43 results about "Top-hat transform" patented technology

Disclosed is a copper sheet and strip surface defect detection method based an on-line sequential extreme learning machine. The method includes the following steps that a copper sheet and strip surface image is captured through an image capturing module; the captured copper sheet and strip surface image is enhanced according to the median filtering method with the masking size of 7*7 to reduce noise in the copper sheet and strip surface image and the effect of the noise on the quality of the surface image; the copper sheet and strip surface image is subject to tophat transform treatment to reduce the effect of uneven illumination; a copper sheet and strip surface image pre-detection method based on eight-neighborhood difference values is adopted; defects in the surface image are segmented according to an image segmentation method, wherein it is judged that the copper sheet and strip surface image has the surface defects after pre-detection; geometrical characteristics, gray characteristics, shape characteristics, texture characteristics and other characteristics of each defect are extracted, and copper sheet and strip surface defect characteristic dimensions are subject to optimization and dimensionality reduction according to the principal component analysis method; a copper sheet and strip surface defect classifier based on the on-line sequential extreme learning machine is designed, and samples are used for training; characteristics of the copper sheet and strip surface image to be detected are extracted to identify types of the surface defects.

The present invention relates to a system for automatically evaluating oxygen saturation of the optic nerve and retina, said system comprising: image capturing system further comprising: a fundus camera (26), a four wavelengths beam splitter (27), a digital image capturing device (28), a computer system, image processing software performing in real-time the steps of: registering set of multi-spectral images (1) by, binarizing the multi-spectral image (7), find the all the border regions of each image by finding the region including the straight line that passes the most number of points in the region (8), use the orientation of the borders to evaluate the orientation of each spectral image (9), equalize the orientation of each spectral image by rotating the spectral image (10), edge detect each spectral image (11), estimate the translation between the spectral images based on the edges of adjacent images (12), transform the images to a stack of registered images (13), locating blood vessels (2) in each of the multi-spectral images by, retrieving registered spectral images (14), for each spectral image, remove defective pixels (15), enhance image contrast (16), perform top-hat transform using structuring element larger than the largest vessel diameter (17), binarize each image (18), apply filter for smoothing the image (19), combine all the spectral images using binary AND operator (20), skeletonize the resulting image (21), prune the skeleton image (22), re-grow the pruned image (23), locate junction points (24), evaluating the width of the blood vessels (3) by calculating the normal vector to the vessel direction to, estimate the direction and position of the vessel profile, evaluate positions of vessel walls based on the vessel profile obtained, evaluate vessel width based on the position of the vessel walls, selecting samples for calculating optical density, calculating the optical density ratio (4), evaluating oxygen saturation level (5), and presenting the results (6), wherein presenting the results may include presenting numerical and visual representation of the oxygen saturation.

The invention relates to an image fusion technique, in particular to an infrared intensity image and infrared polarization image enhancement and fusion method, which can solve the inherent problems of edge distortion and low contrast in conventional infrared fused images. The method is carried out according to the following steps that support value transform is respectively carried out on an infrared intensity image and an infrared polarization image, so that low-frequency images and support value sequence images are obtained; the top-hat transform of mathematical morphology is used for respectively extracting bright information and dark information; the bright information images and the dark information images are enhanced; the two last-layer low-frequency average images and the two enhanced images are fused, so that a low-frequency enhanced fused image is obtained; inverse support value transform is carried out on the low-frequency enhanced fused image and a support value sequence image fused by a maximum method, so that a final fused image is obtained, the contrast between the targets and the background in the fused image is enhanced, and edge distortion is little.

The invention relates to an image enhancing method using multi-dimensional multi-structural element mathematical morphology, which comprises four steps: setting m structural elements in different shapes with n dimensions to be calculated, and firstly using Top-Hat conversion in the mathematical morphology for calculating image regions corresponding to m structural elements of different shapes under each dimension; then, obtaining bright regions and dark regions extracted by m structural elements under each dimension through accumulating the image regions corresponding to each structural element; subsequently, taking the maximum values of the bright regions and the dark regions under all dimensions to be used as the bright regions and the dark regions extracted by all structural elements under all dimensions; and finally, reaching the goal of image enhancement through respectively adding the extracted bright regions on original images and subtracting the extracted dark regions for enhancing the contrast grade between the bright regions and the dark regions on the original images. The invention can be widely applied to various application systems based on images, and has wide market prospect and high application value.

