30 results about "Structuring element" patented technology

The invention relates to the technical field of image processing, and discloses a morphology-based image texture feature extraction method, which comprises the following steps: an image preprocessing step: carrying out graying processing on an original image, and carrying out smoothing processing on a grayscale image by using a Gaussian filter; a morphological operation step: selecting structural elements suitable for image texture characteristics, performing expansion and erosion operation on the smoothed image, performing opening operation on the expanded image, and performing closing operation on the eroded image; and an adaptive feature selection step: carrying out local binary pattern feature extraction on the image after morphological processing, adaptively determining a threshold according to statistical distribution of LBP features, and segmenting the image into a foreground and a background. The image texture feature extraction method based on morphology aims at solving the problems that feature extraction is inaccurate, noise interference is likely to happen, and feature combination is complex in an existing image texture feature extraction method.

The invention belongs to the technical field of image processing, and provides an image processing method and an image processing terminal, and the method comprises the following steps: obtaining an original binary image from an image source, and carrying out the preprocessing of the original binary image; performing run-length coding on the preprocessed binary image to obtain an image data result after run-length coding; performing morphological processing on the image in the row direction by using the structural element matrix; re-integrating each row of data processed in the row direction into a complete image representation, and performing morphological processing in the column direction; taking a processing result in the column direction as a binary morphological calculation result of the original binary image; according to the method, the image is independently processed in the row direction and the column direction, specific shapes and structures can be more flexibly extracted and enhanced, proper structural elements are adopted according to specific application requirements, and the processing effect can be optimized; by using the run-length coding to represent the image, the overhead of data storage and transmission can be significantly reduced, and the processing speed is improved.

The invention discloses a bolt surface defect detection method and system based on computer vision. The method comprises the steps that 360-degree image collection and preprocessing are conducted on a bolt through an annularly-arranged multi-camera array; fourier transform is adopted to analyze thread periodic texture features, and an adaptive filter is designed to enhance the image; multi-scale morphological operation is adopted, and morphological gradient features are extracted by using structural elements for crack, pit and scratch type defects; a self-adaptive threshold segmentation method based on local statistical characteristics is combined with thread direction information to dynamically adjust a segmentation threshold, and defect candidate areas are segmented; and extracting geometric and textural features of the candidate region, and identifying the type, position and severity of the defect by adopting a random forest classifier. According to the invention, high-precision automatic detection and classification of various defects on the surface of the bolt are realized.

The invention relates to the technical field of liquid level detection, and discloses a liquid level detection method and system based on image processing, and the method comprises the steps: obtaining to-be-processed original liquid level image data; preprocessing the data of the original liquid level image, wherein the preprocessing comprises the steps of extracting a red channel from the original liquid level image, carrying out binarization processing on the image based on double thresholds, carrying out expansion on a binary image by using a square structure element, carrying out opening operation and closing operation on the expanded image, carrying out corrosion operation on the image subjected to the closing operation, and carrying out iteration for three times; and carrying out feature extraction on the preprocessed image, calculating a minimum bounding rectangular frame, drawing a rectangular frame in the original liquid level image, and judging the liquid level height according to the height of the rectangular frame. Compared with the prior art, dual threshold segmentation and multi-scale morphological processing are innovatively combined, and the detection robustness under the complex illumination condition is improved.

The invention belongs to the technical field of computer vision and artificial intelligence, and discloses a handwritten mathematical formula primitive feature enhancement method based on a self-adaptive symbol strategy, which comprises the following steps of: 1, dynamically scaling primitive scale matching: realizing undistorted scaling by keeping the aspect ratio of an original image, only fixing the height and dynamically calculating the width to obtain a primitive scale matching result; adaptive interpolation is carried out; step 2, pixel disturbance of the morphological filtering chain: isolated noise points are gradually eliminated through the multi-stage morphological filtering chain, broken strokes are repaired, and large-area background interference is removed; and the optimal structure elements are adaptively selected for different symbol types. According to the method, dynamic scaling of geometric constraints is combined with intelligent morphological filtering, so that the core problems of symbol deformation, noise interference, multi-source data difference and the like in handwritten mathematical formula recognition are solved, and the accuracy of a recognition system is remarkably improved. And an efficient and reliable preprocessing scheme is provided for education AI, intelligent correction and other scenes.

