16 results about "Structuring element" patented technology

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 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.

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 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.