The invention discloses a segmenting method of a fingerprint image. The segmenting method specifically comprises the following steps: a, reading in the fingerprint image; b, quickly cutting the fingerprint image; c, transforming in reverse colour; d, equalizing; e, carrying out top-hat transformation; f, calculating the characteristic quantity in blockst; g, segmenting in blocks; h, processing the image according to morphology; and i, obtaining the segmented fingerprint image. The segmenting method of the finger image is applicable to the fingerprint image poorer in quality, can correctly segment the fingerprint, and is higher in reliability; the ISODATA (Iterative Self-organizing Data Analysis Techniques Algorithm) clustering algorithm is adopted and used for segmenting in blocks, and the clustering speed is higher; the quick cutting way is carried out, thereby the processing time is reduced; the block segmenting mode and pixel segmenting mode are combined, thus the segmented fingerprint is relatively smooth in contour; and the image equalizing mode and the top-hat transformation mode are adopted to enhance the image, and as a result, the segmenting is more efficient.

The invention discloses a hybrid remote sensing image enhancement method based on an NSST domain, which includes the following steps: S1, enhancing the overall contrast of a remote sensing image through top-hat transform and bottom-hat transform; S2, decomposing the remote sensing image of which the overall contrast is enhanced into multi-scale high-pass and low-pass sub-band coefficients throughNSST (Non-subsampled Shearlet Transform); S3, carrying out adaptive threshold de-noising on the high-frequency sub-band coefficient, and linearly enhancing the de-noised high-frequency sub-band coefficient; and S4, carrying out inverse NSST according to the high-frequency sub-band coefficient processed in S3 and the low-pass sub-band coefficient obtained in S2, and using a parameterized logarithmic image processing (PLIP) model to enhance the high-frequency sub-band coefficient and the low-pass sub-band coefficient after inverse NSST in order to highlight the edge detail information of the image. Therefore, the visual effect of the remote sensing image is significantly improved, and the sharpness of the remote sensing image is effectively enhanced.

The present invention relates to an infrared weak target detection method of utilizing sample characteristic learning classification. The method is characterized by setting an area containing a target as a positive sample and a background area as a negative sample, and obtaining enough positive and negative samples with labels from an actual infrared image, and comprises the steps of firstly analyzing the samples at the four aspects of gray distribution, edge, information entropy and energy, and extracting seven characteristics of fitting residual, center surrounding contrast ratio, the radius of an edge fitting circle, the center offset amount of the edge fitting circle, the center distance variance of the edge fitting circle, the reference information entropy contrast ratio and the texture energy contrast ratio; then selecting an optimal characteristic subset from all characteristics by the modes of package type selection and forward search and by taking the area below a subject performance curve as an evaluation index; and then extracting the optimal characteristic subset of the positive and negative samples to train a support vector machine classifier, and supervising the learning; and finally, carrying out the top-hat transform pre-processing on the image to obtain a candidate target, and discriminating and screening via the classifier to obtain a final detection result.

The invention discloses a ship wake detection method based on top hat transformation and Radon transformation, and the method comprises the steps: obtaining an original image, processing the originalimage based on the top hat transformation, and obtaining an image with improved contrast; and performing Radon transformation on the image with the improved contrast, extracting a peak point, obtaining wake position information, and marking a wake in the image. The method provided by the invention has a contrast improvement effect on both the bright wake and the dark wake, and the dark line feature of the dark wake can be converted into the bright line feature; radon transformation is carried out on the image with the improved contrast ratio, and a peak point in a Radon transformation domain is highlighted, so that the detection performance of the ship wake is improved; the method can be applied to ship wake detection in Tiangong-2 interferometric three-dimensional imaging microwave altimeter images.

The invention provides an infrared complicated background inhibiting method based on combined filtering. The method comprises the following steps of: S1, determination of the dimensions of morphological structural elements; S2, spatial background inhabitation: spatial primary inhabitation of an infrared complicated background is realized by utilizing the gray-scale morphological top-hat transform on the basis of the self-adapting structural elements; S3, non-subsampled contourlet transform: the non-subsampled contourlet first-level factoring is carried out on infrared images subjected to background primary inhibition by a space domain method, wherein a bandpass subband is factored into four high-frequency directions; S4, high-frequency coefficient reconstruction: the low-frequency influence is eliminated, and each high-frequency direction coefficient is calculated again on the basis of neighborhood average values; S5, high-frequency coefficient central vector construction: the average value of each high-frequency coefficient in each spatial position and in the four directions is calculated, and the high-frequency coefficient central vector is formed; and S6, non-subsampled contourlet domain background inhibition. The infrared complicated background inhibiting method solves the problem of infrared complicated background inhibition in typical environments, and lays a good foundation for the subsequent target detection.

