A method for evaluating an infrared signature present on an object surface, which signature preferably forms a two-dimensional code. Furthermore, a monochrome or multicolour pattern, which reflects light in the visible wavelength range, can be present on the object surface. The infrared signature only absorbs light in the infrared range and can consequently be detected by means of an IR camera. In the method, an infrared light source is switched on and the infrared signature is illuminated with infrared light and an original image is recorded with an infrared camera in this state. The original image or a further-processed image based thereon is then filtered by a high-pass filter, the contrast in the image being increased indirectly or directly after the high-pass filtering. The infrared signature can finally be evaluated in the image processed in this way.

A method and system for generating a three-dimensional representation of a vehicle to assess damage to the vehicle. A mobile device may capture multispectral scans of a vehicle from each a plurality of cameras configured to scan the vehicle at a different wavelength of the electromagnetic spectrum. A virtual model of the vehicle may be generated from the multispectral scan of the vehicle, such that anomalous conditions or errors in individual wavelength data are omitted from model generation. A representation of the virtual model may be presented to the user via the display of the mobile device. The virtual model of the vehicle may further be analyzed to assess damage to the vehicle.

An illustrative method includes obtaining a three-dimensional (3D) mesh of a subject, obtaining a mesh model, and generating a hybrid mesh of the subject. The generating includes replacing a portion of the 3D mesh with the mesh model such that the hybrid mesh includes a non-replaced portion of the 3D mesh represented at a first resolution and the mesh model representing the replaced portion of the 3D mesh at a second resolution.

An illustrative method includes obtaining a three-dimensional (3D) mesh of a subject, obtaining a mesh model, and generating a hybrid mesh of the subject. The generating includes replacing a portion of the 3D mesh with the mesh model such that the hybrid mesh includes a non-replaced portion of the 3D mesh represented at a first resolution and the mesh model representing the replaced portion of the 3D mesh at a second resolution.

A method for classifying an object having the following: receiving at least one item of distance information of an object based on a first electromagnetic signal transmitted by a transmitter device and a first electromagnetic signal received by a receiver device; receiving at least one item of oscillation information of the object based on a second electromagnetic signal transmitted by a transmitter device and a second electromagnetic signal received by a receiver device, which represents a solid oscillation of at least one subsection of the object; and classifying the object based on the received information.

Various embodiments include methods and scanning systems for photonically detecting an object of high-interest having selective wavelength reflection. Various embodiments include sequentially scanning the environment by projecting a coherent pulsed electromagnetic beam of light of a first wavelength. Reflected light of the first coherent beam is received onto a photoelectric detector, which outputs digital intensity data. Various embodiments further include sequentially scanning the environment by projecting a coherent pulsed electromagnetic beam of light of a second wavelength different from the first wavelength. Reflected light of the second coherent beam is received onto a photoelectric detector, which outputs digital intensity data. The intensity of the reflected light of the first wavelength may be compared with the intensity reflected light of the second wavelength, and an alert may be sent to an autonomous vehicle system in response to the intensity difference exceeding a threshold.

Various embodiments include methods and scanning systems for photonically detecting an object of high-interest having selective wavelength reflection. Various embodiments include sequentially scanning the environment by projecting a coherent pulsed electromagnetic beam of light of a first wavelength. Reflected light of the first coherent beam is received onto a photoelectric detector, which outputs digital intensity data. Various embodiments further include sequentially scanning the environment by projecting a coherent pulsed electromagnetic beam of light of a second wavelength different from the first wavelength. Reflected light of the second coherent beam is received onto a photoelectric detector, which outputs digital intensity data. The intensity of the reflected light of the first wavelength may be compared with the intensity reflected light of the second wavelength, and an alert may be sent to an autonomous vehicle system in response to the intensity difference exceeding a threshold.

A method for determining one or more physiological parameters of a subject. The method includes providing a plurality of images of a vessel of the subject in response to illumination of the vessel to light of different wavelengths; converting each of the plurality of images of the vessel into at least two grayscale images, thereby generating a plurality of first grayscale images of a first wavelength range and a plurality of second grayscale images of a second wavelength range, the first wavelength range and the second wavelength range being different from each other; and determining the one or more physiological parameters of the subject based on at least the plurality of first grayscale images and the plurality of second grayscale images.

A method of training a neural network (Convolutional Neural Network-CNN) including reduced graphical annotation input is provided. The training method can be used to train a Testing CNN that can be used for determining Hemolysis (H), Icterus (I), and/or Lipemia (L), or Normal (N) of a serum or plasma portion of a test specimen. The training method includes capturing training images of multiple specimen containers including training specimens, generating region proposals of the serum or plasma portions of the training specimens; and selecting the best matches for the location, size and shape of the region proposals for the multiple training specimens. The obtained features (network and weights) from the training CNN can be used in a testing CNN. Quality check modules and testing apparatus adapted to carry out the training method, and characterization methods using abounding box regressor are described, as are other aspects.

A method for detecting an abnormal target includes: determining a first detection result based on an infrared signal detected in a detection area, where the first detection result is used to indicate whether an abnormal target exists in the detection area; acquiring N frames of images of the detection area and performing abnormal target detection on the N frames of images to obtain a second detection result, where the second detection result is used to indicate whether the abnormal target exists in the detection area and N is a positive integer; and determining that the abnormal target exists in the detection area, in a case that both the first detection result and the second detection result indicate that the abnormal target exists in the detection area.