120 results about "Spectral domain" patented technology

The invention provides a modified metal surface defect detection method and device and a medium, and the method comprises the steps: obtaining a multi-angle reflection image sequence of a modified metal surface under the irradiation of a multi-spectral light source, and generating a defect sensitive parameter set based on a preset modified metal material characteristic database and in combination with the spectral reflectivity distribution information of the multi-angle reflection image sequence; performing cooperative feature enhancement on the multi-angle reflection image sequence and the defect sensitive parameter set, and enhancing the feature contrast of a defect area and a normal area through weight distribution to obtain an enhanced defect feature set; joint anomaly detection of a spatial domain and a spectral domain is carried out on the enhanced defect feature set, a potential defect region of the modified metal surface is obtained through identification, and a defect region feature descriptor is generated; and performing defect morphological quantitative analysis according to the defect region feature descriptors, and determining the type, position and severity level of the modified metal surface defect. According to the invention, the comprehensiveness and reliability of a defect detection result can be improved.

The invention relates to the technical field of tunnel crack detection and artificial intelligence edge calculation, and provides a tunnel apparent disease detection method based on deep learning and knowledge distillation, which comprises the following steps: step 1, introducing spectral domain information enhancement to an original tunnel image, the edge texture features of the disease area in the image are enhanced through methods such as multi-scale wavelet transform and small-scale enhancement. Step 2, constructing a high-performance teacher model, introducing a flexible up-sampling structure to adapt to feature recovery requirements of different levels of semantic information, introducing an efficient visual coding module to enhance feature fusion capability of different scale channels, and designing a scale adaptive weighted loss function at the same time; by introducing a frequency spectrum enhancement mechanism, structural features of disease areas with low contrast, fuzzy edges and the like are remarkably enhanced in an image preprocessing stage, clearer information input is provided for a model, and the stable recognition capability of a system in environments of uneven illumination, complex background and the like is enhanced.

The invention discloses an optical cable micro-disturbance identification method based on physical simulation and self-supervised time sequence decoupling, and relates to the technical field of optical cable identification, and the method comprises the steps: constructing a physical digital twin simulator, and generating a high-fidelity training set; constructing a deep learning model, wherein the deep learning model adopts a lightweight time sequence decoupling network; training the model by adopting a staged training strategy, and sequentially carrying out self-supervised noise distribution pre-training, simulation supervised training and spectral domain physical consistency fine tuning operation; inputting DAS time sequence data collected in real time into the trained model, and outputting the data as an optical cable identity ID and a physical position; the lightweight time sequence decoupling network comprises a physical guide preprocessing module, a lightweight U-Net separation module, a sparse gating module and an intelligent parallel decoding module. Through the technical means of simulation-driven data generation, staged training strategies and the like, the defects of the prior art in the aspects of reducing the data cost, improving the detection capability in a low SNR environment, realizing multi-source blind source separation and the like are overcome.

The invention belongs to the technical field of remote sensing, and discloses a wolfberry planting distribution high-precision remote sensing monitoring method based on multi-temporal fusion and spectral feature optimization. According to the method, a multi-temporal and multi-spectral satellite image is used as a data source, and preprocessing and multi-temporal image fusion are firstly carried out; the method comprises the following core steps: establishing a time sequence characteristic curve according to a unique phenological period (such as bare soil characteristics in a dormancy period and high vegetation coverage in a rapid growth period) of wolfberry; in a spectral domain, screening out a characteristic spectrum dimension combination with the highest discrimination degree between the wolfberry and other crops through a characteristic wave band optimization algorithm (such as vegetation index difference degree and red edge characteristics); and in combination with an object-oriented classification or deep learning classification model, constructing a space-time coupling classifier, and performing high-precision extraction and distribution mapping on the Chinese wolfberry planting region. The method can effectively solve the problem of confusion classification of Chinese wolfberry and similar ground features (such as other shrubs and orchards), and realizes rapid and accurate monitoring of Chinese wolfberry planting area and spatial distribution.

