52 results about "Local variance" patented technology

The invention relates to the technical field of detection, in particular to a complex shell detection method and system based on structured light scanning. The method comprises the following steps: calculating a wrapped phase value and a phase modulation amplitude of each pixel point under each frequency, calculating discrete probability distribution containing candidate stripe series and a corresponding probability for each pixel point based on a multi-frequency wrapped phase difference, generating an initial pixel confidence map, and constructing an energy function; the penalty weight of the smooth item is determined according to the phase modulation amplitude and the wrapped phase gradient local variance, obtaining an initial fringe order distribution diagram, updating discrete probability distribution by using an initial pixel confidence map, re-minimizing an energy function to obtain a corrected fringe order distribution diagram, and reconstructing a final three-dimensional shape. And iteratively optimizing a projector principal point and a radial distortion coefficient in system calibration parameters. According to the scheme, the geometric details of the surface can be reserved, and the processing effect and the measurement precision of the complex curved surface and the high-curvature area are improved.

The invention discloses a metal product defect detection method and system based on image recognition. The method comprises the following steps: firstly, executing reflection disturbance digestion processing on a surface image of a to-be-detected metal product to obtain a reflection digestion image; obtaining surface reference texture features of the defect-free metal product, and generating a reference texture model on the basis of a texture distribution rule, a gray average value and texture continuity; performing defect texture gradient separation on the reflection resolution image based on a reference texture model, positioning an abnormal region, and performing segmentation to obtain a suspected defect texture region; performing boundary pixel reconstruction on the suspected defect texture region to obtain a defect texture reconstruction region; obtaining a target defect texture region through local variance enhancement and neighborhood correlation analysis; and finally, the overexposure area is positioned, gray inverse stretching processing is performed, abnormal pixel group feature clustering is performed on the preprocessed defect area, and then a surface defect detection result is output, so that the precision and accuracy of metal product surface defect detection are improved, and the false detection rate is reduced.

The application discloses a textile defect detection method and system based on multi-light source dynamic fusion and a double-branch network, and belongs to the technical field of machine vision and industrial detection. The method acquires multi-view images of a textile under coaxial, low-angle and backlight conditions through an encoder trigger line array camera; a local variance calculation and image fusion are performed by using a Softmax dynamic weight algorithm with a temperature coefficient introduced to generate an enhanced background suppression image; subsequently, a first branch lightweight network containing a depth separable convolution and a CBAM mechanism is used to quickly locate a candidate defect area, and then a second branch network is used to finely classify the cropped candidate area, and a Focal Loss is used to solve the long-tail distribution problem. The application effectively solves the problems of easy loss of small defect features in complex texture textiles and large consumption of video memory, meets the demand of real-time online detection of high-resolution images in industrial fields, and greatly reduces the missed detection rate.

A shrimp feed residue lightweight detection method fusing multi-attention features belongs to the field of image processing, is used for solving the problem of improving small target detection accuracy, and is technically characterized by inputting a multi-scale feature map into a neck network of a YOLO network, calculating a fusion weight value through an EnSimAM attention module of the neck network, and obtaining a fusion feature map according to the fusion weight value; wherein the EnSimAM attention module comprises a global attention branch, a local variance enhancement branch and an edge response enhancement branch; the EnSimAM attention module calculates a fusion weight value based on the following mode; the global attention branch calculates a global attention weight; calculating a local attention weight by a local variance enhancement branch; the edge response enhancement branch calculates an edge attention weight; determining adaptive weight values of the global attention weight, the local attention weight and the edge attention weight; and according to the adaptive weight value, calculating a fusion weight value of the global attention weight, the local attention weight and the edge attention weight.

The present application relates to the technical field of computer vision, and particularly relates to a three-dimensional scene reconstruction rendering method and system. The present application generates a corresponding 3D Gaussian ellipsoid for each sparse point cloud of a scene to be reconstructed; obtains a re-projection area of each 3D Gaussian ellipsoid on a two-dimensional image at different viewing angles; takes each re-projection area as the center, and sets all re-projection areas within a certain pixel distance range as adjacent re-projection areas of the re-projection area; based on the sum of the Gaussian kernel function values from the center of the re-projection area of each 3D Gaussian ellipsoid on the two-dimensional image at different viewing angles to the centers of each adjacent re-projection area of the area, the edge intensity, the local variance, the normalized gradient amplitude, and the absolute value of the dot product of the gradient vector and the normal vector, calculates the split control function value of each re-projection area; compares the split control function value of each re-projection area with the size of a set threshold, and controls the 3D Gaussian ellipsoid to split. The present application improves the three-dimensional scene reconstruction rendering precision.

