90 results about "Local variance" patented technology

The invention provides a self-adaptive nonlinear image enhancement method and system for a low-light scene of a mobile terminal, and relates to the technical field of image enhancement, and the method comprises the steps: carrying out the image preprocessing and noise reduction, and carrying out the graying and noise suppression of an input color image through a local variance self-adaptive algorithm; adaptive down-sampling is carried out, and the down-sampling proportion is dynamically adjusted according to the image resolution and the content complexity, so that the processing efficiency is improved; brightness adaptive enhancement is carried out, and the overall brightness of the image is rapidly improved by adopting an Otsu method and a lookup table; contrast nonlinear enhancement: enhancing image details and contrast in combination with a Laplace operator and local mean adjustment; and color restoration: restoring the resolution through bilinear interpolation and performing weighted fusion to realize natural color reconstruction. And finally, a high-quality image of which the brightness, the contrast ratio and the color are remarkably improved is output. According to the invention, the recognition accuracy and processing efficiency of the low-illumination image are improved.

The invention provides an adaptive welding seam detection and three-dimensional reconstruction method based on deep learning and binocular vision. The adaptive welding seam detection and three-dimensional reconstruction method comprises the steps of S1, collecting samples and making a training data set; s2, the picture of the sample to be welded is processed, a feature region is recognized, the image quality of the region to be welded is analyzed and evaluated through wavelet transform and local variance, and the noise level and the contrast ratio are calculated; s3, dynamically generating edge detection parameters and model fitting parameters according to the image quality; s4, using an edge detection algorithm to extract edge point cloud of the welding seam area; s5, performing RANSAC linear fitting, weighted least square fitting and polynomial curve fitting on the edge point cloud in parallel; s6, selecting an optimal fitting result based on an image quality adaptive dynamic scoring model; and S7, carrying out three-dimensional coordinate conversion in combination with the three-dimensional matching model IGEV-Stereo, and outputting a final welding seam three-dimensional coordinate. According to the invention, automatic detection of the position and size of the welding seam can be efficiently and accurately realized.

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 invention discloses a two-stage hyperspectral image band selection and target detection method, which comprises the following steps of: based on a two-stage band selection and background reconstruction network of a transformer, realizing band selection through a first-stage training transformer and realizing background reconstruction through a second-stage training transformer; a transform position coding module and a multi-head self-attention mechanism are used for learning similar features and difference features among wave bands, a full connection layer network is used as a clustering device, a structural similarity index and an Euclidean distance are added to serve as a loss function training model, wave band clustering is achieved, and finally a local variance is used for estimating the noise level of an image of each wave band. Obtaining a band training subset; the band subset is subjected to constraint energy minimization detection and then sent to a background feature extraction network composed of a transformer and a discriminator, background pixels are arranged through position coding, network learning background features are enhanced through a multi-head self-attention mechanism, an improved loss function is used for training, and therefore reconstruction of a background image is achieved. The invention provides a two-stage wave band selection and background reconstruction network based on transformer so as to realize hyperspectral target detection. The network comprises a first-stage wave band selection work and a second-stage background spectrum learning work. And difference detection is carried out on the final background reconstruction image and the original image, so that a final task is realized, and the detection precision is improved.

According to the infrared image enhancement method based on style migration and multi-level fusion, a style migration preprocessing step comprises five levels of coding and decoding modules, multi-scale feature fusion is realized through cross-level dense jump connection, an encoder compresses spatial dimensions step by step, and a decoder recovers resolution through deconvolution and cancels a batch normalization layer. Information loss of small target features in the standardization process is avoided, and a triple mixing loss function is adopted for training; in the super-resolution reconstruction step, curvelet decomposition is carried out on a style migration result, curve features such as an arc line are accurately captured by adopting a 36-direction wedge-shaped basis function, a local variance threshold mechanism is adopted for high-frequency sub-band fusion, and a style migration feature weight is given to a high-variance region; low-frequency sub-bands are dynamically weighted according to regional contrast, a residual dense block enhanced SRGAN architecture is adopted in a reconstruction stage, and training stability is improved in cooperation with a spectrum normalization discriminator. Through the mode, the technical problem of effectively improving the quality of the low-quality infrared image is solved.

The embodiment of the invention provides a weed image segmentation method, and the method comprises the steps: carrying out the region-of-interest extraction of an input image, so as to obtain a weed region image; calculating a local variance of the weed region image, and performing nonlinear normalization processing on the local variance to obtain a feature-enhanced weed region image; edge information of the weed area image after feature enhancement is enhanced by using a Laplace operator, and binarization processing is performed on local features of the weed area image after edge information enhancement by using adaptive threshold segmentation; and performing de-noising processing on the binarized weed region image by using morphological operation, and segmenting the de-noised weed region image to obtain a weed target in the weed region image. According to the method, the defects existing when a traditional ultra-green feature method is applied to weed image segmentation are overcome, higher adaptability is achieved, and the method can be widely applied to the fields of precision agriculture, intelligent monitoring and the like.