The invention discloses a vector flow velocity measuring device and method based on a bidirectional scanning spectral domain optical coherence tomography technology. Comprising a super light-emitting diode light source, an optical fiber coupler, a polarization controller, an interferometer comprising a sample arm and a reference arm, and a collimating lens, the collimating lens, the grating, the focusing lens group and the linear array camera are sequentially connected through a light path; the linear array camera, the computer and the two-dimensional galvanometer are sequentially connected through a line; according to the method, the fixed bias vector velocity is introduced by utilizing the bidirectional scanning technology, the dynamic light scattering optical coherence tomography method and the Doppler optical coherence tomography method are combined, decoupling of each parameter of the vector flow velocity is efficiently and accurately realized, and a quantitative index is provided for vector flow velocity analysis. The vector flow velocity measuring device based on the bidirectional scanning spectral domain optical coherence tomography technology is simple in system setting and low in cost, the time resolution is improved by about 2.5 times, and the measuring time is shorter.

The invention relates to the technical field of big data, in particular to a personalized recommendation method and device based on structure and behavior layering and a medium, and the method comprises the steps: extracting global features and graph structure information of interaction data of a user and an article; modeling a personalized portrait of the user, and generating a filter coefficient; carrying out hierarchical coefficient fusion, and executing mixed spectral domain atlas filtering to obtain a personalized atlas filtering score; obtaining a basic recommendation score through multi-source score fusion, and executing dynamic score modulation to obtain a final recommendation score; and generating a recommendation list based on the final recommendation score, and returning the recommendation list to the user. According to the method, through layered and two-dimensional personalized modeling, a recommendation engine can dynamically adjust the signal response of each user in a low frequency-high frequency and global-personalized spectral domain, so that the diversity, novelty and interpretability of a recommendation result are remarkably improved while the recommendation precision is ensured.

The invention discloses an extreme short and temporary rainfall forecasting method and system based on wavelet domain decoupling and multi-course learning, and belongs to the technical field of meteorological big data processing and artificial intelligence deep learning. According to the invention, a meteorological image sequence is decomposed into a low-frequency approximate component and a high-frequency detail component through frequency domain decoupling, a double-branch deep neural network is adopted to extract features and predict corresponding frequency domain coefficients, and a rainfall prediction map is output through fusion of a spectral domain reconstruction and refining module. In addition, a multi-task course learning strategy is introduced during model optimization, and the loss weight is dynamically adjusted. The method is characterized in that modeling is moved to a wavelet domain to explicitly protect high-frequency details, local severe convection is accurately captured in combination with a double-branch architecture and a feature pyramid, the problem of traditional prediction fuzziness is effectively solved, the prediction definition and the extreme rainfall early-warning capacity are remarkably improved, high-fidelity and high-precision minute-level short-temporary rainfall prediction is achieved, and the prediction efficiency is improved. And reliable technical support is provided for meteorological disaster prevention and reduction.

The invention discloses a high-fidelity visual character image generation method, system and device and a storage medium, which are corresponding schemes, in the scheme, spatial domain and spectral domain feature mutual learning is realized based on a double-domain font coding mechanism, and the problem of character structure disorder is solved; based on a frequency domain perception refining mechanism, a frequency domain signal is dynamically modulated to improve stroke detail precision, collaborative optimization of character details and background quality is realized through an instance-level scaling coefficient, and the bottlenecks of character distortion and background instability in the prior art are broken through. Besides, the method supports multi-language accurate generation, can be widely applied to commercial scenes such as advertisement design, product packaging, film and television posters, teaching materials and the like, and can greatly improve creation efficiency and reduce manual correction cost; and meanwhile, the feasibility of cooperatively improving the multi-modal generation efficiency by the cross-domain features is verified, a high-quality weak supervision training sample in a scarce scene can be provided for a character recognition model, the blank of related data is effectively filled up, and the method has technical advancement and market landing value.

