feature value data which denotes feature of an image subject. Upon receiving the feature value data, and on the basis of the result of a comparison between the feature value data and feature value reference data, the matching-side device wirelessly transmits image data request information to the imaging-side device. Upon receiving the image data request information, the imaging-side device wirelessly transmits image data to the matching-side device.

A rapid and automatic virus imaging and analysis system includes (i) electron optical sub-systems (EOSs), each of which has a large field of view (FOV) and is capable of instant magnification switching for rapidly scanning a virus sample; (ii) sample management sub-systems (SMSs), each of which automatically loads virus samples into one of the EOSs for virus sample scanning and then unloads the virus samples from the EOS after the virus sample scanning is completed; (iii) virus detection and classification sub-systems (VDCSs), each of which automatically detects and classifies a virus based on images from the EOS virus sample scanning; and (iv) a cloud-based collaboration sub-system for analyzing the virus sample scanning images, storing images from the EOS virus sample scanning, and storing and analyzing machine data associated with the EOSs, the SMSs, and the VDCSs.

Enables integration of sensor data with other information on servers such as social media sites to detect, confirm and/or publish events. Sensors may measure values such as motion, temperature, humidity, wind, pressure, elevation, light, sound, or heart rate, etc. Sensor data and event tags may be utilized to curate text, images, video, sound and post the results to social networks, for example in a dedicated feed. Event tags generated by the system may represent for example activity types, players, performance levels, or scoring results. The system may analyze social media postings to confirm or augment event tags. Users may filter and analyze saved events based on the assigned tags. The system may create highlight and fail reels filtered by metrics and by tags. Recommendations may be provided to a user based on analysis of sensor data and other information; recommendations may include for example recommended friends, purchases, or activities.

This application provides an image defect detection method. The method includes obtaining a first image and a second image of a flawless image. A third image is obtained from the second image and the first image, and a fourth image is obtained according to the second image and an image to be detected. A fifth image is obtained based on the third image and the second image. A sixth image is obtained based on the third image and the fourth image. A seventh image is obtained from the fifth image and the sixth image. A defect value of the fourth image is obtained according to the third image and the seventh image. A detection result of the fourth image is determined based on the defect value.