The invention relates to the technical field of textile fabric detection, in particular to a textile fabric defect detection method and system based on image recognition, and the detection method comprises the following steps: S1, collecting multiple frames of linear array images of a to-be-detected textile fabric; s2, generating a residual vector; s3, generating a model residual error; s4, generating a standardized residual error; s5, identifying a defect candidate signal; s6, when the consistency condition is met, judging that the flaws are real flaws and outputting the flaws; dynamic low-rank factor modeling is constructed for online tracking, time-varying fixed stripe noise is filtered out, complex texture interference is decoupled through space pre-whitening and local variance standardization, and defect signals are accurately captured through sparse constraint and space-time consistency verification. The problems that under narrow-band imaging, fixed stripe noise drifting is difficult to restrain, and false detection and missing detection are serious due to complex texture and noise coupling are solved, and finally the composite beneficial effects of high real-time performance, high detection rate and low false alarm rate are synchronously achieved under the condition of a high-speed production line.

This invention belongs to the field of image processing and computer vision technology, and discloses a lightweight single-image super-resolution reconstruction method based on a directional stripe hybrid network. It captures directional structural information of the image from both the spatial and frequency domains through horizontal stripes, vertical stripes, local window attention of the directional stripe self-attention module, and direction-specific convolution of the directional high-frequency enhancement module. The directional stripe self-attention module effectively captures long-distance dependencies extending along specific directions; the directional high-frequency enhancement module extracts high-frequency components through frequency domain decomposition, combines them with direction-specific convolution, and introduces a dual-guided adaptive enhancement mechanism. It identifies texture-rich regions through local variance maps and structural boundaries through edge maps, generating adaptive weights to guide feature enhancement, accurately enhancing high-frequency edges and textures with different orientations while effectively suppressing noise. The two work together to achieve spatial-frequency domain dual-level directional modeling, significantly improving the reconstruction quality of images rich in directional textures, such as architectural scenes and cartoon images, while maintaining a lightweight design.

The application discloses a video target recognition method based on multi-model hot switching, and belongs to the technical field of computer vision and video processing. The method comprises an arbitration module, a switching control module and a feature adaptation and buffer module. The arbitration module generates a switching preparation signal by extracting multi-dimensional indexes such as optical flow mean, local variance, target density and scene confidence in real time through a lightweight channel independent of main reasoning. The switching control module performs atomic replacement of a computation graph pointer in a vertical blanking period, and realizes millisecond-level hot switching with zero frame loss by combining an asynchronous pre-copy and a chasing mechanism of a double buffer. The feature adaptation and buffer module solves tensor shape mismatch between heterogeneous models through a pre-compiled adaptation layer. The application also provides optimization schemes such as multi-index nonlinear fusion decision, zero-copy memory management, local slice focus reasoning and edge-cloud hierarchical unloading, significantly reduces switching delay, guarantees continuous recognition of a video stream, and is suitable for edge computing scenes with limited resources.

The invention provides a breathing mode classification method and system based on a CNN-LSTM neural network, and the method comprises the steps: collecting body surface point cloud data in a non-shielding state and an arm shielding state in a human body breathing process, and extracting a breathing motion feature on the basis of extracting the breathing motion feature; the invention provides an optimization method for abnormal respiratory movement characteristics of a significant respiratory movement area, solves the interference of environmental noise and newborn clinical characteristics on respiratory signals, and comprises the following steps of: firstly, partitioning a thoracic and abdominal voxel model, and extracting the respiratory movement characteristics of the significant area based on KPCA (Kernel Principal Component Analysis); motion artifacts are removed through a Savitzky-Golay filter, body motion interference is inhibited by using a method of fusing a peak threshold method and a local variance threshold, and baseline drift is removed by using a grey wolf optimization algorithm. According to the method, a CNN-LSTM neural network is constructed to classify four breathing modes of normal, rapid, slow and pause, model parameters and evaluation indexes are determined, effective classification and recognition of the breathing modes are achieved, and the monitoring precision and clinical application value are improved.