The invention belongs to the technical field of electric power, and provides a user load increase and probability boundary prediction method, which comprises the following steps of: firstly, realizing unified projection and deep fusion of multi-source heterogeneous data by adopting a topological adaptive space-time fusion mechanism; secondly, physical constraint comparative learning and hypergraph clustering are utilized to generate a load-sensitive topology mode according with a power grid rule; a CPT-Mama model is constructed by injecting physical rules, meteorological codes and lightweight NTK features, and spatial correction is performed by means of a graph attention network to improve the precision of load increment prediction, so that efficient prediction of the multi-modal load increment is realized; and finally, innovatively introducing a net rack spectral domain built-in operator, reconstructing a residual error and quantifying uncertainty, and accurately describing a probability boundary of load increase. According to the method, the medium and long term load prediction precision is improved, the crossing from point prediction to probability prediction is realized, and reliable technical support is provided for power grid dispatching, risk prevention and control and planning decision.

This invention discloses a method for camouflaged target detection, belonging to the field of camouflaged target detection. This method constructs a BiSAM based on the SAM network. The SAM encoder, CNN encoder, and dual-domain cascaded attention module BCAM work collaboratively to enrich multi-scale feature information and extract multi-source features from the input image. Specifically, BCAM operates collaboratively in the spatial and spectral domains, selectively recovering and enhancing high-frequency object boundaries while suppressing irrelevant background noise. The multi-source features are fused using AKM to guide the SAM decoder in decoding the ViT image embedding. The method replaces the original position encoding of the cross-attention key vector in the SAM decoder, injecting task-specific knowledge into the frozen SAM decoder. By replacing the original position encoding, the general attention key vector is transformed into a task-aware attention key vector, specifically adapted to camouflaged target detection scenarios, thereby improving the accuracy of camouflaged target detection while maintaining generalization ability.

The invention provides a seismic fault identification method and system based on multi-attribute collaboration and spectral domain graph enhancement, and relates to the technical field of seismic exploration, and the method comprises the steps: carrying out the all-directional dip angle and azimuth angle scanning of a seismic data volume, extracting multi-scale guide field information through combining structure tensor decomposition, and executing anisotropic diffusion filtering; calculating characteristic value distribution through a characteristic value coherence algorithm based on the filtering data volume, and determining the structural consistency difference of adjacent seismic traces to obtain a fracture coherence attribute data volume; carrying out azimuth gather sorting and pre-stack time migration processing on the seismic data volume, extracting seismic wave dynamic response characteristics, carrying out Fourier series expansion on the azimuth change rate to obtain a crack indication information data volume, splicing the data volume and executing multi-scale three-dimensional convolution solution, and constructing a spatial dependency graph through a spectral clustering algorithm; spectral domain enhancement features are obtained through spectral domain graph transformation and frequency selective filtering reconstruction, and morphological connectivity analysis is executed to obtain a crack prediction result.

The invention discloses a visible light and infrared image fusion method based on double-domain collaborative attention. A space-frequency joint optimization framework (DD-CANet) is constructed to realize multi-modal feature complementation. The method specifically comprises the following steps: firstly, in a spatial domain feature extraction module, realizing extraction of multi-scale context features by combining an expansion convolution kernel and a self-adaptive activation function; then, a cascade feature enhancement strategy is adopted, and channel-level dynamic calibration and edge consistency constraint are achieved through a double-attention cooperation unit; then, a spectral decomposition fusion mechanism is introduced into a frequency domain feature extraction module, an amplitude spectrum and a phase spectrum are processed through fast Fourier transform, and de-noised and feature-enhanced spectrum information is obtained in combination with a spectral domain cross encoder; and finally, obtaining a high-quality fusion image through a cross-domain fusion module. The device is reasonable in structural design and suitable for the technical field of image fusion.