The invention relates to a three-dimensional CT defect extracting and marking method for a solid engine and belongs to the field of nondestructive detection. In terms of a series of solid engine propellant hole defects, when defect segmentation is conducted, reconstructed volume data are layered firstly, operations such as filtering, morphological processing, threshold segmentation, defect extracting, defect marking and three-dimensional visualization are conducted on a set of obtained two-dimensional slice data, and defect information is extracted and marked. During morphological processing, top-hat transformation is conducted on each image layer by adopting a square structure operator which is slightly smaller than a central star hole 10. During defect extracting, a defect area is extracted by adopting the maximum connected region method. Through the three-dimensional CT defect extracting and marking method for the solid engine, hole defects in an image can be effectively segmented, extracted and marked.

The invention provides a detection algorithm for grid line offset on the surface of a cell. The detection algorithm includes the steps of performing preprocessing of image correction and image conversion on each frame of acquired image to obtain a target image of a photovoltaic cell; carrying out threshold segmentation of the target image, filling a grid line part, then carrying out shape conversion to eliminate the influence of defects at welding spots of the cell on the outline extraction, and accurately extracting the outline 1 of the edge of the cell; processing the target image by using grayscale top-hat transform to obtain a cell image only containing the grid line part; enhancing the image to eliminate the influence of a discontinuous grid line caused by the welding spots; carryingout threshold segmentation, and filling a gate line part to extract the outline 2 of the outermost peripheral gate line of the cell; and calculating the minimum distance between the outline 1 of the edge and the outline 2 of the outermost peripheral gate line of the cell, setting a threshold and comparing the minimum distance with the threshold to realize the detection of the grid line offset on the surface of the photovoltaic cell.

The invention provides a photovoltaic cell edge breakage and blunt and V-shaped notch detection algorithm, which is characterized by carrying out image correction and image conversion pretreatment oneach collected frame image to obtain a target image of a photovoltaic cell; carrying out gray top hat transform to obtain an image of the photovoltaic cell only comprising a grid line portion; carrying out threshold segmentation, and filling the grid line portion, so that influence of the internal portion of grid lines on defect detection can be eliminated; obtaining a foreground image only comprising the grid line portion, and carrying out threshold segmentation to obtain a background image; carrying out morphological close operation processing and filling the edge breakage and blunt and V-shaped notch portion; and comparing difference between the background image obtained after morphological processing and the unprocessed background image, so that detection of the edge breakage and bluntand V-shaped notch can be realized. The algorithm overcomes the defect of manual detection, can effectively improve accuracy of defect detection of the photovoltaic cell and has a huge application value for the photovoltaic industry.

The invention discloses a filtering and denoising method applied to edge detection, which comprises the following steps of: analyzing pictures of pedestrians, vehicles and obstacles on a road shot bya forward-looking camera, respectively filtering and denoising three spatial components of a color image by adopting a wavelet threshold algorithm, and graying the three recombined components by adopting a weighted average method; processing an image by adopting top hat transformation and multiple edge detection methods, and extracting edge features, so the image quality is effectively improved, and the influence of the problem of non-uniform light on recognition is weakened. The filtering and denoising method applied to edge detection provided by the invention performs edge detection on targets such as pedestrians and vehicles on a road with high efficiency and high precision, solves the influence of noise and uneven illumination on image edge detection, improves the detection precision of edge detection, provides a basis for subsequent target identification work, and is remarkable in action effect and suitable for wide popularization.

The invention discloses a two-phase flow pattern feature extraction method. The invention belongs to the field of flow pattern recognition in metallurgy and chemical engineering. the method comprisesthe following steps: firstly, by adopting an existing visualization technology, for example, acquiring a two-phase flow mixed color image by a high-speed camera; preprocessing a color image by utilizing top hat transformation and image filtering in existing software matlab; after preprocessing, extracting RGBvalues of a representative target and a representative background respectively by adoptingthe existing Colorpix software; adopting a support vector machine (SVM) algorithm to segment a target and a background in a two-phase flow image, extracting the target or the background, and finallyrealizing target image recognition through an rgb2gram function in Matlab software and binarization threshold processing, so that two-phase flow distribution is reflected more directly. According to the method, the space-time distribution diagram of the two-phase flow can be obtained more directly. The flow pattern feature extraction method is simple and short in operation time, the image recognition precision is improved, and the subjectivity of selection of an existing threshold method is also avoided.

