The present invention relates to a cancer diagnostic apparatus for checking a cancer incidence by allowing an antibody (captured antibody), which reacts with an rpS3 (ribosomal protein S3; antigen) capable of checking a phenomenon of a cancer, to react with the rpS3, and performing concentration measurement after extracting an emission signal of the reacted antigen (rpS3), and a cancer diagnostic system using the cancer diagnostic apparatus. The cancer diagnostic system includes: the cancer diagnostic apparatus for irradiating an ultraviolet ray (UV) from a bottom of a bio-chip provided with an antibody reacting with an antigen (ribosomal protein S3; rpS3), making an image from a visible ray obtained from the ultraviolet ray, which is converted by the antigen and the antibody bound to each other while passing through the bio-chip, and extracting only a specific frequency color from the visible ray to generate cancer diagnosis information; and a user terminal.

The present invention relates to a cancer diagnostic apparatus for checking a cancer incidence by allowing an antibody (captured antibody), which reacts with an rpS3 (ribosomal protein S3; antigen) capable of checking a phenomenon of a cancer, to react with the rpS3, and performing concentration measurement after extracting an emission signal of the reacted antigen (rpS3), and a cancer diagnostic system using the cancer diagnostic apparatus. The cancer diagnostic system includes: the cancer diagnostic apparatus for irradiating an ultraviolet ray (UV) from a bottom of a bio-chip provided with an antibody reacting with an antigen (ribosomal protein S3; rpS3), making an image from a visible ray obtained from the ultraviolet ray, which is converted by the antigen and the antibody bound to each other while passing through the bio-chip, and extracting only a specific frequency color from the visible ray to generate cancer diagnosis information; and a user terminal.

A light source (11) of a disguising mask detecting device (1) emits light at a person who is a subject. A camera (12) acquires images in multiple different wavelength ranges of reflected light of the light emitted at the person. A face detector (172) detects a face region of the person from the images acquired by the camera (12). A determiner (173) determines that the person is wearing a disguising mask when luminances of the face region in the images satisfy specific relations different from relations exhibited by skin.

A light source (11) of a disguising mask detecting device (1) emits light at a person who is a subject. A camera (12) acquires images in multiple different wavelength ranges of reflected light of the light emitted at the person. A face detector (172) detects a face region of the person from the images acquired by the camera (12). A determiner (173) determines that the person is wearing a disguising mask when luminances of the face region in the images satisfy specific relations different from relations exhibited by skin.

A method of generating object surface texture in thermal infrared images is disclosed which includes receiving heat radiation from a scene by a spectropolarimetric imaging system, generating a plurality of spectral frames associated with the scene, each frame having a plurality of pixels, for each pixel from the generated plurality of spectral frames, extracting spectral information associated with the scene, including pixel-specific temperature representing an objects temperature, and thermal texture factor representing the objects texture, for each of a plurality of materials having a specific emissivity in a library, generating reference spectral information as a function of temperature and thermal texture, matching the extracted spectral information for each pixel from the generated plurality of spectral frames to the generated reference spectral information using a statistical method to minimize the associated variation, and extracting spectral metadata from the matched reference spectral information for the associated material based on the match.

Methods and apparatuses that utilize machine learning techniques to identify maintenance assets using sets of machine-health diagnostic images and link individual machine-health diagnostic images to the identified maintenance assets are described. The sets of machine-health diagnostic images may include a set of thermal images, a set of visible-light images, and/or a set of acoustic images. An identified maintenance asset may comprise an individual machine associated with a unique asset identifier. A diagnostic image linking system may acquire machine-health diagnostic images, apply object detection and other computer vision techniques to identify a particular machine within the machine-health diagnostic images, determine machine properties for the particular machine, generate a feature vector using the machine properties, select machine learning models corresponding with maintenance assets, generate predicted answers using the machine learning models, and generate an asset identifier for the particular machine based on the predicted answers.

Methods and apparatuses that utilize machine learning techniques to identify maintenance assets using sets of machine-health diagnostic images and link individual machine-health diagnostic images to the identified maintenance assets are described. The sets of machine-health diagnostic images may include a set of thermal images, a set of visible-light images, and/or a set of acoustic images. An identified maintenance asset may comprise an individual machine associated with a unique asset identifier. A diagnostic image linking system may acquire machine-health diagnostic images, apply object detection and other computer vision techniques to identify a particular machine within the machine-health diagnostic images, determine machine properties for the particular machine, generate a feature vector using the machine properties, select machine learning models corresponding with maintenance assets, generate predicted answers using the machine learning models, and generate an asset identifier for the particular machine based on the predicted answers.

The disclosure generally pertains to systems and methods for providing a vehicle-based security system. In an example method, at least a first image and a second image may be received from an infrared camera associated with a vehicle. A thermal 3-dimensional map may then be constructed from the first image and the second image. An area of interest may be identified in the thermal 3-dimensional map. A visual camera associated with the vehicle may then be instructed to obtain a third image including the area of interest. The third image including the area of interest may then be received, and a living object in the third image may be determined. A notification associated with the living object may then be transmitted.

There is achieved short-time measurement of a measurement target such as a farm field with high accuracy. Therefore, processing is performed on a sampling image obtained by the imaging of a part of a range as a measurement target with a multi spectrum camera that performs imaging to capture images in a plurality of different wavelength bands. Then, arithmetic processing is performed on the sampling image as a processing target to generate a calculation result image serving as evaluation information for the entire measurement target.

There is achieved short-time measurement of a measurement target such as a farm field with high accuracy. Therefore, processing is performed on a sampling image obtained by the imaging of a part of a range as a measurement target with a multi spectrum camera that performs imaging to capture images in a plurality of different wavelength bands. Then, arithmetic processing is performed on the sampling image as a processing target to generate a calculation result image serving as evaluation information for the entire measurement target.