This invention provides a method for predicting aging based on metabolite profiles using nonlocal variance enhancement, relating to the fields of medical data analysis and metabolomics. The method acquires LC-MS and / or GC-MS metabolite profile data and the actual age of the subject to be predicted. It performs missing data checks and intra-class normalization on the data, constructs a class-constrained nonlocal variance enhancement feature extraction model, and combines multi-kernel principal component analysis and linear multi-view fusion to obtain fused metabolite profile features. The fused features are then input into a support vector regression model to output predicted age values. The aging degree is determined based on the difference between the predicted and actual age values, which improves the feature expression ability and interpretability of metabolite profile age prediction.

The invention discloses an image denoising method based on multi-scale feature enhancement and local pixel disturbance. According to the method, features of different scales are extracted by introducing a multi-scale convolution module and cavity convolution, so that the perception capability of complex noise is enhanced. Meanwhile, flat areas are calculated through the local variance of the pseudo clean image, and pixel disturbance is carried out in the areas so as to weaken noise correlation. According to the method, channel attention and space attention mechanisms are combined, and learning of important features is enhanced. In the aspect of loss function design, pixel loss, multi-scale feature loss and local weighting loss are fused. In practical application, a cosine annealing learning rate scheduling and exponential moving average strategy is adopted, and the convergence speed and stability of the model are remarkably improved. According to the method, image details can be effectively reserved, and the method is suitable for denoising tasks of various complex scenes such as natural images and medical images.

This application discloses a method and system for detecting defects in metal products based on image recognition. The method first performs reflection perturbation elimination processing on the surface image of the metal product to be detected to obtain a reflection elimination image; then, it acquires the surface reference texture features of the defect-free metal product, and generates a reference texture model based on texture distribution patterns, gray-level mean, and texture continuity; next, it performs defect texture gradient separation on the reflection elimination image based on the reference texture model, locates abnormal regions, and segments them to obtain suspected defect texture regions; then, it performs boundary pixel reconstruction on the suspected defect texture regions to obtain defect texture reconstruction regions; the target defect texture region is obtained through local variance enhancement and neighborhood correlation analysis; finally, it locates overexposed regions and performs gray-level inverse stretching processing, and outputs the surface defect detection results after clustering abnormal pixel group features of the preprocessed defect regions, thereby improving the accuracy and precision of surface defect detection of metal products and reducing the false detection rate.

The invention discloses a pavement pit slot segmentation method based on adaptive statistical analysis and point cloud geometric features. The method comprises the following steps: converting pavement three-dimensional point cloud data into a depth map; calculating a local variance of the depth map, obtaining a contrast factor according to the local variance, performing CLAHE processing on the depth map, and performing bilinear interpolation on the depth map after the CLAHE processing according to the contrast factor to obtain a contrast enhanced map; binarization processing is carried out on the contrast enhancement image, a binarization result is mapped back to the road surface three-dimensional point cloud, and a target area point cloud is segmented; smoothing the point cloud of the target area, and performing multi-feature combined pre-selected point screening on the smoothed point cloud to obtain a pre-selected point set; and performing plane fitting on the pre-selected point set, calculating the distance from the point cloud of the target area to a fitting plane, and extracting the point cloud of the pavement pit slot based on the distance. The method can rapidly and effectively extract the pit slot on the road surface, and is especially suitable for the road condition with a slope.

The invention discloses a multi-focus image fusion method and system based on Allan variance. The method comprises the following steps: preprocessing a plurality of original multi-focus images with the same scene and different focusing clearness degrees; calculating a pixel mean value in the local window, taking the pixel mean value as a pixel value of a pixel point, and updating the multi-focus grayscale image; obtaining a data sequence in a local window, carrying out multiple groups, carrying out local Allan variance calculation, taking a calculation result as a pixel value of a pixel point, and obtaining a multi-focus feature image under multiple groups; under each group, assignment is carried out according to the pixel values of the multi-focus feature images with different focus definitions at the same position, a plurality of preliminary decision diagrams are generated, optimization and superposition are carried out, and a fusion decision diagram is obtained; and carrying out pixel point fusion on the plurality of original multi-focus images and the fusion decision diagram to generate a multi-focus fusion image. The method can improve the definition of the image and enrich the details of the image.