The invention, which belongs to the field of intelligent identification in the machine vision industry, discloses a method for realizing blank page detection based on morphological transformation. Themethod is characterized by realizing automatic detection on a blank page rapidly and accurately based on the morphology. According to the basic principle, morphological top hat transformation is carried out on an original image to remove a background part of the image; mean value filtering is carried out by using a structure element with a font size; and then according to a proportion of points with grayscales exceeding a certain threshold value in the image, determining whether a current page is a blank page. According to the method disclosed by the invention, with focus on a detection speed, the method employs some effective ways with simple principles. And the experiment results show that the method disclosed by the invention has high-efficiency and quick effects, high identification precision and great practical value.

The invention discloses a method and a system for automatically extracting a defect area of an X-ray image of an automobile hub. The method comprises the steps: firstly, obtaining an X-ray image of anautomobile hub, and constructing structural elements; carrying out top-hat transformation and top-hat reconstruction transformation on the X-ray image, and carrying out expansion reconstruction operation by taking a top-hat reconstruction transformation result as a mark and a top-hat transformation result as a template; performing binarization processing on the expansion reconstruction result toobtain a preliminary defect area of the hub; performing feature elimination on the preliminary defect area to obtain a real defect area of the hub; and obtaining the minimum enclosing rectangle of thereal defect area, namely the defect area of the hub X-ray image. When the method is used for extracting the defect area of the hub, an operator does not need to specify an interested area in advance,the defect area can be directly and automatically determined on the X-ray image of the detected hub, the method and system are not influenced by links such as wheel type recognition and an area tracking matching algorithm, and the extraction efficiency and accuracy of the defect area can be greatly improved.

The invention discloses an interactive extraction method and system for a defect area of an X-ray image of an automobile hub. The method comprises the following steps: firstly, acquiring an X-ray image of an automobile hub and a minimum enclosing rectangle of a mark area on the image; constructing structural elements according to the minimum bounding rectangle; carrying out top-hat transformationand top-hat reconstruction transformation on the X-ray image by adopting structural elements, carrying out expansion reconstruction operation of mathematical morphology according to a transformation result, and carrying out binarization processing on an obtained expansion reconstruction operation result; and analyzing the binarization processing result to obtain a defect area to be extracted. By adopting the defect area interactive extraction method provided by the invention, rapid and accurate separation of the whole defect area can be achieved as long as an operator randomly marks the defectposition by using a mouse, so that the speed and the efficiency of quantitative analysis of defects by detection personnel are greatly improved, and the method and system have the advantages of simplicity and convenience in operation and accuracy in identification.

The invention relates to an unmanned aerial vehicle detection method and system based on infrared polarization. The method comprises the following steps: fusing an infrared polarization image in a polarization direction orthogonal to a background polarization angle and an infrared light intensity image subjected to adaptive contrast entropy top-hat transformation by utilizing the characteristic that the target polarization characteristic and the background polarization characteristic of the unmanned aerial vehicle have obvious difference, so that the background is suppressed, and the contrast ratio of the target and the background is improved; and finally, carrying out intuitionistic fuzzy C-means clustering on the fused image under polarization information probability induction, and detecting an unmanned aerial vehicle target in a complex background. The detection accuracy of the unmanned aerial vehicle can be improved.

The invention discloses a small target detection method based on joint edge filtering morphology in the technical field of image processing, and aims to solve the technical problems of high small target identification difficulty and low accuracy in the prior art. The method comprises the following steps: operating local pixels of an original image by using a joint edge filtering function to generate a structural element matrix of the original image; performing joint edge filtering morphological corrosion on the original image by using the structural element matrix of the original image to obtain a corroded image as a background image; performing top-hat transformation based on the original image and the background image to obtain a result image; and performing target recognition on the result image by using the adaptive threshold.

The invention provides a method for detecting appearance fingerprint defects of a polycrystalline silicon solar battery cell. The method is mainly divided into two steps: step 1, pre-processing the battery cell to obtain an interested region, wherein subsequent operation is carried out based on the interested region; step 2, identifying and marking a fingerprint region. The pre-processing step comprises the following steps: carrying out rotary correction operation on the collected battery cell so as to enable the battery cell to be parallel to a coordinate axis; then carrying out smooth processing to inhibit interference of image noises and high-frequency components; then converting an acquired RGB three-channel image into a gray level image; dividing a foreground region by adopting a fixed threshold value; then carrying out top hat conversion operation on the foreground region to eliminate interference of edges and a conveyor belt; taking an image subjected to top hat conversion processing as a mask so as to capture the interested region from the image subjected to the smooth processing.