The application relates to the technical field of optical processing, and discloses a laser optical processing system with adjustable imaging depth, which comprises a laser emission unit, an optical transmission unit, a spectral domain optical coherence tomography unit, a laser galvanometer scanning unit, an indicating light imaging unit, an image processing and display unit and a control unit. The wavelength scanning mode of the spectral domain optical coherence tomography system is driven by a scanning telescopic rotating module, time scanning of high output wavelength is realized, image processing technology is combined to provide position information or image information of a sample from a surface to an interior in real time, the position of the sample is scanned in real time by a three-dimensional galvanometer scanning unit, the position and directional information of a scanning point are recorded, the real-time image information of the sample is simultaneously scanned, different depths, widths and axial positions are realized, the focusing depth and wavelength of an imaging laser beam, the focusing position and energy of a scanning laser beam are adjusted and fed back, and the sample is positioned for processing.

The application relates to the technical field of optical detection, and discloses an imaging depth-adjustable guided laser positioning system, which comprises a laser emission unit, a laser galvanometer scanning unit, a spectral domain optical coherence tomography unit, an indicating light imaging unit, an image processing and display unit and a control unit. The application utilizes a scanning telescopic rotating module to drive the wavelength scanning mode of the spectral domain optical coherence tomography system, improves and adjusts the Z depth range of the spectral domain optical coherence tomography system in real time, realizes time scanning of high output wavelength, accurately positions the scanning laser beam and the imaging laser beam in real time, and can guide the scanning laser beam and the imaging laser beam to be positioned from the sample surface to the sample interior by the image information from the sample surface to the sample interior, the real-time scanning of the sample interior different depths, widths and axial positions by the three-dimensional galvanometer scanning unit, the recording of the position and orientation information of the scanning points, and the real-time photographing of the sample by the indicating light imaging unit.

The invention discloses a photovoltaic array insulation state real-time monitoring method based on distributed optical fiber sensing, particularly relates to the technical field of power cable fault detection, and is used for solving the problem that a plurality of independent insulation defect points in a cable accessory are difficult to effectively distinguish and position from a fused surface temperature field in the prior art. The method comprises the following steps: acquiring temperature distribution data measured by distributed optical fibers along a cable line, identifying a temperature abnormal region, mapping the temperature data of the abnormal region into a graph signal, performing graph Fourier transform to obtain spectral domain representation, analyzing statistical independence among different frequency components in a spectral domain, and calculating the spectral domain representation. And if the possibility exists, identifying candidate positions of potential defect points in a spatial domain through inverse transformation according to spectral domain characteristics related to statistical independence, and carrying out spatial clustering and center calculation on the candidate positions, so as to determine whether a multi-heat-source composite fault exists in the attachment. And a plurality of potential independent insulation defect points in the cable accessory can be identified and accurately positioned.

The application provides a kind of wind light water storage double-layer robust optimization scheduling strategy construction method and system, relating to energy management technical field, including first obtaining the multi-source data of wind farm, photovoltaic power station and cascade hydropower station;Characteristic extraction and integrated prediction are carried out based on the multi-source data, and wind light power prediction result is generated;Further, water power coupling modeling analysis is combined to obtain the power generation capacity of hydropower station and the sequence of adjustable water volume;By constructing multi-layer energy coupling graph and carrying out spectral domain structure optimization, power coupling network representing space-time correlation and ecological sensitivity is formed;Finally, double-layer distributed robust optimization is used to optimize economic target and ecological risk constraint simultaneously, and generate collaborative scheduling strategy.The application effectively solves the time sequence matching problem between wind light fluctuation and water power time lag, improves the renewable energy consumption capacity and water resource utilization efficiency, and reduces the ecological operation risk.