The invention relates to a self-adaptive gain adjustment fingerprint image enhancement method, which comprises the following steps of: firstly, performing frequency domain decomposition on a fingerprint image to obtain a low-frequency component and a high-frequency component; then calculating a local variance and a local standard deviation of the high-frequency component, and adaptively determining a gain adjustment coefficient according to the standard deviation; performing adaptive enhancement on the high-frequency component by using a gain adjustment coefficient; and finally, recombining the enhanced high-frequency component and the enhanced low-frequency component to obtain an enhanced fingerprint image. Through frequency domain decomposition and adaptive gain adjustment, the definition and contrast of the fingerprint image can be effectively improved, detail features of the fingerprint are highlighted, a good foundation is laid for subsequent fingerprint feature extraction and comparison, and the accuracy of fingerprint identification is improved.

The present disclosure relates to an adaptive semi-global stereo matching parameter setting method, device, electronic equipment and storage medium. The method comprises: based on a left camera image of a binocular camera shooting a target, obtaining the coordinates of a preset left target area and calculating the image proportion; calculating the global variance and the local variance; calculating the local inverse luminosity according to the local variance, generating the minimum disparity number based on a preset correspondence relationship; calculating the joint inverse luminosity according to the image proportion, the global variance and the local variance, generating the sum of absolute differences cost calculation window based on a preset correspondence relationship; configuring the minimum disparity number and the sum of absolute differences cost calculation window to complete the adaptive setting of the semi-global stereo matching parameters. By calculating the minimum disparity number and the sum of absolute differences cost calculation window size of the image, the present disclosure can adapt to various lighting conditions and perform autonomous parameter setting under non-human control.

The application discloses an infrared image non-uniformity prediction and correction method based on wavelet transform, which comprises the following steps: adopting double-density dual-tree complex wavelet transform to perform multi-scale decomposition on an input infrared image; in a high-frequency subband, detecting a blind element position based on a local variance statistical method; in a low-frequency subband, constructing an autoregressive model, predicting non-uniformity of a background region, and performing accurate processing on a prediction result; combining the prediction results of the high-frequency subband and the low-frequency subband to generate a pre-correction coefficient; and adopting the pre-correction coefficient to perform correction processing on an original infrared image to obtain a corrected image. Through the combination of an adaptive fusion strategy, space-time consistency constraint and dynamic gain and bias updating technology, the problems of non-uniformity noise, balance between details and background, device drift compensation and consistency in a dynamic scene in infrared image processing are solved, and the accuracy and stability of infrared image processing are significantly improved.

The present application relates to the technical field of detection, and particularly relates to a complex shell detection method and system based on structured light scanning. The method comprises the following steps: calculating the wrapped phase value and phase modulation amplitude of each pixel point at each frequency, calculating the discrete probability distribution containing the candidate fringe order and the corresponding probability for each pixel point based on the multi-frequency wrapped phase difference, generating an initial pixel confidence map, constructing an energy function, determining the penalty weight of the smoothing term according to the phase modulation amplitude and the local variance of the wrapped phase gradient, obtaining a preliminary fringe order distribution map, updating the discrete probability distribution by using the initial pixel confidence map, re-minimizing the energy function to obtain a corrected fringe order distribution map, reconstructing the final three-dimensional topography, and iteratively optimizing the projector principal point and the radial distortion coefficient in the system calibration parameters. That is, the scheme of the present application can preserve the surface geometric details, improve the effect of processing the complex curved surface and the high-curvature area, and improve the measurement accuracy.

The application belongs to the technical field of image fusion, and particularly relates to a remote sensing image fusion method and system based on local variance image mutual information. The method comprises the following steps: firstly, using a multivariate linear regression component replacement fusion method to perform fusion to obtain an initial fusion image; then, on the basis of the initial fusion image, similarity between an intensity component of a high-resolution multispectral image and an intensity component of the initial fusion image is calculated with the aim of obtaining an optimal Gaussian filter; finally, the original high-resolution panchromatic image is filtered by using the estimated optimal Gaussian filter to obtain a low-resolution panchromatic image, and image fusion is performed on the basis of the low-resolution panchromatic image. The application makes the fused image more conducive to the preservation of spectral characteristics and reduces spatial distortion in the spatial scale.

