1274 results about "Gradation" patented technology

The invention discloses a cut workpiece defect detection method and system based on image feature feedback, and relates to the technical field of image processing.The method comprises the steps that cut workpiece technological characteristics are obtained, a preset defect type library is constructed, a hardware system is built, and parameters are initialized; synchronously acquiring a multi-view original image, and storing and associating annotation information; de-noising the original image, enhancing the contrast, and extracting a region of interest ROI; extracting texture, shape, edge and gray features from the ROI, and screening through a Relief-F algorithm to obtain an optimal feature subset; inputting into an SVM (Support Vector Machine) model for reasoning, and screening to obtain an effective defect detection result; and calculating an evaluation index and generating a feedback signal, and performing iterative optimization after adjusting parameters. The system comprises an acquisition module, a master control module, a data processing module and a display module. Through the precise design and closed-loop feedback of the whole process, the precision, efficiency and long-term adaptability of defect detection of the complex cutting workpiece are improved, and the industrial quality management and control requirements are met.

The invention relates to the technical field of image data processing, in particular to a forge piece surface defect detection method and system based on machine vision. The method comprises the steps of obtaining a gray image of a to-be-detected forging surface; determining a boundary significance weight; determining a path consistency weight; screening the boundary significance weight and the path consistency weight to obtain a final weight; and carrying out local adaptive threshold segmentation on the final feature map to obtain a binary image, and carrying out defect identification on the surface of the forge piece to be detected based on a connected region in the binary image. According to the method, the boundary significance weight and the path consistency weight are constructed and are respectively used for accurately positioning defect edges and verifying structure continuity, texture interference is effectively inhibited, and false alarms are reduced; through adaptive Gabor filtering, the problem of a response blind area of a traditional method is solved, finally two weights are fused to modulate filtering response, and the accuracy of forging surface defect detection is improved.

The invention relates to the technical field of metal defect detection, and discloses an aluminum alloy round aluminum rod surface defect image super-resolution method. The method comprises the following steps: acquiring a low-resolution original image sequence of surface defects of the aluminum alloy round aluminum rod, and synchronously acquiring gray value distribution at different illumination angles through a multi-channel optical sensor; constructing a dynamic degradation model according to pixel displacement of adjacent frames in the original image sequence, extracting cross-scale defect features in the original image sequence, and taking output parameters of the dynamic degradation model as spatial constraint conditions of a feature extraction network; and a high-resolution defect image is generated through the multi-stage residual error reconstruction network, the high-resolution image output by the reconstruction network is fed back to the dynamic degradation model, and the frequency domain response coefficient of the spatial fuzzy kernel function is updated to form closed-loop optimization. The identification degree of defect features is improved, and a reliable image data basis is provided for accurate detection of the surface defects of the aluminum alloy round aluminum rod.

The invention relates to the technical field of computer vision, in particular to a defect segmentation positioning method and system for an inorganic mineral casting image, and the method comprises the following steps: calling an illumination image to analyze brightness, matching exposure parameters, splicing the image, analyzing a gradient, recognizing a defect, screening an effective region, calculating a gray variance, and constructing roughness weight recognition texture features. According to the method, the high-reflection area identification, the brightness gradient analysis, the pixel-level roughness weight and the structure tensor analysis are combined, the exposure interval can be dynamically adjusted when the casting image is processed, the defect type information is output in the direction, and the positioning information is generated by correcting the recognition position in combination with the actual coordinate of the target spot. The method has the advantages that the high-reflection area identification, the brightness gradient analysis, the pixel-level roughness weight and the structure tensor analysis are combined; the method has the advantages that the method is simple and easy to implement, detail loss of overexposure areas is reduced, the recognition precision of defect areas is improved, accurate area segmentation and classification processing are achieved, roughness weight calculation combining gray variance and pixel density is combined, the sensitivity to surface fine defects is enhanced, and the precision and reliability of defect positioning are improved.