The invention provides a velocity deblurring method based on multi-band radar Doppler power spectrum joint constraint, which comprises the following steps: acquiring at least two different-band radar Doppler power spectrum data, the maximum non-blurring velocity of a second-band radar being smaller than that of a first-band radar; eliminating invalid distance library data through noise bottom estimation and effective spectrum threshold judgment; deblurring the first wave band radar to obtain a reference speed profile; establishing a cross-band data corresponding relation through space-time matching; determining the fuzzy type of the second band radar library by library; performing spectral domain reconstruction on the power spectrum according to partial folding or complete folding, constructing a candidate spread spectrum, and selecting an optimal folding frequency and a deblurring speed by utilizing cross-band consistency constraint and vertical continuity joint judgment; and outputting the deblurring speed profile and the quality identifier of each wave band. According to the invention, the technical problems of poor speed deblurring accuracy and low robustness in a complex meteorological scene and misjudgment caused by the fact that a single-band method depends on single continuity constraint or average speed are solved.

A spectral domain optical coherence tomography (OCT) system utilizes an electrowetting prism having four isolated electrodes and configured to deflect a light beam according to an actuating control signal applied to the electrodes. The control signal causes the light beam to laterally scan over a sample. The light beam is produced by a broad bandwidth laser and light from the sample and a reference path is provided to a spectrometer for analysis. The light source may be an all-normal-dispersion fiber laser seeding a gain-managed nonlinear fiber amplifier. The electrowetting prism is capable of scanning up to ±6°. The system is low power and operates at kHz speed.

The invention provides a reflection wave band spectrum domain influence uncertainty quantification method and device for reference star radiation reference transmission, and relates to the technical field of data processing, and the method comprises the steps: inputting ground features, atmosphere and observation geometric parameters into a radiation transmission model, and simulating to generate high-resolution atmosphere top spectrum radiance data as a real radiation spectrum; carrying out convolution on the data and a reference instrument spectral response function to obtain a reference instrument measurement spectrum; the reference instrument measurement spectrum and the real radiation spectrum are respectively convolved with the target instrument spectral response function, the relative difference percentage is calculated, the root mean square is taken, and a first standard uncertainty is obtained; positive and negative deviation is applied to the central wavelength of the reference instrument, and a wavelength sensitivity coefficient is obtained through calculation and normalization; the variance is synthesized into related parameters to obtain the total standard uncertainty, then the coverage factors are fused, and the expansion uncertainty is output. The accuracy of the quantification result is improved.

The application relates to the technical field of spectral imaging, and discloses a multi-snapshot hyperspectral compression imaging system. The multi-snapshot hyperspectral compression imaging system disclosed by the application comprises an optical lens, a pixel filter, a surface array camera and a displacement actuator. The displacement actuator moves the pixel filter according to a preset movement mode so that a detector collects multiple image data with the filter being at different positions and having different filtering effects. An upper computer carries out spatial combination through a spectral domain reconstruction neural network model to reconstruct the multiple image data into spatial-spectral data cube reconstruction data. At this time, image processing is carried out through a set original image acquisition device, so that the spatial-spectral data cube reconstruction data reconstructed through the above scheme has high accuracy, thereby solving the technical problem of low image reconstruction accuracy of the existing hyperspectral compression imaging system.

This invention discloses a self-supervised heterogeneous graph learning method for item recommendation based on a unified spatial-spectral perspective. This method is a self-supervised heterogeneous graph learning framework that simultaneously considers spatial and spectral domain information, which is beneficial for graph representation learning. Specifically, a spatial encoder is used to aggregate information from local neighbor nodes, and a spectral encoder is used to extract global structural information, thereby suppressing noise from local aggregation. Subsequently, a homeomorphism mapping method is proposed, which can align dual-domain representations while avoiding the loss of domain-specific information. Furthermore, a spectral optimization module is proposed, which uses fused representations to generate cluster-level guidance for effectively optimizing the original representations. Extensive experimental results on various real-world heterogeneous graph datasets for item recommendation demonstrate the significant superiority of this invention.