The invention relates to the technical field of computer systems based on specific calculation models, and discloses a platform spatio-temporal feature fusion method introducing a Transform model, which comprises the following steps: acquiring a platform scene spatio-temporal feature tensor, and calculating similarity distribution to generate an initial attention score matrix; counting the local variance of the score matrix to generate a weight adjustment operator; extracting topological constraint attributes and motion vector parameters of the platform, and mapping to generate a dynamic bias factor; according to the method, a gain adjustment mechanism of semantic uncertainty and physical bias strength is established, the prior intervention strength of the model is automatically enhanced, and the probability of the prior intervention of the model is lowered. Adaptive switching of a data driving mode and a physical guiding mode is achieved, and logic continuity of feature fusion and situation awareness accuracy under complex working conditions are ensured.

The invention belongs to the technical field of computer vision, and particularly relates to an improved RT-DETR-based target detection method in a foggy environment. The invention provides a targeted self-adaptive repair framework which is specially designed for solving the problem of non-uniform degradation caused by fog and is used for positioning degradation first and then performing gating repair. According to the algorithm, firstly, local variance perception is introduced through a variance enhanced convolution block attention mechanism, so that an area degraded due to fog in a feature map is identified; and then decomposing a detection task into two parallel steps of degradation perception and content restoration in a feature degradation enhancement module. In the degradation sensing stage, in order to simulate the physical characteristics of irregular fog form and fuzzy boundary, deformable convolution is adopted, so that the irregular boundary of the degradation area is adaptively fitted. In a content restoration stage, a self-adaptive gating enhancement mechanism is used to take the spatial degradation map generated in the previous step as a pixel-level weight estimation feature correction amount. And finally, performing target detection of the vehicle in the foggy weather by using the repaired features.

The application provides an image processing method based on an improved wavelet threshold function, and belongs to the field of image processing, comprising the following steps: wavelet processing a to-be-processed image to obtain wavelet coefficients; processing the wavelet coefficients by using the improved wavelet threshold function to obtain new wavelet coefficients; and reconstructing to obtain a denoised image according to the new wavelet coefficients, wherein the improved wavelet threshold function involves second-order gradient information of the to-be-processed image, noise standard deviation of the to-be-processed image, local variance and local gradient, and the part of the improved wavelet threshold function processing the wavelet coefficients greater than a threshold value adopts a Gaussian attenuation function, and the part of the improved wavelet threshold function processing the wavelet coefficients less than the threshold value adopts a logarithmic attenuation function. The application can effectively avoid over-denoising or noise residue, and improve image processing quality.

The invention provides a liquid rocket engine simulation test run video noise reduction system and a noise reduction method, which are used for solving the technical problem that an existing image noise reduction method is difficult to meet the engineering actual demand of liquid rocket engine simulation test run video noise reduction. According to the liquid rocket engine simulation test run video noise reduction system provided by the invention, the color space conversion module is adopted to convert an input image frame in an original RGB format into a YUV format, separation of a brightness component and a chrominance component is realized, a Y channel containing main noise is subjected to multi-step processing through the Y channel processing unit, and the noise is reduced. A space domain-time domain combined adaptive mechanism is formed, so that the edge weight and the local variance realize space domain self-adaption to adapt to a strenuous motion scene, respond to the change of the current frame more quickly and resist a trailing phenomenon; lightweight denoising is performed on a U channel and a V channel which only contain color information by adopting bilateral filtering, the effect and efficiency are both considered, and the denoising effect and the video stability are remarkably improved.