The invention discloses a quality detection system and method for a glue injection laser contour, and belongs to the technical field of quality detection, and the method comprises the steps: obtaining a reflection point cloud signal of a glue injection finished product, constructing a point cloud data stream, setting an image reconstruction method, constructing a glue layer three-dimensional model, synchronously generating a pseudo-color grey-scale map, and achieving the visual representation of the surface texture of a glue layer. A three-layer feature extractor is constructed, a quality primary judgment method is set, features of different scales are fused in a cross-layer mode, the problem of missing detection of complex defects in traditional single-feature detection is solved, basic defects are automatically recognized, and a quality primary judgment result is output; and performing defect judgment on the glue-injected finished product which is unqualified in primary judgment, setting a defect fine judgment method, performing voxel-level segmentation on the three-dimensional image, calculating defect characteristics, and judging whether the defects affect the use performance of the product or not so as to generate a fine judgment result, so that accurate positioning of complex defects is realized, and the defect type identification accuracy is improved.

The invention belongs to the technical field of deep learning, and particularly discloses an ultra-precision full-field displacement measurement method and system based on a convolutional variational auto-encoder. Comprising the following steps: acquiring a video when a to-be-detected structure is subjected to vibration deformation, and selecting a picture of a deformation position to construct a data set; training a deep learning model of the convolutional variational auto-encoder based on the data set; reconstructing the gray value of the original image by using the trained deep learning model to obtain a gray value containing infinitesimal displacement information; and carrying out displacement calculation on the reconstructed image by utilizing an optical flow method, and realizing ultra-precision displacement calculation on the to-be-measured structure according to gray value conversion. According to the method, the problem that the infinitesimal displacement smaller than the sensitivity limit is difficult to measure is solved, the problem that the to-be-measured structure is blocked and cannot be measured is solved by utilizing the characteristics of the generative deep learning model, and the basic data precision of displacement measurement is remarkably improved.

The invention discloses an image multi-modal feature extraction method, a ground feature classification and recognition method and a GIS image generation method. Comprising the following steps: firstly, extracting texture features from a target image by adopting a multi-directional statistical method fusing rotation invariant coding of a local binary pattern and a gray-level co-occurrence matrix, and extracting color features from the target image by adopting an LAB-HSV dual-color space collaborative analysis method to obtain features of different modes of the target image; and then according to the information values of the extracted color features and texture features, adjusting the weight ratio corresponding to the color features and the texture features so as to optimize the recognition precision of the classification model on complex ground features. And finally, according to a weight ratio corresponding to the color feature and the texture feature, performing weighted fusion on the color feature and the texture feature to obtain a corresponding multi-modal feature vector.

The invention relates to the technical field of mode recognition and data recognition, and discloses an intelligent visual detection method for non-standard precision part surface microdefects, which comprises the following steps: acquiring surface gray level image data of a to-be-detected part, physically abandoning low-frequency components through discrete wavelet transform, and reserving high-frequency detail components to construct a frequency domain input tensor; constructing a double-flow reconstruction model containing a space domain coding network and a frequency domain coding network, and minimizing the distribution difference of the same feature between double-domain characterization through potential feature space consistency constraint joint optimization; the method comprises the following steps of: calculating a spatial domain residual image and a frequency domain residual image, combining a texture topological residual image extracted by structural tensor characteristic decomposition, and generating a comprehensive abnormal response image through weighted fusion to judge the defect, and effectively inhibiting macroscopic geometric contour interference through frequency domain decoupling and a topological check mechanism on the premise of not needing a standard geometric template. And sensitive perception and accurate identification of weak texture defects on the surface of the non-standard part are realized.