Abstract Apparatus and methods are presented for spectral domain optical imaging, in particular for single shot 3-D spectral domain imaging of the retina of the human eye. In certain embodiments one or more 3-D images across elongated areas of an object are acquired, with scanning perpendicular to the long axis of the elongated areas for imaging extended volumes of 5 the object. In preferred embodiments the captured light is sampled in the Fourier plane, in a dimension substantially perpendicular to the long axis, with a cylindrical lenslet array, while in other embodiments the captured light is sampled in the image plane. Apparatus and methods are also presented for hyperspectral imaging of the retina, with the illuminating beams preferably angled to suppress interference from corneal reflections. Apparatus and methods are 10 also presented for multi-wavelength wavefront sensing, with simultaneous capture of light in two or more paths with different delays. 5 1020 26 20 47 51 19 J un 2 02 6 A b s t r a c t 1 9 J u n 2 0 2 6 5 2 0 2 6 2 0 4 7 5 1 1 0

The invention provides a clothes defect detection method and system based on multi-spectrum characteristic harmonic complex, and relates to the technical field of image detection. The method comprises the following steps: establishing a multi-spectrum characteristic harmonic aggregation complex defect detection model and carrying out pre-training, wherein the multi-spectrum characteristic harmonic aggregation complex defect detection model comprises a spectral domain frequency division network, a spectrum harmonic aggregation network, a boundary spectrum complex network and a target detection output layer; inputting a to-be-detected image in the original clothing image data into the multi-spectrum feature harmonic congelation complex defect detection model, and processing the to-be-detected image by using a spectral domain frequency division network, a spectrum harmonic congelation network and a boundary spectrum complex network in sequence to obtain feature representation with enhanced edge perception capability, and performing target detection and classification on the feature representation through a target detection layer, and outputting a defect detection result of the to-be-detected image. The problems that in the actual production process, the garment flaws are difficult to recognize, and the manual detection efficiency is low are solved, and efficient and accurate detection of the garment flaws under the actual engineering application scene is achieved.

This invention discloses a dual-stream collaborative reconstruction method and system based on template frame initialization, belonging to the field of image processing technology. The method includes: recovering sparse point clouds from multi-view images and segmenting them to generate template clothing meshes and their UV unwrapping; initializing Gaussian elements and binding mesh patches using a nearest neighbor strategy, and extracting physical and Gaussian cue features to form a feature matrix; using a spectral domain low-frequency and spatial domain high-frequency dual-stream network to predict global deformation and local wrinkles respectively, and updating the clothing mesh; then jointly optimizing the human-clothing mesh through rendering and collision loss; and achieving high-fidelity clothing appearance reconstruction based on UV domain Gaussian representation and appearance refinement network, combined with differentiable rendering. This invention achieves high-fidelity and dynamically consistent 3D reconstruction of clothing images through template initialization and spectral-spatial dual-domain collaborative deformation prediction.

The invention discloses a characteristic model analysis method based on a periodic Green function and application thereof, and the method is characterized in that a unit structure model of a target unit is obtained, triangular mesh generation is carried out, and an RWG primary function is constructed; acquiring a periodic Green function and splitting the periodic Green function into a spatial domain part and a spectral domain part by using EWALD transformation; constructing an impedance matrix; after the impedance matrix is decomposed, a generalized eigenvalue equation is constructed, and eigenvalues are solved, so that the mode importance of a target unit is obtained, and analysis of a feature mode is completed; the method has the advantages that coupling of a target unit and other units in a periodic structure is directly calculated by adopting a periodic Green function, and a real mode in an infinite period is accurately reflected; according to the method, the periodic Green function is split into the spatial domain part and the spectral domain part by utilizing EWALD transformation, convergence is carried out on the spatial domain part and the spectral domain part respectively, and addition is carried out after convergence, so that the convergence speed is greatly accelerated, the calculation complexity is greatly reduced, and the calculation efficiency is improved.