The method for detecting edge collapse and blunt and V-shaped notches of photovoltaic cells provided by the present invention performs preprocessing of image correction and image conversion on each frame of images collected, and can obtain target images of photovoltaic cells; Cap transformation, which can obtain the image of the photovoltaic cell that only includes the grid line part, threshold segmentation, fills the grid line part, can eliminate the influence of the inside of the grid line on defect detection, obtains the foreground image that only contains the grid line part, and thresholds to segment the background The image is processed by morphological closed operation, which can fill the edge collapse, blunt and V-shaped gaps, and make a difference between the morphologically processed background image and the unprocessed background image, which can realize edge collapse, blunt shape and V The detection of type gaps overcomes the shortcomings of manual detection, and can effectively improve the accuracy of detection of defects in photovoltaic cells, which has great application value for the photovoltaic industry.

The invention discloses a sea surface infrared image enhancement method, which comprises the following steps of: inputting a sea surface infrared image to be enhanced, and performing top-hat transformation on the sea surface infrared image to be enhanced to obtain a region of interest; performing adaptive segmentation on the region-of-interest image to obtain a radiation source binarization mark image of the sea surface infrared image to be enhanced, determining a radiation source region image by using the radiation source binarization mark image, and constructing a radiation source suppression image according to the radiation source binarization mark image; determining a land-air area local illumination map and a seawater area local illumination map of the sea surface infrared image by taking a sea-sky line as a reference; constructing a local guide map according to the local illuminance map of the land-air area and the local illuminance map of the seawater area, and performing background enhancement on the sea surface infrared image to be enhanced to obtain a background enhancement map of the sea surface infrared image; and superposing the radiation source area image to the sea surface infrared image background enhancement image to obtain an enhancement result of the sea surface infrared image.

The invention discloses a multi-signal light recognition method based on image processing, comprising: (1) performing Top-Hat transformation on a target area; (2) performing grayscale threshold segmentation on the transformed target area to extract a luminous area; (3) Further screen the luminous area through geometric filtering, and mark the candidate area; (4) Train a classifier and use the classifier to classify the candidate area to determine the signal light area; (5) Determine the shape of the signal light through probability analysis. The present invention reduces the influence of halo by preprocessing the training samples, and better restores the shape characteristics of the signal lights. During the processing, the classification obtained by simulating the possible shake and other influences that may occur during the image taking process is increased. device; the present invention is entirely based on image processing, does not need to modify the existing signal light equipment, and its real-time performance is much higher than calling the algorithms for recognizing circular signal lights and recognizing arrow-shaped signal lights respectively.

A machine vision-based track obstacle detection system belongs to the field of railway safety. The black-and-white video image is obtained by the on-board camera installed on the train head, and the track is judged as straight or curved according to whether the camera is overtraveled. The curve uses a fixed high-definition camera and a wireless transmitter to receive wireless images to realize manual detection of the curve. Quasi-straight track will analyze the acquired real-time image sequence, adopt the track edge extraction algorithm based on top-hat transformation and Otsu threshold, combine the inter-frame difference method based on mathematical morphology improvement with the background difference method and the corner feature matching tracking algorithm, real-time The obstacles in front are divided into two categories: non-dangerous (stationary small obstacles, moving obstacles that quickly cross the track) and dangerous (stationary large obstacles, moving obstacles that affect the passage of trains). The invention can quickly process track images, can extract track edges more effectively and accurately, and has higher detection accuracy for track obstacles.

The present application discloses a method for detecting foreign objects in a cover glass module and a related device, which are used to improve the detection accuracy of foreign objects in the cover glass module. The method of the present application includes: setting a camera, a cover glass module, and a light source; turning on the second light source to take pictures of the cover glass module; turning on the first light source to take pictures of the cover glass module; performing median filtering on images I0, I1 and I2; Image IF0, image IF1, and image IF2 are subjected to gray-scale morphological bottom-hat transformation and binarization processing; image I1 and image I2 are respectively subjected to gray-scale morphological top-hat transformation and binarization processing; Perform OR, AND processing, and morphological reconstruction of the four foreign object images, the second foreign object image, and the third foreign object image; perform OR, AND processing, and size selection on the first foreign object image, image IF1, second foreign object image, and image IF2 Processing and morphological reconstruction; generation of OCA dissection foreign body images and screening detection.

The present invention proposes a method for image classification, recognition and judgment, comprising the following steps: step 1, collecting images, preprocessing the images, removing image noise through the top-hat transformation method, and then using histogram equalization to enhance the images; step 2 , perform feature extraction on the enhanced image to obtain a joint histogram of N-dimensional vectors, perform dimensionality reduction operations on N-dimensional vectors, and the N is a positive integer; step 3, for the N-dimensional vectors of dimensionality reduction operations, pass the training set The classification set is summarized through training, and feature images are extracted through classification or screening.