A medical system includes a light source configured to irradiate an imaging target with light having different wavelength zones in a first observation mode and a second observation mode, the imaging target being a part of a living body being operated, an imaging apparatus configured to capture reflected light from the imaging target irradiated with the light and output a captured image, a storage controller configured to perform control for causing a storage section to store a first captured image upon the first observation mode as a reference image, a generator configured to compare a second captured image upon the second observation mode with the reference image to generate a parameter used for approximating a color shade of the second captured image to a color shade of the reference image, a color conversion processor configured to perform color conversion processing on the second captured image on a basis of the parameter to output a color-converted image, and a display controller configured to perform control for causing a display section to display the color-converted image.

A method for determining a reference position, on one side of a flat piece, wherein: image data is acquired; a first reference pattern is detected based on the image data, and first position data is determined for the first reference pattern; based on the first position data, a region of interest is defined in the image data; in the region of interest, a second reference pattern (34) is detected and second position data is determined for the second reference pattern; based on the first position data and the second position data, the reference position is determined. In addition, the present disclosure relates to a system for determining a reference position including an image data acquisition unit and an evaluation unit.

A method for determining a reference position, on one side of a flat piece, wherein: image data is acquired; a first reference pattern is detected based on the image data, and first position data is determined for the first reference pattern; based on the first position data, a region of interest is defined in the image data; in the region of interest, a second reference pattern (34) is detected and second position data is determined for the second reference pattern; based on the first position data and the second position data, the reference position is determined. In addition, the present disclosure relates to a system for determining a reference position including an image data acquisition unit and an evaluation unit.

The invention is based, in part, on a system and method designed to be able to easily and automatically scale up to millions of cameras and users. To do this, this discourse teaches use of modern cloud computing technology, including automated service provisioning, automated virtual machine migration services, RESTful API, and various firewall traversing methods to facilitate the scaling process. Moreover, the system and method described herein teaches scalable cloud solutions providing for higher though-put camera provisioning and event recognition. The network may segregate the retrieval server from the storage server, and by doing so, minimizing the load on any one server and improving network efficiency and scalability

The invention is based, in part, on a system and method designed to be able to easily and automatically scale up to millions of cameras and users. To do this, this discourse teaches use of modern cloud computing technology, including automated service provisioning, automated virtual machine migration services, RESTful API, and various firewall traversing methods to facilitate the scaling process. Moreover, the system and method described herein teaches scalable cloud solutions providing for higher though-put camera provisioning and event recognition. The network may segregate the retrieval server from the storage server, and by doing so, minimizing the load on any one server and improving network efficiency and scalability

An image processing method and apparatus include: obtaining a plurality of input optical images and a plurality of output optical images for a near-eye display apparatus; training a neural network for modeling the waveguide optical system by using a training set including the obtained input and output optical images; and obtaining a uniformity correction image to be input to the trained neural network such that the trained neural network then outputs a target uniformity image having certain brightness uniformity.

An image processing method and apparatus include: obtaining a plurality of input optical images and a plurality of output optical images for a near-eye display apparatus; training a neural network for modeling the waveguide optical system by using a training set including the obtained input and output optical images; and obtaining a uniformity correction image to be input to the trained neural network such that the trained neural network then outputs a target uniformity image having certain brightness uniformity.

A system for monitoring a human operator of critical equipment comprises an imaging module; a biometric measurement module; a risk detection module; and a risk response module. The imaging module includes a multi-spectral light source configured to emit light in a first spectral wavelength range for illuminating at least a portion of the human operator; a camera configured to detect light received from the human operator in a second spectral wavelength range; an imaging data generator configured to generate image data based on the emitted light and detected light. The biometric measurement module is configured to receive the image data; and based on the image data, perform at least one biometric measurement on the human operator. The risk detection module is configured to establish a safety risk associated with the human operator; and the risk response module is configured to based on the safety risk generate a risk response.

A system for categorizing seeds of plants into hybrid and non-hybrid categories. Seeds sorted according to the disclosed system are also disclosed.

A method and system including: an aerial vehicle including: a first camera comprising a first sensor having at least red, green, and blue color channels, where the blue color channel is sensitive to near-infrared (NIR) wavelengths; a first optical filter disposed in front of the first sensor, wherein the first optical filter is configured to block wavelengths below green, between red and NIR, and longer wavelength NIR; a processor having addressable memory in communication with the first camera, where the processor is configured to: capture at least one image of vegetation from the first camera; provide red, green, and NIR color channels from the captured image from the first camera; and determine at least one vegetative index based on the provided red, green, and NIR color channels.