The invention discloses a weak measurement-based sapphire low-temperature thermal expansion coefficient detection method and system, relates to the technical field of material thermophysical property precision measurement, belongs to the technical field of material thermophysical property precision measurement, and applies a weak measurement technology to low-temperature thermal expansion measurement of a sapphire block material. An interference light path containing a sapphire sample as an optical sensitive element is constructed, weak optical path difference (or phase change) caused by temperature change of sapphire is converted into observable significant shift in a spectral domain by utilizing a weak value amplification mechanism, and then the change of spectral characteristic wavelength along with temperature is measured; and in combination with the known sapphire refractive index temperature coefficient, the thermal expansion coefficient of the sapphire can be inverted with high precision. The device can realize non-contact and high-sensitivity measurement of the thermal expansion coefficient, has strong interference capability, and is suitable for precise measurement of the thermal expansion coefficient of sapphire and other transparent brittle materials in a full-temperature region from extremely low temperature to room temperature.

This invention relates to the field of fiber optic temperature sensing technology, and in particular to a method and system for sensing temperature in hollow-core optical fibers based on soliton self-frequency shift. The method includes generating a probe pulse using a laser source and modulating it into an optical soliton signal; injecting the optical soliton signal into a hollow-core photonic crystal fiber filled with high-pressure inert gas; receiving a backscattered echo signal at the incident end of the hollow-core photonic crystal fiber; synchronously splitting the echo signal to a time-domain probe branch and a spectral-domain probe branch; calculating the flight time of the echo signal based on the time-domain signal; identifying physical deformation events in the fiber; calculating the spectral redshift of the soliton center wavelength relative to a reference wavelength; cross-judging temperature abrupt events and physical deformation events; constructing a target error function and performing iterative optimization to obtain temperature data distributed along the hollow-core photonic crystal fiber. This invention achieves high-precision inversion of spectral refresh rate and temperature data distributed along the fiber.

An imaging system is provided. A first imaging system acquires initial sensor data in the form of visible domain data. A second imaging system acquires subsequent sensor data in the form of second domain data, where the initial and subsequent sensor data have different spectral domains. A controller subsystem detects, in real-time, at least one region of interest by applying a machine learning technique to the visible domain data, locates at least one object of interest in the at least one region of interest to generate location data for the at least one object of interest, and autonomously directs a focus of the second imaging system to an area of a scene that includes the object of interest in response to the location data to acquire the second domain data.

This invention provides a system for simultaneously measuring thin film thickness and refractive index based on spectral domain optical coherence tomography. It employs a free-space Michelson interferometer structure, using a beam splitter to separate broadband low-coherence light into reference and sample light. The two beams propagate separately in the free-space reference arm and free-space sample arm, respectively, before being combined and interfering. This fundamentally avoids the adverse effects of dispersion, polarization changes, and optical path drift introduced during fiber optic transmission. Simultaneously, this invention utilizes spectral domain optical coherence tomography to obtain the depth position information of different reflective interfaces in the sample arm. Combined with the positional changes of the plane mirror under reference and sample conditions, the geometric thickness and refractive index of the thin film can be simultaneously obtained by solving a simple set of equations. This simplifies the measurement process, improves data processing efficiency and measurement operability, and provides a reliable technical foundation for online monitoring and quality control in industrial coating processes.

The present application relates to the field of computer vision and biometric identification technology, specifically to an identity recognition and authentication system based on machine vision three-dimensional features, comprising: a data acquisition module: obtaining and preprocessing discrete three-dimensional point cloud data to generate a pure point cloud set; a manifold construction module: constructing a triangular mesh model and solving a generalized eigenvalue problem to obtain a feature sequence; a spectral domain decoupling module: performing frequency domain band-pass filtering on the feature sequence and reorganizing it into an identity feature spectrum; a fingerprint generation module: generating a hot kernel or wave kernel signature and converting it into a multi-dimensional spectrum fingerprint histogram; a matching decision module: calculating the spectral domain distance of the histogram and the standard spectrum fingerprint and judging the authentication condition; an adaptive updating module: if the condition is met, weighted updating is performed, otherwise, a rejection instruction is triggered; the present application improves the tolerance to non-rigid deformation while ensuring the identity structure recognition degree.