An information processing device (200A, 200B, and 200C) according to the present disclosure includes a control unit (220, 220B, and 220C). The control unit (220, 220B, and 220C) acquires a captured image of a target imaged by a sensor. The captured image is an image obtained from reflected light of light emitted to the target from a plurality of light sources arranged at different positions, respectively. The control unit (220, 220B, and 220C) extracts a flat region from the captured image based on a luminance value of the captured image. The control unit (220, 220B, and 220C) calculates shape information regarding a shape of a surface of the target based on information regarding the sensor and the flat region of the captured image.

An information processing system comprising a portable information device, a relay apparatus that connects the portable information device to a network, and a server. The server includes a library in which, for each relay apparatus, an area where the relay apparatus is present is registered in association with relay apparatus identification information. The portable information device obtains connection authentication from the relay apparatus installed in an area where the own device is present, and when the authentication is successful, the relay apparatus that provides the authentication transmits the relay apparatus identification information to the server.

An information processing system comprising a portable information device, a relay apparatus that connects the portable information device to a network, and a server. The server includes a library in which, for each relay apparatus, an area where the relay apparatus is present is registered in association with relay apparatus identification information. The portable information device obtains connection authentication from the relay apparatus installed in an area where the own device is present, and when the authentication is successful, the relay apparatus that provides the authentication transmits the relay apparatus identification information to the server.

A receiver receives a radio wave transmitted to a target and scattered by the target to acquire a signal. An imaging unit generates a 3D complex image of the target based on the signal. A value extraction unit extracts intensity information and phase in including an intensity matrix and a phase matrix, the extracted intensity information constituting the intensity matrix and the extracted phases information constituting the phase matrix. A subset selection unit selects a subset from the value set. A transformation unit changes a representation of the subset to generate a 2D real image. A detection unit detects whether there is an undesired object on the target based on the 2D real image.

A receiver receives a radio wave transmitted to a target and scattered by the target to acquire a signal. An imaging unit generates a 3D complex image of the target based on the signal. A value extraction unit extracts intensity information and phase in including an intensity matrix and a phase matrix, the extracted intensity information constituting the intensity matrix and the extracted phases information constituting the phase matrix. A subset selection unit selects a subset from the value set. A transformation unit changes a representation of the subset to generate a 2D real image. A detection unit detects whether there is an undesired object on the target based on the 2D real image.

The driving assistance system includes an imaging device capable of capturing a first monochrome image in a vehicle traveling direction, a first neural network for segmentation processing, a second neural network for depth estimation processing, a determination portion determining a center of a portion cut off from the first monochrome image on the basis of the segmentation processing and the depth estimation processing, a third neural network for colorization processing of only a second cut-off monochrome image, and a display device for enlargement of the second monochrome image subjected to the colorization processing.

The driving assistance system includes an imaging device capable of capturing a first monochrome image in a vehicle traveling direction, a first neural network for segmentation processing, a second neural network for depth estimation processing, a determination portion determining a center of a portion cut off from the first monochrome image on the basis of the segmentation processing and the depth estimation processing, a third neural network for colorization processing of only a second cut-off monochrome image, and a display device for enlargement of the second monochrome image subjected to the colorization processing.

A handwash monitoring system includes: an imaging device; and a processor. The processor detects a first candidate abnormality existing in a hand of a user from a first image captured by the imaging device before handwashing, and detects a second candidate abnormality existing in the hand of the user from a second image captured by the imaging device after the handwashing. The processor determines a type of an abnormality on the hand of the user based on a difference between a shape of the first candidate abnormality and a shape of the second candidate abnormality wherein the first candidate abnormality and the second candidate abnormality are detected from an identical region.

A handwash monitoring system includes: an imaging device; and a processor. The processor detects a first candidate abnormality existing in a hand of a user from a first image captured by the imaging device before handwashing, and detects a second candidate abnormality existing in the hand of the user from a second image captured by the imaging device after the handwashing. The processor determines a type of an abnormality on the hand of the user based on a difference between a shape of the first candidate abnormality and a shape of the second candidate abnormality wherein the first candidate abnormality and the second candidate abnormality are detected from an identical region.

A system for training a machine learning framework to estimate depths of objects captured in 2-D images includes a first trained machine learning network and a second untrained or minimally trained machine learning framework. The first trained machine learning network is configured to analyze 2-D images of target spaces including target objects and to provide output indicative of 3-D positions of the target objects in the target spaces. The second machine learning network can be configured to provide an output responsive to receiving a 2-D input image. A comparator receives the outputs from the first and second machine learning networks based on a particular 2-D image. The comparator compares the output of the first trained machine learning network with the output of the second machine learning network. A feedback mechanism is operative to alter the second machine learning network based at least in part on the output of the comparator.