The invention relates to the technical field of silicon wafer detection, and discloses a silicon wafer multi-defect decoupling weighted fitting method and device and a storage medium, and the method comprises the steps: S1, obtaining original point cloud data, S2, calculating a gradient G and a local variance Var (z), recognizing a defect dominant type, S3, stripping defects and noise through a multi-defect coupling model, and obtaining a real surface type point cloud; according to the method, the problems of defect confusion and non-uniform global precision of a traditional technology are solved, self-adaptive adaptation of different defect scenes is realized, manual intervention is not needed, the fitting precision of r, k and Sq is greatly improved, the detection error of a multi-defect superposition scene is controlled, and the detection accuracy of the multi-defect superposition scene is improved. The detection efficiency is guaranteed, and the industrial mass production requirement is met.

The present application belongs to the technical field of image analysis, and particularly relates to a wastewater recycling treatment monitoring method and system for a sandstone separator, which comprises the following steps: collecting a surface image of a wastewater pool, using local variance calculation and adaptive histogram equalization and other technologies to enhance the texture difference between foam and wastewater, and then using an active contour model based on an enhanced texture gradient image to accurately segment the foam area; calculating the area surrounded by the converged contour line to obtain the foam coverage, and extracting the average brightness and average texture of the foam area to be weighted and fused into a foam thickness index; finally, the system executes a hierarchical collaborative control strategy according to the two key indicators of the foam coverage and the thickness index, intelligently starts and stops or adjusts the defoaming device, and realizes the automatic, refined and energy-saving management of the foam.

The invention discloses a distributed optimization sampling method for different nodes under a heterovariance situation, and the method comprises the steps: calculating an optimal target sampling number corresponding to each node in a distributed random optimization problem, and enabling the sampling number of each node to depend on a local variance, thereby achieving the distributed optimization sampling of different nodes under the heterovariance situation, and improving the sampling efficiency. The limitation that the sampling number of each node is equal in the prior art is broken, the sampling cost under the heterovariance condition is greatly reduced, and the calculation efficiency of the distributed random optimization method under the heterovariance condition is greatly improved; meanwhile, the convergence of the provided method is theoretically guaranteed, and the reliability of the distributed optimization sampling method is improved.

The embodiment of the present disclosure discloses an aerial infrared moving target detection method and device combined with space-time information, which comprises: performing prior weight filtering pretreatment on an original infrared sequence image to obtain a pretreated sequence image; the original infrared sequence image is an infrared image collected for an aerial moving target; a local variance feature map of the sequence image is established based on variance information of a mean value of a target region and a mean value of a background region in the pretreated sequence image, and a target potential region is obtained; a target feature map is obtained by performing space domain weighting and time domain weighting on the target potential region; and a moving target is detected based on the target feature map. The technical scheme can improve the accuracy of target detection.

The invention discloses a self-adaptive space segmentation method for global sensitivity analysis of an undercarriage mechanism. The method comprises the following steps: firstly, generating an alternative sample pool by considering the randomness of sample points, and then dividing the sample pool into a plurality of continuous non-overlapped subintervals / subspaces by utilizing a space division method; and a next sample point is intelligently selected from the alternative pool through a Sobol index learning function, and the function measures the potential of the candidate point on improved local variance estimation, the change intensity of the function at the point and the coverage value of the function on an area which is not fully explored at the same time. After a new sample is added, the agent model is updated, and the sensitivity index is re-evaluated. In the process, the convergence condition of the condition variance is monitored, and a dynamic space re-division mechanism is introduced, so that the condition variance is always matched with the current data state. And finally, solving a final Sobol index of the undercarriage mechanism based on a space segmentation method of global sensitivity analysis, and obtaining an input parameter importance measure.

The present disclosure relates to the technical field of fringe projection three-dimensional reconstruction, and particularly relates to a point cloud enhancement method and system based on polarization structure perception and trend guided recovery; the method comprises: acquiring polarization images at multiple angles, generating an object region mask based on a polarization angle consistency mask, a polarization angle gradient mask and a linear polarization degree mask; obtaining an original phase image through four-step phase shifting and a complementary Gray code, and then obtaining an internal missing region of an object according to a hole distribution of the original phase image, adding a morphological structure index and a local variance feature to identify a real self-shadow region; for the identified real self-shadow region, a structure guided recovery model combining a first-order structure gradient guide term and a second-order structure gradient guide term is established to obtain a complete object point cloud; the background and invalid shadow in the object point cloud are separated through the object region mask to obtain a point cloud retaining only the object region.