The invention relates to the technical field of machine vision, in particular to a precision part size automatic detection method and system based on machine vision, precision part images are collected through a high-precision industrial camera, part positioning is carried out, sub-pixel-level topological feature mapping is carried out on interested area images, and precision part size automatic detection is carried out. Comprising the steps of gray histogram equalization, gray morphological processing, edge detection and edge chain code tracking, construction of an edge point topological feature space, execution of sub-pixel subdivision, obtaining of an edge line through contour analysis of a feature distance and a feature angle, and double-constraint geometric reconstruction based on the edge line. The characteristic distance and the characteristic angle are used for rotation matrix conversion and geometric dimension calculation, a relation model of the geometric dimension and the actual dimension of the part is established, precise part dimension measurement is achieved through dynamic error analysis and compensation, the measurement precision is remarkably improved, the risk caused by unreliability of a single characteristic is effectively reduced, and the measurement accuracy is improved. And the measurement stability is improved.

The invention discloses a metal processing detection system and method based on computer vision, and relates to the technical field of defect detection.Rotatable polarization filters are installed in front of a lens of a vision sensor, the polarization filters are arranged at four angles respectively, and four sets of polarization images are collected; calculating the polarization degree of each pixel point on the surface during metal processing by using the gray values of the four groups of polarization images, setting a polarization region interval, and dividing a metal surface reflection region into a specular reflection region and a diffuse reflection region; acquiring images of different wavebands by using a multispectral vision sensor, and constructing a multispectral image set; for the same wave band, the images of different exposure times are synthesized to obtain an HDR high dynamic range image; setting a weight for the HDR image of each wave band, and combining the weighting of the HDR image and the suppression of the polarization degree to carry out multispectral image fusion so as to obtain a final image; and a gray threshold value and an area threshold value are respectively calculated through the average value and the standard deviation, and the metal processing defects are judged by using the two threshold values.

The invention discloses a method for identifying small target features in a ray detection image, and relates to the field of image processing, and the method comprises the steps: carrying out the size unification, pixel standardization and normalization preprocessing of an original ray image; constructing a training sample set through image region cutting, and introducing a dynamic sampling strategy to realize positive and negative sample proportion adaptive control; enhancing the number and diversity of small target samples in a training set by using point-shaped and linear artificial defect generation strategies; constructing an image segmentation model and introducing feature jump connection to fuse shallow space and deep semantic information; combining Dice loss and Focal loss to form a composite loss function, and guiding the model to pay attention to a target region with a small area and weak gray level; and finally, a segmentation result is optimized through morphological processing and connected domain analysis, and structured target detection information is output. According to the invention, the recognition accuracy and integrity of the tiny target in the ray image can be effectively improved, and the adaptive capacity of the detection method to the change of the imaging quality is enhanced.

The invention belongs to the technical field of inspection robot systems, and particularly relates to an inspection robot path planning method and system.The visual semantic perception module collects an industrial environment image through a top industrial camera, after graying and Gaussian filtering preprocessing, feature points are detected and matched through an ORB algorithm, and a path planning result is obtained; in combination with an illumination self-adaptive threshold screening mechanism, mismatching points are eliminated, semantics are marked, and a semantic feature map is constructed; the initial path planning module generates an initial path through a semantic cost-containing A * algorithm based on the map; the dynamic obstacle avoidance module captures a moving obstacle by using a visual sensor, and predicts a trajectory through Kalman filtering; the path optimization module combines an initial path and an obstacle track, optimizes the path by using quadratic programming of a fusion curvature constraint and an energy consumption model, and corrects positioning by fusing vision and IMU data through a dynamic weight fusion algorithm; and the execution feedback module generates an instruction according to the optimized path, re-triggers path optimization, forms a closed loop, and ensures the inspection stability.

The invention relates to the technical field of size measurement, in particular to a clock part size measurement method and system based on machine vision, and the method comprises the following steps: based on clock part surface data, analyzing reflection characteristics and pixel statistics, combining a light source angle to optimize imaging, comparing gray level distribution and shadow area, and screening an image with optimal marginal definition. And fusing the brightness and contour information, screening multi-direction size data, judging the stability of boundary pixels, and outputting a size measurement result. According to the method, multiple data features are automatically collected and fused in the collection process, parameter optimization of different surface structure information is combined, cross validation of multi-direction size information is synchronously completed in the measurement link, abnormal fluctuation data are eliminated, continuous judgment and result screening are achieved in the whole processing flow, and the accuracy of data processing is improved. The stable size data of batch parts can be efficiently obtained under the automatic discrimination process, the influence of abnormal interference on the output accuracy is reduced, and the uniformity and traceability of the measurement data are improved.

