The present invention enables highly accurate analysis when visualizing analysis results in spectral imaging.

An surface analysis method includes: acquiring spectral image data regarding a sample surface with use of a spectral camera; extracting n wavelengths dispersed in a specific wavelength range in the acquired spectral image data, and converting spectrums of the wavelengths in the spectral image data into n-dimensional spatial vectors for each pixel; normalizing the spatial vectors of the pixels; clustering the normalized spatial vectors into a specific number of classifications; and identifying and displaying pixels clustered into the classifications, for each of the classifications.

A pattern recognition system including an image gathering unit that gathers at least one digital representation of a field, an image analysis unit that pre-processes the at least one digital representation of a field, an annotation unit that provides a visualization of at least one channel for each of the at least one digital representation of the field, where the image analysis unit generates a plurality of image samples from each of the at least one digital representation of the field, and the image analysis unit splits each of the image samples into a plurality of categories.

A Raman spectrometer 1 comprising a laser 1001 for illuminating a sample S under investigation, an auto-focusing system for focusing the laser 1001 on the sample S under investigation, and a detector 1010 for detecting Raman spectra emitted in response to illumination by the laser 1001. The auto-focusing system further comprises at least one adjustable focusing element for adjusting the location of the focus of the laser, a determination unit 1012 for determining a selected location for the focus of the laser 1001, and a control unit for adjusting the adjustable focusing element to focus the laser at said selected location determined by the determination unit 1012. The auto-focusing system is arranged under the control of software to enable determination of the selected location for the focus of the laser 1001.

A system and method using remote sensing instrument with hyper spectrum quantitatively measure metal dust elements in lubricating oil, which includes (no limited): Al, Cd, Cr, Cu, Fe, Pb, Mg, Mn, Mo, Ni, Ag, Sn, Ti, V, Zn, B (Boron, for Coolant), Ca (Calcium for water contaminant), and particle size, cone penetration, dropping point, steel mesh oil separation, moisture, PQ concentration, in few seconds. The instrument integrates near-field communication (NFC), Internet of Thing (IoT), Cloud computing, spectral matching and other data processing, and application software forming a system to easily operated and build a model enable self-learning to improve precision through collection accumulation. With the system, the instrument as FIG. 1 can provide comprehensive on-site analysis enable preventive maintenance of mission critical engine and rotating equipment. The characteristics of the system are easy to operate, get result quickly, and self-learning to improve precision.

Embodiments of the invention provide a method and system that allows parameters of a desired target image to be determined from hyperspectral imagery of scene. The parameters may be representative of various aspects of the scene being imaged, particularly representative of physical properties of the scene. For example, in some medical imaging contexts, the property being imaged may be blood perfusion or oxygenation saturation level information per pixel. In one embodiment the parameters are obtained by collecting lower temporal and spatial resolution hyperspectral imagery, and then building a virtual hypercube of the information having a higher spatial resolution using a spatiospectral aware demosaicking process, the virtual hypercube then being used for estimation of the desired parameters at the higher spatial resolution. Alternatively, in another embodiment, instead of building the virtual hypercube and then performing the estimation, a joint demosaicking and parameter estimation operation is performed to obtain the parameters. Various white level and spectral calibration operations may also be performed to improve the results obtained. While establishing functional and technical requirements of an intraoperative system for surgery, we present iHSI system embodiments that allows for real-time wide-field HSI and responsive surgical guidance in a highly constrained operating theatre. Two exemplar embodiments exploiting state-of-the-art industrial HSI cameras, respectively using linescan and snapshot imaging technology, were investigated by performing assessments against established design criteria and ex vivo tissue experiments. We further report the use of one real-time iHSI embodiment during an ethically-approved in-patient clinical feasibility case study as part of a spinal fusion surgery therefore successfully validating our assumptions that our invention can be seamlessly integrated into the operating theatre without interrupting the surgical workflow.

A method for illumination of an object to be observed to be observed and the background, the method comprising the steps of: obtaining a relationship between wavelength and spectral radiance of the object while the object and the background are illuminated by a first light source that emits light that has a continuous spectrum in the wavelength range from 380 nanometers and 780 nanometers, and determining a value of representative wavelength that corresponds to a maximum value of the spectral radiance of the object plotted against wavelength or values of representative wavelength that correspond to maximum values of the spectral radiance of the object plotted against wavelength; determining a value or values of comparative wavelength; and illuminating the object and the background with light of the value or values of representative wavelength and light of the value or values of comparative wavelength.

A snapshot hyperspectral imaging method includes the steps of: S1, selecting a set of reference wavelengths for calibration, rectifying the shifted positions due to dispersion at each reference wavelength, and selecting a center wavelength; S2, estimating relative dispersion at each reconstructed wavelength with respect to the center wavelength; S3, generating a dispersion matrix describing the direction of dispersion, and generating a spectral response matrix using a spectral response curve of a sensor; S4, capturing images blurred with dispersion; S5, deblurring the dispersed images captured in S4 using the dispersion matrix and the spectral response matrix generated in S3 to obtain spectral data spatially aligned in all spectrums; and S6, projecting the aligned spectral data obtained in S5 into color space, extracting a foreground image by a threshold method, sampling the dispersed images obtained in S4 as strong prior constraints for the foreground image, and reconstructing accurate spatial hyperspectral data.

A method for analyzing 2D material thin film and a system for analyzing 2D material thin film are disclosed. The detection method includes the following steps: capturing sample images of 2D material thin films; measuring the 2D material thin films by a Raman spectrometer; performing a visible light hyperspectral algorithm on the sample images by a processor to generate a plurality of visible light hyperspectral images; performing a training and validation procedure, performing an image feature algorithm on the visible light hyperspectral images, and establishing a thin film prediction model based on a validation; and capturing a thin-film image to be measured by the optical microscope, performing the visible light hyperspectral algorithm, and then generating a distribution result of the thin-film image to be measured according to an analysis of the thin film prediction model.

A non-contact system for the sensing the concentration of a compound includes a hyperspectral imaging device configured to capture a hyperspectral image of a fluid, a flow cell configured to enable the capturing of a hyperspectral image of a fluid, a process, and a memory. The memory includes instructions stored thereon which, when executed by the processor, cause the system to generate a hyperspectral image of the fluid in the flow cell, generate several spectral signals based on the hyperspectral image, provide the spectral signal as an input to a machine learning network, and predict by the machine learning network the concentration of a compound in a fluid.

A spectral camera control device, being installed, along with a spectral camera provided with a liquid crystal tunable filter, in an aircraft capable of stationary flight. The spectral camera control device causes the spectral camera to capture a spectral image in a snapshot mode each time a transmission wavelength of the liquid crystal tunable filter is switched while the aircraft is in stationary flight, and the spectral camera control device causes a plurality of spectral images to be captured in succession at a same transmission wavelength when an SN ratio of the captured spectral image is less than a predetermined threshold.