The invention discloses a video noise reduction method based on hybrid filtering, and the method comprises the steps: obtaining a pixel value set of each pixel position in a local window through a selected local window according to a frame-by-frame image in a video; according to the pixel value set, combining pixel weight weighting to obtain a weighted pixel value set, and through numerical sorting of the weighted pixel value set, obtaining a corrected mid-value pixel value and a corrected mean value pixel value; and obtaining a local variance according to the corrected median pixel value and the corrected mean value pixel value, obtaining a local weight in combination with a preset threshold value, and obtaining a denoised pixel value in combination with the corrected median pixel value and the corrected mean value pixel value. According to the method, weighted pixel set processing is adopted, local variance and self-adaptive weight are combined, balance of noise suppression and detail reservation is achieved, meanwhile, through dynamic adjustment of median and mean filtering weight, dust (Gaussian noise) and electric wave surge (impulse noise) in a construction site video are effectively suppressed, and the noise suppression rate is remarkably improved.

The invention discloses a multi-feature fusion heart sound classification method based on a channel attention mechanism, and belongs to the technical field of signal processing and heart disease diagnosis. The method comprises the steps that collected original heart sound records are preprocessed, a self-adaptive two-stage filtering method based on local variance and global variance is adopted, a wavelet denoising technology is combined, background noise is effectively removed, high-quality heart sound signals are extracted, and meanwhile heart sound events are accurately detected through a double-threshold segmentation method; fusing information of different dimensions to enrich the expression of the heart sound signal; a deep learning architecture fusing a convolutional recurrent neural network and a convolutional neural network is constructed, a channel attention mechanism is introduced, the fusion weight of each sub-network is dynamically adjusted according to the importance of features, and the classification accuracy is improved. According to the method, the quality of heart sound signals can be effectively improved, multi-domain features are fully fused, meanwhile, the discrimination ability of the model for heart sound categories is enhanced, and therefore the heart sound diagnosis precision is improved.

The invention relates to the technical field of computer vision, in particular to a three-dimensional scene reconstruction rendering method and system. The method comprises the following steps: generating a corresponding 3D Gaussian ellipsoid for each sparse point cloud of a scene to be reconstructed; obtaining a re-projection area of each 3D Gaussian ellipsoid on the two-dimensional image of different visual angles; taking each re-projection area as a center, and taking all re-projection areas within a set pixel distance range as adjacent re-projection areas of the re-projection area; the method is based on the sum of Gaussian kernel function values from the center of a re-projection area of each 3D Gaussian ellipsoid on a two-dimensional image of different visual angles to the center of each adjacent re-projection area of the area, the edge strength, the local variance, the normalized gradient magnitude, and the absolute value of the dot product of a gradient vector and a normal vector. Calculating a split control function value of each re-projection area; and comparing the splitting control function value of each re-projection area with a set threshold value, and controlling the 3D Gaussian ellipsoid to split. According to the invention, the reconstruction rendering precision of the three-dimensional scene is improved.

The invention relates to the technical field of image processing, in particular to a brake disc safety performance detection method and system. When the safety performance of the brake disc is detected, appearance detection, size detection and material performance detection are carried out on the brake disc, and detection is comprehensive; meanwhile, an improved adaptive filtering algorithm is adopted to carry out de-noising processing on the to-be-detected brake disc image; performing multi-scale decomposition on the to-be-detected brake disc image, and decomposing the to-be-detected brake disc image into sub-images of multiple scales; performing local feature extraction on each sub-image, and dynamically allocating weights according to the local features; performing adaptive filtering on the sub-image of each scale according to the weight so as to improve the filtering effect; the filtering intensity is dynamically adjusted according to the local variance, the gradient intensity and the texture information, so that the edges and details of the image can be better reserved, and excessive smoothness is avoided; in combination with global and local information, a self-adaptive weight is distributed for each pixel, the filtering effect is optimized, and the adaptability and robustness of the algorithm are improved.

The invention relates to the technical field of fringe projection three-dimensional reconstruction, in particular to a point cloud enhancement method and system based on polarization structure perception and trend guided recovery. The method comprises the following steps: acquiring a multi-angle polarization image, and generating an object area mask based on a polarization angle consistency mask, a polarization angle gradient mask and a linear polarization degree mask; obtaining an original phase diagram through four-step phase shift and complementary Gray codes, obtaining a missing region in an object according to hole distribution of the original phase diagram, and adding morphological structure indexes and local variance features to recognize a real self-shadow region; aiming at the identified real self-shadow area, establishing a structure guiding recovery model combining a first-order structure gradient guiding item and a second-order structure gradient guiding item to obtain a complete object point cloud; and separating a background and an invalid shadow in the object point cloud through the object area mask to obtain a point cloud which only retains the object area.

