A system, platform, computer program product, and/or method to analyze a computer-implemented video conference includes: a plurality of participant devices, and a central processing server. Each participant device is configured to form a video snippet for a time interval of the video conference having audio data and video data; generate a transformed video snippet by embedding extracted participant data and/or metadata into the video snippet; and send each transformed video snippet to the central processing server. The central processing server receives each transformed video snippet; performs analytics on each transformed video snippet; and transmits to at least one of the participant devices, results of the performed analytics. Participant devices can display one or more results of the performed analytics.

An apparatus (100) comprises at least one electronic processor (101, 113) programmed to: control an associated medical imaging device (120) to acquire an image (130); compute values of textural features (132) for the acquired image; generate a signature (140) from the computed values of the textural features; and at least one of: display the signature on a display device (105); and apply an artificial intelligence (AI) component (150) to the generated signature to output image artifact metrics (152) for a set of image artifacts and display an image quality assessment based on the image artifact metrics on the display device.

An apparatus (100) comprises at least one electronic processor (101, 113) programmed to: control an associated medical imaging device (120) to acquire an image (130); compute values of textural features (132) for the acquired image; generate a signature (140) from the computed values of the textural features; and at least one of: display the signature on a display device (105); and apply an artificial intelligence (AI) component (150) to the generated signature to output image artifact metrics (152) for a set of image artifacts and display an image quality assessment based on the image artifact metrics on the display device.

Disclosed herein is an apparatus for facilitating managing cultivation of crops based on monitoring the crops. Further, the apparatus comprises an apparatus body, cameras, light sensors, a processing unit, and a communication interface. Further, the cameras generate a measurement of a crop and a field portion. Further, the light sensors generate an environment measurement of an environment of the apparatus. Further, the processing unit analyzes the environment measurement, determines a factor affecting the measurement, and generates a calibrating factor for the cameras. Further, the calibrating factor facilitates compensating the affecting of the factor in the measurement. Further, the cameras calibrate a camera parameter of the cameras based on the calibrating factor to generate the measurement. Further, the processing unit analyzes the measurement and generates a status of the crop. Further, the communication interface transmits the status to a device.

Disclosed herein is an apparatus for facilitating managing cultivation of crops based on monitoring the crops. Further, the apparatus comprises an apparatus body, cameras, light sensors, a processing unit, and a communication interface. Further, the cameras generate a measurement of a crop and a field portion. Further, the light sensors generate an environment measurement of an environment of the apparatus. Further, the processing unit analyzes the environment measurement, determines a factor affecting the measurement, and generates a calibrating factor for the cameras. Further, the calibrating factor facilitates compensating the affecting of the factor in the measurement. Further, the cameras calibrate a camera parameter of the cameras based on the calibrating factor to generate the measurement. Further, the processing unit analyzes the measurement and generates a status of the crop. Further, the communication interface transmits the status to a device.

Augmented reality features are selected for presentation to a display of an electronic eyewear device by using a camera of the electronic eyewear device to capture a scan image and processing the scan image to extract contextual signals. Simultaneously, voice data from the user is captured by a microphone of the electronic eyewear device and voice-to-text conversion of the captured voice data is performed to identify keywords in the voice data. The extracted contextual signals and the identified keywords are then used to select at least one augmented reality feature that matches the extracted contextual signals and the identified keywords, and the selected augmented reality feature is presented to the display for user selection. The contextual information thus refines the search results to provide the augmented reality feature best suited for the context of the scan image captured by the electronic eyewear device.

An ultrasound diagnostic apparatus 1 includes an image acquisition unit 3 that generates an ultrasound image, an image recognition unit 9 that performs image recognition for the ultrasound image to calculate recognition scores, an index value calculation unit 10 that calculates index values of a plurality of parts on the basis of the recognition scores calculated for a predetermined number of ultrasound images, an order decision unit 11 that decides a determination order in which part determination is performed for the plurality of parts on the basis of the index values, and a part determination unit 12 that determines an imaging part of a subject on the basis of the recognition scores calculated according to the determination order.

An electronic apparatus and an operating method are provided. The electronic apparatus includes a storage, at least one sensor, and at least one processor configured to execute stored instructions to while the electronic apparatus is moving, capture a surrounding image by using the at least one sensor, when an unable-to-move situation occurs while the electronic apparatus is moving, generate context data including a surrounding image captured within a predetermined time from a time when the unable-to-move situation has occurred, store, in the storage, the generated context data corresponding to the unable-to-move situation having occurred, and learn the stored context data by using one or more data recognition models.

A computer-implemented method of searching for a region indicative of a pathology in an image of a portion of an eye acquired by an ocular imaging system, the method comprising: receiving image data defining the image; searching for the region in the image by processing the received image data using a learning algorithm; and in case a region in the image that is indicative of the pathology is found: determining a location of the region in the image; generating an instruction for an eye measurement apparatus to perform a measurement on the portion of the eye to generate measurement data, using a reference point based on the determined location for setting a location of the measurement on the portion of the eye; and receiving the measurement data from the eye measurement apparatus.

A semantic similarity fingerprint is generated for an image by inferring a plurality of SKUs each at an associated first weight based upon analysis of the image using the machine learning models. The associated first weights for each of the classifications based upon each machine learning model is the semantic similarity fingerprint. The semantic similarity fingerprint may be compared to previously generated semantic similarity fingerprints. If a match is found with a semantic similarity fingerprint that has previously been identified as a particular.