The invention provides a CLAHE image enhancement optimization method and system based on an FPGA and a medium. The method comprises the steps that firstly, an input image is divided into a plurality of histogram sub-regions; on the basis of histogram statistics of each sub-region, the histograms of the sub-regions are cut, pixels exceeding a threshold value are redistributed according to a given algorithm, and the histograms are corrected; mapping is carried out through CDF operation to obtain an equalized gray level, and then gray stretching is carried out to generate a new gray mapping result; and finally, reading the mapping gray level stored in the previous frame, processing the gray value of the pixel point through combined operation of bilinear interpolation, completing block effect elimination between the sub-regions, and outputting an image after local contrast enhancement. According to the method provided by the invention, the controllability of the overall brightness level of the image can be ensured on the basis of ensuring the enhancement effect of the video image. And meanwhile, the method is realized on an FPGA platform, so that the processing speed of image enhancement is ensured, and the dual requirements on the processing speed and quality can be met.

The invention discloses a shield muck coarse particle gradation real-time identification system and method based on multi-modal machine vision, and relates to the field of muck particle identification, and the system comprises a multi-modal data collection module which is composed of a plurality of groups of sensors and is used for collecting multi-modal data of muck particles; the edge calculation processing unit is used for preprocessing the multi-modal data; the analysis module is used for constructing a deep learning model and realizing grading prediction and mud content prediction; and the control module is used for real-time feedback and control including visualization, decision support and tunneling parameter adaptive adjustment. According to the system, accurate real-time analysis of geometric, image and material features of muck particles is realized through multi-modal sensor fusion and edge calculation efficient preprocessing in combination with feature-level / decision-level fusion and a lightweight deep learning model; by means of closed-loop control, the method has high adaptability and engineering intelligent feedback capacity, and muck grading recognition precision and tunneling parameter self-adaptive adjustment efficiency in shield construction are remarkably improved.

The invention relates to the field of image data processing, in particular to a part defect detection method and system based on machine vision, and the method comprises the steps: obtaining a gray image of the surface of an automobile part, carrying out the preprocessing, and extracting a surface texture image of a part region; using a fuzzy entropy method to block the surface texture image, and obtaining the gray difference degree and the abnormal degree in each block according to the gray value change and the texture expression in each block; dynamically adjusting the number of iterations of the sub-blocks based on the abnormal degree of the blocks; and optimizing an existing fuzzy entropy method according to the number of iterations to obtain a significant fuzzy entropy value of each block, and judging whether the automobile part has a bubble defect area or not based on the significant fuzzy entropy values. According to the method, the gray difference degree and the texture change in each block are analyzed, and the fuzzy entropy method is combined, so that the potential bubble defect area can be identified more accurately.

The invention discloses an enamel plate surface defect detection method based on machine vision, and relates to the technical field of enamel plate surface defect detection. The method comprises the following steps: acquiring an original image of an enamel plate, and fusing the original image through a point-by-point optimal gray fusion method to obtain a real-time reflection suppression image; a defect-free reflection suppression image of the enamel plate is collected, matching analysis is conducted on the defect-free reflection suppression image and the real-time reflection suppression image through an improved PaDiM algorithm, a difference texture area is obtained, and coordinates of the difference texture area are recorded; in the difference texture area, two types of image features are extracted through a scale attention feature extraction method and fused, a real-time and standard fusion matrix is obtained, and a defect detection result is obtained through similarity matching; and converting the coordinates of the difference region to obtain a defect positioning result, calculating a defect comprehensive score based on a defect detection result, and grading to complete the surface defect detection of the enamel plate.