The application discloses a bolt surface defect detection method and system based on computer vision, comprising: 360-degree image acquisition and preprocessing of the bolt through a multi-camera array arranged in a ring; Fourier transform is used to analyze periodic texture features of the thread, and an adaptive filter is designed to enhance the image; multi-scale morphological operation is used to extract morphological gradient features using a structure element for crack, pit and scratch type defects; an adaptive threshold segmentation method based on local statistical features is used to dynamically adjust the segmentation threshold combined with thread direction information to segment out the defect candidate region; geometric and texture features are extracted for the candidate region, and a random forest classifier is used to identify the defect type, position and severity. The application realizes high-precision automatic detection and classification of various defects on the bolt surface.

The invention provides a protection pressing plate state judgment method based on OpenCV, and the method comprises the steps: A, sequentially carrying out the gray conversion, Gaussian blur contour detection, Canny edge extraction, structural element denoising and contour refinement description of a protection pressing plate opening and closing state template image, and obtaining a template refinement contour; step B, executing the processing flow of the step A on the to-be-identified protection pressing plate image to obtain a to-be-identified refined contour; and step C, calculating the 1-7-order Hu invariant moment similarity of the refined contour of the template and the refined contour to be identified, and judging the opening or closing state of the protection pressing plate based on the 1-7-order Hu invariant moment similarity and a preset threshold value. According to the invention, the problems of low efficiency and insufficient accuracy during substation secondary screen cabinet inspection are solved.

The present invention provides an object detection method, device and medium based on multi-scale morphological operators, belonging to the field of object detection in image processing. It includes: processing a single-frame image to obtain a preprocessed image; performing multi-scale morphological dilation operation pixel by pixel to obtain the dilation result and erosion result of each pixel under each scale structuring element; constructing a gray-scale transformation index using the dilation result and erosion result, and marking the scale of the structuring element with the smallest gray-scale transformation index value as the optimal scale; calculating the mean value of the pixel points corresponding to the optimal scale structuring element; for each pixel point, constructing a morphological feature value using the mean value, dilation result, and the gray value of the pixel point, and determining whether the pixel point is a target. The multi-scale morphological operation of the present invention has the characteristics of high detection probability and low false alarm compared with traditional methods, and can effectively alleviate false alarm events caused by local gray-scale changes.

The invention provides a luminous character defect detection method, which comprises the following steps of: after obtaining a difference chart of luminous characters, extracting each independent difference area in the difference chart, generating morphological structure elements with corresponding sizes and orientations based on a minimum enclosing rectangle of each independent difference area, and respectively adopting each morphological structure element to detect the defects of the luminous characters. Performing morphological closed operation on the difference image, extracting each independent difference region from the difference image after the morphological closed operation, and judging whether the defect is a pseudo defect or not based on the geometrical characteristics of each independent difference region; in this way, pseudo defects in the difference image are recognized and eliminated, the problem of defect false detection caused by glittering character edge burrs, registration errors and the like is solved, and the detection accuracy is remarkably improved.

This invention relates to the field of image data processing technology and discloses an image data processing method for detecting minor defects on the surface of food. The method includes: acquiring an original grayscale image as a reference mask matrix; performing morphological erosion on the mask matrix using structuring elements to generate a basic label matrix; calculating the gradient magnitude matrix of the reference mask matrix; weighted superposition of the gradient magnitudes onto the basic label matrix to perform grayscale gain compensation, generating an adaptive label matrix; performing restricted morphological reconstruction using the reference mask matrix as a constraint and the adaptive label matrix as a seed to generate a background fitting matrix; and extracting the residual image through differential operations. This invention effectively solves the frequency domain aliasing problem between natural textures and minor defects through a gain compensation mechanism based on gradient magnitude, achieving accurate detection of minor defects while suppressing complex texture backgrounds.