The invention relates to an intelligent detection method and system for differential settlement of a super high-rise building foundation, and belongs to the technical field of settlement detection. The method comprises the following steps: collecting settlement monitoring data, and constructing a training data set; calculating local statistics according to the spatial topological relation of the monitoring points, and combining with the adjacent point set to standardize the original data to obtain a standardized settlement value; calculating a curvature mutation factor through second-order difference, superposing a local variance weight factor to amplify the contribution of the mutation region, and splicing the standardized settlement feature vector and the curvature mutation factor to obtain an enhanced feature vector; constructing a differential settlement detection model, and inputting the enhanced feature vector into the model to obtain a differential settlement probability and a local abnormal thermodynamic diagram vector; optimizing the training process of the model through the loss function to obtain a trained model; newly collected real-time settlement monitoring data are processed and then input into the trained model, and an intelligent detection result is obtained. According to the invention, the detection capability of the differential settlement of the foundation can be improved.

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.

The present application relates to the technical field of mechanical hand calibration, and especially to a quick calibration method of a connecting rod type double-arm direct-drive mechanical hand; when detecting joint image data and extracting feature point coordinates of the marker, a dynamic threshold Canny edge detection algorithm is used to detect the joint image data, the detection precision and robustness of the Canny edge detection algorithm to the reflective point marker are improved by combining local image statistical characteristics and a dynamic threshold adjustment strategy, and the algorithm is particularly suitable for scenes with large changes in illumination and uneven contrast; furthermore, an adjustment coefficient is determined based on global gradient, global variance and global average brightness of the image, that is, the influence of the local variance and the local average brightness of each pixel point in the image on the high and low thresholds is constrained by the global characteristics of the image, the sensitivity of the algorithm to different image characteristics can be flexibly controlled, and the dynamic threshold Canny edge detection algorithm is more flexible and universal.

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 relates to the technical field of manipulator calibration, in particular to a quick calibration method for a connecting rod type double-arm direct-driven manipulator. When the joint image data are detected and the feature point coordinates of the marker are extracted, the joint image data are detected by adopting the dynamic threshold Canny edge detection algorithm, and the detection precision and robustness of the Canny edge detection algorithm on the reflective point marker are improved by combining the local image statistical characteristics and the dynamic threshold adjustment strategy, so that the detection accuracy and robustness of the reflective point marker are improved. The method is especially suitable for scenes with large illumination variation and non-uniform contrast; and further, an adjustment coefficient is determined based on the global gradient, the global variance and the global average brightness of the image, that is, the influence of the local variance and the local average brightness of each pixel point in the image on high and low thresholds is constrained through the global feature of the image, so that the sensitivity of the algorithm to different image features can be flexibly controlled, and the accuracy of the algorithm is improved. And the dynamic threshold Canny edge detection algorithm is more flexible and universal.

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 embodiment of the present application relates to the field of detection technology and discloses a defect detection method, which processes a detection image to select an empty detection item area and a detection item area, then grayscales the empty detection item area and the detection item area to obtain a grayscale image, further calculates the background mean and background variance of the grayscale image of the empty detection item area, determines the local mean and local variance of the grayscale image of the detection item area from the background mean, then corrects the grayscale value of each pixel in the grayscale image of the detection item area based on the background mean, background variance, local mean, and local variance to extract the scotch tape area, and finally determines whether the scotch tape has a defect based on the area of ​​the scotch tape area. In this way, the embodiment of the present application solves the problem of low efficiency in scotch tape defect detection.

The present invention discloses a method and system for rapid screening of internal mold in peanuts, specifically relating to the technical field of non-destructive testing of agricultural products, and is used to solve the problems of false detection and missed detection caused by low contrast and gradual boundaries in the initial stage of mildew and samples with internal diffusion in existing detection methods; based on hyperspectral imaging technology, the filter kernel size is dynamically adjusted through the joint analysis of frequency-domain energy distribution and spatial local variance to enhance the detailed features of the gradual boundaries in the image; the spectral and spatial feature consistency verification is used to screen high-confidence candidate regions, and the principal component features representing the mildew diffusion trend are extracted by combining texture direction analysis, and a fusion feature vector is constructed by dynamically splicing with the spectral gradient direction; the boundary confidence is quantified through a Bayesian probability classification model, and a region growing algorithm with neighborhood similarity constraint is used to generate a mildew marking map with continuous space and consistent features, and at the same time, a dynamic threshold correction mechanism is introduced to adaptively optimize the segmentation standard; the detection accuracy and anti-interference ability of fuzzy boundaries are improved.

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 belongs to the technical field of image analysis, and particularly relates to a waste water circulation treatment monitoring method and system for a sand separator, and the method comprises the steps: collecting a surface image of a waste water pool, enhancing the texture difference between foam and waste water through the technologies of local variance calculation, self-adaptive histogram equalization and the like, and calculating the surface image of the waste water pool; accurately segmenting a foam area by using an active contour model based on an enhanced texture gradient map; the foam coverage rate is obtained by calculating the area surrounded by the contours after convergence, the average brightness and the average texture of a foam area are extracted, and a foam thickness index is obtained through weighted fusion; finally, the system executes a hierarchical cooperative control strategy according to the two key indexes of the foam coverage rate and the thickness index, the defoaming device is intelligently started, stopped or adjusted, and automatic, refined and energy-saving management of foam is achieved.