The invention discloses a video anti-shake method and system based on multi-scale fusion and adaptive smoothing, relates to the technical field of video image processing, and aims to effectively solve the image quality problem caused by shake in a video shooting process. Gradient histograms and wavelet energy distribution characteristics of video frames are extracted through graying and normalization processing, and the gradient histograms and the wavelet energy distribution characteristics are input into a jitter type recognition network to recognize translation, rotation and Z-axis jitter probabilities. And further extracting motion, frequency domain and edge features, and generating multi-modal coupling features through combination of a dynamic feature interaction network and a dot product attention mechanism. And constructing a motion trajectory by using the features, optimizing the trajectory by using a texture perception double-layer smoothing strategy, introducing an adaptive penalty term into a dynamic planning cost function, and outputting a smooth motion compensation parameter. And finally, processing the boundary region through motion compensation and image extrapolation to generate an anti-shake video frame. Through multi-scale feature fusion and a self-adaptive smoothing strategy, the video anti-shake effect is effectively improved, and the method is suitable for complex scenes.

The invention relates to the technical field of visual control, in particular to a picture defect test equipment control system and method, and the system comprises a difference tensor construction module, a space-time constraint construction module, a strain response judgment module, a coupling focusing mapping module and a boundary stability evaluation module. According to the method, the multi-dimensional difference incidence relation is constructed by extracting the pixel gray difference value, the brightness gradient and the chrominance phase angle, and fine brightness fluctuation and color shift in a picture can be captured. The superposition of the time-space change rate enables the dynamic performance of image defects to be distinguished, and continuous and transient anomalies can be effectively recognized. And in combination with tensor strain ratio and difference amplitude analysis, an abnormal region can be adaptively recognized, and neglect of a traditional method on a complex scene is avoided. The weighted aggregation of the local energy response improves the stability of the defect boundary, ensures the accurate positioning and judgment consistency of the image defect, and significantly improves the reliability and accuracy of the detection result especially in dynamic change and complex illumination environments.

The invention discloses a visual filter tip modeling recognition and control system and a data interaction method thereof, and relates to the technical field of image recognition and intelligent control, and the method comprises the following steps: carrying out the continuous image collection of a filter tip through an industrial camera, and obtaining an image sequence of the filter tip in a conveying process; for each frame of image in the image sequence, extracting a transverse brightness change curve in a gray domain, and constructing an image brightness distribution matrix based on the spatial distribution of the filter tip in the image recognition area; according to the invention, by introducing a frequency domain analysis and risk prediction control mechanism, intelligent perception, quantitative evaluation and dynamic suppression of periodic optical interference are realized. The system can extract interference frequency characteristics, calculate the stability and occupancy ratio, predict and identify the risk in combination with a historical misjudgment model, dynamically adjust the duty ratio of the LED light source according to the risk result, break the resonance relation with the conveying rhythm, and suppress false characteristic interference. The method improves the accuracy, stability and yield of an identification system, and has good engineering application value.

The invention belongs to the technical field of image data processing, and discloses a driver fatigue state early warning method and system based on face recognition tracking, and the method comprises the steps: extracting a face region through semantic segmentation, splitting a single-channel gray-scale map, calculating the local complexity and noise degree of a pixel point, obtaining a window adjustment coefficient, and carrying out the adjustment of the window adjustment coefficient; the size of the adaptive window is determined, different pixel region features can be adapted in a targeted manner, and the processing deviation caused by a fixed window is effectively reduced; an illumination component is subsequently extracted based on an adaptive window, and the image is enhanced through a Retinex algorithm, so that the face image quality under complex illumination can be improved, and the detail definition is improved; and finally, the fatigue degree is detected in combination with a PERCLOS algorithm, so that the facial fatigue characteristics of the driver can be more accurately captured, and effective recognition and early warning of the fatigue state are realized. According to the invention, continuous and stable monitoring can be realized in the driving process, reliable guarantee is provided for driving safety of a driver, and the risk of accidents caused by fatigue driving is reduced.