The invention relates to the technical field of image enhancement, in particular to a blade scratch recognition method based on image enhancement, which comprises the following steps of: defining a texture sliding window with the size of N * N according to a to-be-detected blade grayscale image, and controlling the texture sliding window to carry out pixel-by-pixel traversal on a pixel coordinate system of the to-be-detected blade grayscale image, and calculating and generating a local entropy value. According to the method, the fused matrix is processed through morphological closed operation, the discontinuous high-response area is connected through the structural elements, internal holes are filled, the scratch fracture phenomenon caused by uneven illumination or metal reflection is repaired, and the completeness and continuity of the defect form are ensured. And performing connected domain marking based on a binary mask, extracting geometric parameters of an external rectangle, strictly screening the connected domains according to a length-width ratio form standard, and removing non-strip interference noise such as spots and pits, so that high-precision positioning of fine scratches on the surface of the blade is realized.

The present application belongs to the technical field of image processing, and particularly relates to a surface oxidation detection method and system for overhead strand, which comprises the following steps: obtaining gradient field data of the overhead strand image; determining the local main direction entropy of the pixel point based on the angle difference between the gradient direction of all neighborhood pixel points of the pixel point and the local main direction, and determining the entropy threshold through clustering to screen anchor points; constructing a gradient profile line along the local main direction of the anchor point, determining the local rust width of the anchor point according to the gradient amplitude change of the pixel points on the gradient profile line; obtaining the adaptive structure element parameters based on the local rust width and the local main direction of the anchor point, and performing morphological top-hat transformation to calculate the response value of the anchor point, and marking the anchor points with a response value greater than the response threshold as oxidation defects. The present application significantly improves the accuracy of overhead strand surface oxidation rust recognition by adaptively matching the local geometric morphology of the rust.

This invention discloses a method, apparatus, device, and medium for morphological operations based on local density maps, relating to the field of image processing technology. The method includes: calculating a local density map based on a target image; using the local density map to characterize the local grayscale distribution features of each pixel; assigning structural element parameters of different sizes to different regions based on the local density map; performing morphological operations on the target image using structural element parameters of different sizes to obtain multiple morphological operation results; and fusing the multiple morphological operation results based on the local density map to obtain the final morphological operation result. This invention accurately perceives local image features through local density maps, adaptively assigns structural element parameters of different sizes to different regions, and organically combines the processing advantages of different parameters through a fusion strategy. While maintaining high computational efficiency, it effectively balances detail preservation and noise suppression, significantly improving the processing quality of morphological operations.

The application discloses a paperboard warping degree detection method and device, and relates to the technical field of paperboard surface flatness detection.The paperboard warping degree detection method comprises the following steps of image acquisition, image preprocessing, reflection feature extraction, edge detection, morphological operation, contour analysis and warping degree calculation; the image acquisition is performed by means of a light source and a camera located above the paperboard to be detected to acquire image information of the paperboard; the image preprocessing is performed by converting the acquired image information into a gray-scale image, removing noise and then performing histogram equalization to enhance contrast; the reflection feature extraction is performed by calculating the brightness gradient in the horizontal and vertical directions to obtain a reflection change intensity map, and then performing binarization processing to obtain a reflection feature map; the edge detection is performed by adopting a Canny algorithm to extract a basic edge, and then performing a morphological closing operation on the reflection feature map, and then performing a bit or operation on the basic edge to obtain an enhanced edge; and the morphological operation is performed by performing corrosion operations on the enhanced edge by using different structure elements in sequence to obtain an optimized edge image.

The invention discloses an image corrosion method and device, electronic equipment and a storage medium, and the method comprises the steps: traversing an obtained current image and all runs of a structural element, generating a basic run, and marking the serial number of the run of the structural element corresponding to the basic run, the basic run is a result of executing corrosion operation on the run of the current image and the run of the structural element; the basic runs are traversed line by line, if the number of the basic runs of the current line is smaller than M, or the number of the basic runs of the current line is larger than or equal to M and the structural element run indexes are incomplete, all the basic runs of the current line are deleted, and M is an integer larger than 1; otherwise, in the current row, performing intersection operation on the basic run according to the structural element run index, and searching the public area of the structural element run index. By adopting the technical scheme, the Blob image corrosion method based on run length coding is introduced, so that the Blob analysis processing speed and efficiency are efficiently and remarkably improved.

