An information processing system includes an image processing apparatus capable of obtaining image data and a server-for-service connected with the image processing apparatus through a network. The server-for-service holds profile information, which defines processing executed on the image data uploaded from the image processing apparatus, in association with authorization information on a user. Thereafter, the server-for-service executes the processing in accordance with the profile information on the image data in response to a request from a terminal device or the image processing apparatus.

An information processing system includes an image processing apparatus capable of obtaining image data and a server-for-service connected with the image processing apparatus through a network. The server-for-service holds profile information, which defines processing executed on the image data uploaded from the image processing apparatus, in association with authorization information on a user. Thereafter, the server-for-service executes the processing in accordance with the profile information on the image data in response to a request from a terminal device or the image processing apparatus.

The information processing apparatus (2000) includes a feature point detection unit (2020), a determination unit (2040), an extraction unit (2060), and a comparison unit (2080). A feature point detection unit (2020) detects a plurality of feature points from the query image. The determination unit (2040) determines, for each feature point, one or more object images estimated to include the feature point. The extraction unit (2060) extracts an object region estimated to include the object in the query image in association with the object image of the object estimated to be included in the object region, on the basis of the result of the determination. The comparison unit (2080) cross-checks the object region with the object image associated with the object region and determines an object included in the object region.

The information processing apparatus (2000) includes a feature point detection unit (2020), a determination unit (2040), an extraction unit (2060), and a comparison unit (2080). A feature point detection unit (2020) detects a plurality of feature points from the query image. The determination unit (2040) determines, for each feature point, one or more object images estimated to include the feature point. The extraction unit (2060) extracts an object region estimated to include the object in the query image in association with the object image of the object estimated to be included in the object region, on the basis of the result of the determination. The comparison unit (2080) cross-checks the object region with the object image associated with the object region and determines an object included in the object region.

A dental medical record device and a dental medical record method, in which: an image, such as a panoramic photo, a scan image, and a camera image of a patient's oral cavity, is received via artificial intelligence, and charting is performed using the artificial intelligence; and medical records for a treatment area can be read in association with a chart by clicking the treatment area in the image.

A dental medical record device and a dental medical record method, in which: an image, such as a panoramic photo, a scan image, and a camera image of a patient's oral cavity, is received via artificial intelligence, and charting is performed using the artificial intelligence; and medical records for a treatment area can be read in association with a chart by clicking the treatment area in the image.

A behavior recognition method and system, including: dividing video data into a plurality of video clips, performing frame extraction processing on each video clip to obtain frame images, and performing optical flow extraction on the frame images to obtain optical flow images; performing feature extraction on the frame images and the optical flow images to obtain feature maps of the frame images and the optical flow images; performing spatio-temporal convolution processing on the feature maps of the frame images and the optical flow images, and determining a spatial prediction result and a temporal prediction result; fusing the spatial prediction results of all the video clips to obtain a spatial fusion result, and fusing the temporal prediction results of all the video clips to obtain a temporal fusion result; and performing two-stream fusion on the spatial fusion result and the temporal fusion result to obtain a behavior recognition result.

Systems, methods, and non-transitory media are provided for adjusting camera settings based on event data. An example method can include obtaining, via an image capture device of a mobile device, an image depicting at least a portion of an environment; determining a match between one or more visual features extracted from the image and one or more visual features associated with a keyframe; and based on the match, adjusting one or more settings of the image capture device.

A simultaneous localization and mapping-based electronic device includes: a data acquisition device configured to acquire external data; a memory; and a processor configured to be operatively connected to the data acquisition device and the memory, wherein the processor is further configured to extract features of surrounding objects from the acquired external data, calculate a score of a registration error of the extracted features when the number of the extracted features is greater than a set number stored in the memory, and select the set number of features from the among the extracted features, based on the calculated score.

A simultaneous localization and mapping-based electronic device includes: a data acquisition device configured to acquire external data; a memory; and a processor configured to be operatively connected to the data acquisition device and the memory, wherein the processor is further configured to extract features of surrounding objects from the acquired external data, calculate a score of a registration error of the extracted features when the number of the extracted features is greater than a set number stored in the memory, and select the set number of features from the among the extracted features, based on the calculated score.