The invention discloses a multi-channel image adaptive fusion method and system for industrial product surface defect detection. The method comprises the following steps: acquiring a multi-channel image of an industrial product surface defect, and carrying out gray normalization on the multi-channel image; for the multi-channel image after gray level normalization, according to characteristics of surface defects of different industrial products, setting a corresponding feature extraction method to carry out image feature extraction; a weighting strategy based on empirical prior is adopted for the extracted image features, the weights of the surface defects of the different industrial products are adjusted, and a fusion attention map is obtained; performing multi-resolution decomposition on the multi-channel image to obtain a low-frequency component and a high-frequency component; weighted summation is carried out on the low-frequency components of different channels based on the fusion attention map, and selective fusion is carried out on the high-frequency components of different channels; and carrying out layer-by-layer reconstruction on a fusion result of the low-frequency component and the high-frequency component to generate a fusion image. According to the method, the detection precision and the visualization effect of multiple types of defects are improved while the calculation efficiency is maintained.

The invention discloses a mold quality detection method and system based on image processing, and relates to the technical field of mold quality detection.The method comprises the steps that a high-precision industrial camera is installed over a mold to obtain a mold surface gray level image, and graying and smoothing are conducted on the obtained image; constructing a distribution model according to the reference distance distribution condition of each pixel point in the smoothed image, calculating a period strength degree, preliminarily screening the pixel points according to the period strength degree, and distinguishing the possibility of texture pixel points and non-texture pixel points; by using the difference between normal texture periodicity and crack randomness, through calculating the periodicity intensity degree, screening texture pixel points and non-texture pixel points, screening suspected texture pixel points, calculating the periodicity similarity, screening out a texture region and then carrying out iterative threshold segmentation, the detection problem caused by mixing of cracks and normal textures is solved; and the detection accuracy is improved.

The invention relates to the technical field of image processing, in particular to a vehicle-mounted battery gluing defect detection method based on machine vision, and the method comprises the steps: obtaining an original image of a vehicle-mounted battery gluing region, generating color smoothness through a dual-channel variance fusing brightness and saturation, constructing structure tensor analysis to determine local gradient disorder degree, and determining the gluing defect of a vehicle-mounted battery. The method comprises the following steps of: generating abnormal confidence of pixel points by synthesizing color smoothness and local gradient disorder, remodeling gray difference among the pixel points by using the abnormal confidence as a physical modulation factor to obtain a physical remodeling weight of an edge, and realizing pseudo gradient shielding in a graph theory segmentation framework by using the physical remodeling weight to obtain a pseudo gradient shielding result. And accurate segmentation is carried out on the original image so as to realize gluing defect detection. According to the method, the complete connectivity of the glue overflow defect area is improved, the situation that the chaos texture in the defect is misjudged as the boundary is avoided, and the gluing defect detection result is more accurate.

The invention relates to the technical field of digital photoetching equipment, in particular to an LDI machine image segmentation and exposure splicing method, which comprises the following steps: carrying out primary segmentation on an original complex graph, and carrying out segmentation again after refinement processing; in the exposure process, periodically collecting a gray histogram of an exposure pattern, aligning the exposure images of adjacent areas to form a continuous exposure image, and dynamically adjusting exposure parameters by a light beam modulator according to the characteristics of each second segmentation area to realize closed-loop control on the exposure quality; all exposed images are spliced through a search algorithm and a multi-frame fusion technology, smooth transition of image edges is realized, discontinuity of local features is effectively avoided, the overall consistency and integrity of the spliced images are ensured, the imaging quality and precision are further improved, finally, the spliced images are verified, and the image quality is improved. And seamless splicing of each segmented region is ensured, so that high-quality graphic output is generated.

The invention relates to a computer vision and digital image processing technology, in particular to an image defogging method of a multi-scale convolutional neural network based on dark channel prior. Estimating the initial transmissivity and atmospheric light of the foggy image based on a dark channel prior method; the foggy image is converted into a gray level image, a gray level threshold value is selected to be used for distinguishing a sky area and other areas, and the initial transmissivity after screening is obtained; optimizing the initial transmissivity through a multi-scale convolutional neural network by adopting a coarse optimization stage and a fine optimization stage in sequence to obtain refined transmissivity; and reconstructing a fogless image according to the refined transmissivity by adopting an atmospheric scattering model. According to the method, the advantages of a physical model and deep learning are fused, the problems of supersaturation, edge artifacts, color distortion and the like in a sky region in a traditional method are solved, the PSNR (Peak Signal to Noise Ratio) of a defogged image is remarkably improved (up to 23.30), the SSIM (Subscriber Identity Module) (up to 0.978) and the like, and a robust visual enhancement scheme is provided for scenes such as automatic driving and traffic monitoring.