The invention discloses an image corrosion method and an image processing device based on run-length coding, and relates to the technical field of image processing.The image corrosion method comprises the following steps that according to the corrosion method based on run-length coding, a binary image and a specified structural element are converted into a target area and a structural element area; and directly verifying whether all the stroke line segments of the structural element are completely included by the corresponding strokes of the target area or not, so as to determine the retention of the anchor point strokes and complete the efficient corrosion of the target area. According to the method, dense calculation of traditional pixel-level convolution is effectively avoided on the basis of run-length coding, the corrosion calculation efficiency in a high-resolution image or a scene using a large structural element is improved, and therefore the strict real-time requirement of industrial application for the processing speed is met.

This application discloses a method, apparatus, equipment, and medium for constructing digital cores of shale, relating to the field of oil and gas exploration and development technology. The method includes: acquiring FIB-SEM and MAPS images of a target shale sample; segmenting different components in the FIB-SEM image based on the grayscale gradient and grayscale values ​​using a maximum entropy algorithm, and generating a target 3D image corresponding to the target shale sample; optimizing the pore size of the target 3D image based on the MAPS image to obtain an optimized 3D image; performing morphological erosion and dilation operations on the pores in the optimized 3D image using preset structuring elements and determining the corresponding pore boundaries using a Boolean superposition algorithm; and then determining the corresponding pore type based on the distribution of different components at the pore boundaries to complete the construction of a digital core for the target shale sample. This method enables automatic segmentation of multiple components in shale samples and identification of pore types in three-dimensional space.

The invention relates to the field of playing digitization, in particular to a key recognition method based on key seams, an intelligent musical instrument, equipment and a medium. The method comprises the steps of obtaining a first view image; intercepting a plurality of first local images of areas where the second-class keys are located from the first view image according to a key image area where the first-class keys are located; identifying a plurality of initial inter-key gaps between adjacent two types of keys in the first local image, and performing morphological operation on the initial inter-key gaps to obtain a target local image; identifying a plurality of second-class keys from the target local image; the morphological operation comprises the following steps: obtaining a target second-class key adjacent to a to-be-processed initial inter-key gap, and updating setting parameters of structural elements based on perspective deformation parameters of the target second-class key; and performing morphological operation on the to-be-processed initial inter-key gap based on the updated structural element. The efficient utilization of computing resources and the dual optimization of identification precision are realized, and the anti-interference performance of identification is effectively improved.

The invention relates to the technical field of image processing, and discloses a method and a system for realizing two-dimensional appearance detection of a PCBA (Printed Circuit Board Assembly) by a multi-scale image processing method.The method comprises the following steps: acquiring a multi-scale appearance image of the PCBA, and performing deep semantic fusion to obtain a reconstructed appearance image; performing wavelet frequency domain feature coding on the reconstructed appearance image to perform image adaptive enhancement on the multi-scale appearance image to obtain an enhanced appearance image; performing background separation on the enhanced appearance image to obtain a background separation image, and performing positive and negative structural element sliding processing on the background separation image to obtain a morphological processing image; and carrying out region segmentation on the morphological processing image to obtain a target attention region, identifying a multi-scale texture change degree of the target attention region, and carrying out appearance analysis on the PCBA to obtain an appearance detection report. According to the invention, the appearance detection accuracy of the PCBA can be improved.

This disclosure relates to the field of OCT image processing technology, specifically to an OCT image scale processing method, apparatus, and electronic device. The method includes: acquiring a scale region image containing the scale from an original OCT image; preprocessing the scale region image to obtain a preprocessed image; performing adaptive binarization processing on the preprocessed image based on a preferred threshold to obtain an optimized binary image; performing morphological opening operations on the optimized binary image based on a preset structuring element to obtain an enhanced binary image; searching for and extracting contours from the enhanced binary image to obtain an original contour set; selecting the optimal candidate contour corresponding to the scale from the original contour set; and obtaining the long side ratio and short side ratio of the scale based on the preset standard physical size of the image scale and the geometric parameters of the minimum circumscribed rectangle of the optimal candidate contour. This allows for rapid and accurate identification and processing of the OCT image scale, achieving high-precision scale conversion.