The invention provides a facial skin flaw enhancement method based on a Lab color space. The method comprises the following steps: firstly, acquiring an RGB face image and converting the RGB face image into a CIE Lab color space with uniform perception; then, performing differentiation treatment according to the manually selected skin flaw type: for the vascular flaw, extracting statistical characteristics of a component and driving adaptive nonlinear transformation, and generating a grey-scale map which highlights the red flaw; for pigment flaws, nonlinear transformation is carried out on the component L, and then collaborative linear weighting and feature amplification are carried out on the component L, the component a and the component b, so that a grey-scale map with highlighted pigment spots is generated. And finally, coloring the grey-scale map in the Lab color space through adjustable parameters to generate a high-contrast color enhanced image. The method overcomes the dependence on hardware and training data in the prior art, can clearly and adaptively enhance various flaws such as acnes, couperose streaks and color spots, shows robustness under different illumination, and can be widely applied to clinical beauty, later photography and real-time video processing.

The invention provides a stone gradation automatic detection method based on image processing, belongs to the technical field of image processing, and designs a two-dimensional convolution kernel capable of enhancing features according to the features that small-particle stones are bright in center, dark in edge and approximate to a circle by acquiring a gray image and a background image of a stone field through a video stream. Convolution operation is carried out on the image to improve the contrast ratio of the stone and the background, then an accurate binarized image is obtained through double-threshold segmentation and background difference processing, in order to further separate the adhered particles, the particle center is positioned by adopting distance transformation, and effective segmentation is carried out by combining a watershed algorithm. And according to the extracted particle contour, geometric parameters are calculated and the mass is estimated, so that a stone grading curve for evaluating the filling quality is automatically generated. According to the invention, rapid and non-contact automatic analysis of rockfill material grading is realized.

The invention belongs to the field of machine vision, and relates to a novel circle detection method in a complex environment. The method comprises the following steps: firstly, preprocessing an input color image, converting the color image into a grayscale image, then performing Gaussian blur processing, and applying a Canny edge detection algorithm to obtain an edge image; then, a findContours function of OpenCV is used for detecting a contour in the edge image, and traversal processing is carried out on the contour; secondly, circle detection is conducted on each contour through a findcircle () function, and circle fitting is conducted through a three-point circle determination method or a least square circle fitting method; performing accuracy verification on the fitted circle; and finally outputting the number and parameters of the identified circles, such as circle center coordinates and radiuses. The method is low in operand, can rapidly and accurately detect a circle in a complex environment with a weak light source and noise, does not need to occupy high computing resources, and has high detection speed and precision.

The invention relates to the technical field of health assessment, in particular to a traditional Chinese medicine health assessment method and system based on intelligent analysis, and the method comprises the steps: carrying out the multispectral image collection of the face and tongue picture of a user, carrying out the color gradation quantification processing and texture feature extraction of the obtained image data, and obtaining a face color gamut feature matrix and a tongue picture texture feature vector; and based on the tongue picture-complexion associated features and a syndrome description text input by the user, performing intelligent classification pre-judgment on traditional Chinese medicine syndrome types to obtain an initial syndrome label set, and based on user physique data, correcting the initial syndrome label set to obtain a syndrome feature fusion vector. Through the health fluctuation index and the health trend early warning signal, the change of the health condition of the user can be dynamically evaluated, and a personalized health intervention scheme is generated on the basis, so that the fluctuation of the health condition can be reflected in real time, intervention measures can be taken in time, and potential health risks can be prevented.