The application discloses a hyperspectral anomaly detection method based on space spectrum feature fusion, which comprises the following steps: step one, performing principal component analysis on a hyperspectral image to obtain a principal component image; step two, performing a morphological closing operation on the principal component image by using a computer and multiple structure elements to obtain a closing operation feature image; step three, performing a morphological opening operation on the principal component image by using a computer and multiple structure elements to obtain an opening operation feature image; step four, fusing the closing operation feature image, the principal component image and the opening operation feature image to obtain a feature matrix based on space spectrum feature fusion; and step five, performing anomaly score calculation on the feature matrix based on space spectrum feature fusion by using a computer and a Reed-Xiaoli algorithm, and then obtaining anomaly points and background points in the hyperspectral image according to the anomaly scores. The method is simple in steps and reasonable in design, and effectively improves anomaly detection precision by fusing spectral features and spatial features.

The invention provides an image target boundary extraction method, device and equipment based on morphological difference, and is applied to the technical field of image processing. The method comprises the following steps: acquiring a closing operation result and an opening operation result of an initial electronic scanning image corresponding to a preset morphological structure element; taking a difference value between the initial electronic scanning image and the closing operation result as a white cap operator, and taking a difference value between the opening operation result and the initial electronic scanning image as a black cap operator; enhancing the initial electronic scanning image according to the white cap operator and the black cap operator to obtain a first enhanced image; performing gray correction on the first enhanced image to obtain a second enhanced image; dividing the second enhanced image in a preset angle range according to interval angles to obtain structural elements in all directions; and generating a target boundary extraction result according to the initial boundary extraction results corresponding to the structural elements in each direction and the weights corresponding to the structural elements in each direction. And the extraction effect and efficiency of image target boundary extraction are effectively improved.

The invention discloses a bearing fault diagnosis method based on improved time-varying mathematical morphology filtering. The method comprises the following steps: collecting a vibration acceleration signal of a bearing; constructing improved time-varying structure elements with different orders; determining a mathematical product morphological operator with excellent performance; performing mathematical morphology filtering processing on the collected vibration acceleration signals to obtain a series of mathematical morphology filtering signals; selecting an optimal mathematical morphology filtering signal from the obtained mathematical morphology filtering signals; and performing power spectrum analysis on the optimal mathematical morphology filtering signal to judge whether the bearing has a fault and the fault type. The problem that in the prior art, under the strong background noise interference condition, time-varying structural elements are prone to noise interference, pulse features are difficult to accurately match and extract, and fault diagnosis of a bearing in the noisy industrial environment is not facilitated is solved. The improved time-varying structural element capable of eliminating interference of noise on the shape and length of the structural element is provided for bearing fault diagnosis, and the method has the advantages of being excellent in anti-noise performance and sensitive to fault pulse characteristics.

The application discloses a signal fluctuation extraction method combining a Hadamard matrix and mathematical morphology, and comprises the following steps: determining the order m of the Hadamard matrix and the specific number l of rows to be selected according to signal characteristics and sampling accuracy; taking the lth row of the selected Hadamard matrix as a structure element g to perform mathematical morphology operation on an input signal f to obtain a signal fluctuation part and complete the extraction operation. According to the application, a certain row in the Hadamard matrix is selected as the structure element according to signal characteristics, and the order of the matrix is determined; the characteristics that the Hadamard matrix only takes values of 1 and -1 are utilized; the fundamental component in the signal is suppressed through the morphological operation; and the fluctuation part is extracted. The orthogonality of the Hadamard matrix is fully utilized, the Hadamard matrix is used as the structure element for the morphological transformation, the signal can be processed simply and quickly, and the characteristic information is extracted. The application of the mathematical morphology can accurately analyze the signal in the time domain.

Methods and systems are provided herein for generating a morphology-based composition based on an input image, the morphology-based composition being a final image generated by performing one or more white top hat (WTH) transforms on the input image, generating two or more image layers based on two or more WTH transformed images, and scaling adjacent image layers based on one or more scaling factors, each scaling factor being based on an estimated point spread function (PSF), two structure element sizes, and standard deviations of the estimated point spread function (PSF) and image data.

The application relates to the field of digital performance, in particular to a key recognition method based on key gaps, an intelligent musical instrument, equipment and a medium. The method comprises the following steps: acquiring a first field of view image; cutting a first local image of a region where a second type of key is located from the first field of view image according to a region where a first type of key is located; recognizing a plurality of initial key-to-key gaps between adjacent second type of keys in the first local image, and performing morphological operation on the initial key-to-key gaps to obtain a target local image; recognizing a plurality of second type of keys from the target local image; the morphological operation comprises the following steps: acquiring a target second type of key adjacent to an initial key-to-key gap to be processed, and updating the setting parameters of a structure element based on the perspective deformation parameters of the target second type of key; and performing morphological operation on the initial key-to-key gap to be processed based on the updated structure element. The dual optimization of efficient use of computing resources and recognition accuracy is realized, and the anti-interference of recognition is effectively improved.

The invention belongs to the technical field of image processing, and particularly relates to a surface oxidation detection method and system for an overhead stranded conductor, and the method comprises the steps: obtaining the gradient field data of an overhead stranded conductor image; based on the included angle difference between the gradient direction and the local main direction of all neighborhood pixel points of the pixel points, determining the local main direction entropy of the pixel points, and determining an entropy threshold through clustering to screen anchor points; a gradient section line is constructed in the local main direction of the anchor point, and the local rust width of the anchor point is determined according to gradient amplitude changes of pixel points on the gradient section line; and on the basis of the local rust widths and the local main directions of the anchor points, self-adaptive structure element parameters are obtained, morphological top-hat transformation is executed to calculate response values of the anchor points, and the anchor points with the response values larger than a response threshold value are marked as oxidation defects. According to the method, the accuracy of identifying the oxidation rust on the surface of the overhead stranded wire is remarkably improved through self-adaptive matching of the local geometric morphology of the rust.

The present application relates to the technical field of image processing, and particularly relates to a die forging gear metal grain size detection method and system, the method comprising: acquiring a noise reduction image of a metallographic image of a die forging gear, calculating boundary confidence of a pixel point to constitute a boundary confidence map, determining average radius and structure element of a grain based on the boundary confidence map, adopting the structure element to perform morphological reconstruction on the noise reduction image to obtain a binary mask image, converting the binary mask image into a signed distance function as an initial level set function of an energy function evolution process of a guided Chan-Vese model; determining a spatially adaptive region separation force weight and a boundary adsorption force weight, reconstructing an energy function of the Chan-Vese model, iteratively updating the initial level set function to minimize the reconstructed energy function, taking a zero level surface of a level set function corresponding to energy function convergence as a grain boundary contour and calculating a grain size grade, and completing grain size detection. The method improves the accuracy of grain size detection.

The present application relates to the technical field of medical image processing, in order to solve the technical problem of high false positive caused by the existing detection segmentation method, the present application discloses a CT image lung nodule automatic segmentation method and device based on mathematical morphology, including the steps of obtaining original data, lung parenchyma segmentation, mathematical morphology preprocessing, high signal region extraction, mathematical morphology processing, false positive nodule exclusion and three-dimensional reconstruction. The mathematical morphology processing step performs a mathematical morphology opening operation on the high signal region with a structure element radius similar to the maximum intrapulmonary vessel radius, obtaining a potential nodule image region. The false positive nodule exclusion step removes false positive nodules from the potential nodule image region through geometric features and threshold processing. Mathematical morphology operation is fast, and can realize fast lung nodule detection and segmentation operation. Geometric features and threshold processing can effectively remove most false positive objects.

The invention discloses a paperboard warping degree detection method and device, and relates to the technical field of paperboard surface flatness detection. The paperboard warping degree detection method comprises the steps of image acquisition, image preprocessing, reflection feature extraction, edge detection, morphological operation, contour analysis and warping degree calculation. Image acquisition: acquiring image information of the paperboard through a light source and a camera positioned above the to-be-detected paperboard; preprocessing the image, converting the collected image information into a grey-scale map, removing noise, and performing histogram equalization to enhance the contrast; reflection feature extraction: calculating brightness gradients in horizontal and vertical directions to obtain a reflection change intensity graph, and carrying out binarization processing to obtain a reflection feature graph; edge detection: extracting a basic edge by adopting a Canny algorithm, carrying out morphological closing operation on the reflection characteristic pattern, and then carrying out bit OR operation with the basic edge to obtain an enhanced edge; and performing morphological operation, and sequentially performing corrosion operation of different structural elements on the enhanced edge to obtain an optimized edge image.