13 results about "Gray level histogram" patented technology

The invention belongs to the technical field of image processing, and particularly relates to a boundary intrusion target detection method based on machine vision, and the method comprises the steps: obtaining a boundary monitoring video frame sequence, extracting a grayscale image, and constructing a background model image based on a historical frame sequence; according to the gray scale change of the pixel point in the time window, a time sequence fluctuation index is obtained through the standard deviation of the time sequence gray scale set and the overturning frequency of the gray scale difference value; according to texture distribution characteristics in a pixel point neighborhood, obtaining a spatial discrete entropy through a gray level histogram; obtaining a direction chaos index through the vector sum modulus and the algebraic modulus sum of the motion gradient vector; and correcting the original motion response diagram according to the time sequence fluctuation index, the spatial discrete entropy and the direction chaos index to obtain an intrusion confidence coefficient, and carrying out connected region detection according to the intrusion confidence coefficient. According to the method, the environmental dynamic interference is effectively inhibited by fusing the time sequence oscillation, the space texture and the motion direction, and the accuracy of boundary intrusion detection is improved.

The invention relates to the field of in-vitro diagnosis, and discloses a peripheral red blood cell image segmentation method based on self-adaption and edge enhancement, and the method comprises the steps: obtaining a to-be-segmented red blood cell image; performing contour enhancement on the to-be-segmented red blood cell image to obtain a target red blood cell image; calculating a gray level histogram of the target red blood cell image, and extracting an envelope line in the gray level histogram; determining two main wave crests on the envelope line and a wave trough between the two main wave crests through a maximum value filtering method; performing threshold segmentation on the target red blood cell image by taking the pixel value corresponding to the trough as a threshold to obtain a binary image; carrying out segmentation point detection and matching on the binary image, and connecting the matched segmentation points to segment independent red blood cells; and performing ellipse fitting on the independent red blood cells to optimize the contour of the red blood cells. According to the method disclosed by the invention, the problem of effectively segmenting the red blood cells is solved, so that the adhered red blood cells and low-pigment red blood cells are effectively segmented out.

The invention provides a soft tissue adaptive enhancement method and enhancement device for a skull side position film, and the method comprises the steps: calculating a binarization threshold value of a skull side position image according to a gray level histogram and a cumulative distribution function, and generating a binary image of the skull side position film according to the binarization threshold value; extracting a mouth and nose soft tissue region from the binary image; carrying out local self-adaptive dynamic enhancement on the extracted mouth and nose soft tissue area; and carrying out weighted fusion on the enhanced mouth and nose soft tissue area image and the original image of the skull side position film to form a soft tissue enhanced image of the skull side position film. According to the method, the binarization threshold value of the skull side bitmap is calculated by fully utilizing the rule of the brightness range of the mouth-nose soft tissue area, the mouth-nose soft tissue area image can be accurately positioned, and through the local self-adaptive dynamic enhancement and image fusion technology, on the premise that gray level distribution of other anatomical structures is not damaged, the image fusion precision is greatly improved. The visual effect of the mouth and nose soft tissue area image is obviously improved, and more reliable image support is provided for clinical diagnosis.

The invention relates to the technical field of power transmission line detection, in particular to a nondestructive detection method and system for a power transmission line. The unmanned aerial vehicle is used for putting the airborne nondestructive testing robot to the power transmission line through the special mounting device, and the unmanned aerial vehicle is adaptive to various line structures and the number of sub-wires. And the ground remote workstation drives the robot to move through 5G communication and carries out X-ray detection to obtain a high-resolution image. Based on the normalized matching degree of the X-ray image of the to-be-detected crimping fitting and the corresponding structural feature template, determining an adaptive denoising threshold value of wavelet transform, denoising the X-ray image, and segmenting the X-ray image into a steel core area and an aluminum pipe area through gray histogram analysis; and carrying out local adaptive enhancement by adopting a multi-scale Retinex algorithm, and obtaining a nondestructive testing result of the power transmission line through a multi-task defect identification network fused by attitude correction and a geometric attention mechanism. The nondestructive testing precision of the power transmission line is effectively improved.

The invention discloses an image background region recognition method and device, a storage medium and a product, and relates to the technical field of image processing, and the method comprises the steps: calculating a gray histogram of a to-be-recognized image, carrying out the peak detection of the gray histogram, and obtaining candidate peaks; performing validity judgment on each candidate peak to obtain an effective peak; and determining whether the to-be-identified image has a background area according to the number of the effective peaks and a background determination threshold. Through calculation of a gray level histogram and peak detection, peak value structure characteristics of image gray level distribution can be accurately obtained, a basis is provided for background existence judgment, effectiveness judgment is performed on candidate peaks, false peaks caused by noise interference can be eliminated, a peak value analysis process is more stable, and through comparison of the number of effective peaks and a background determination threshold value, the peak value analysis accuracy is improved. The existence state of the background area in the image is recognized, whether the image contains the background area or not is accurately judged under the scene that the imaging condition changes and the background existence state is uncertain, and the accuracy and robustness of background segmentation are improved.

The present application belongs to the technical field of image processing, and particularly relates to a radiator weld defect recognition method and system, which comprises the following steps: collecting a radiator image, segmenting a weld area and obtaining a center axis by clustering, position analysis and center axis extraction; in the weld area: calculating the structure deviation and texture difference of each pixel point; determining the defect suspicion degree of each pixel point by fusing the two indexes, counting the gray level histogram of the weld area, including the frequency of each gray level; calculating the defect suspicion degree of each gray level, weighting the frequency of each gray level by using the defect suspicion degree of each gray level; performing histogram equalization based on the weighted frequency, and positioning the defect area by threshold segmentation. The present application changes the image enhancement process from global optimization to focusing on the suspected defect area, thereby improving the recognition accuracy of low-contrast weld defects.

The present application relates to the technical field of flaw testing, in particular to a tension clamp forging flaw testing system and method based on optical means, which comprises a vibration detection unit, a fuzzy mapping unit and a segmentation testing unit. The conversion mapping module of the present application performs gray level histogram statistics on the de-fuzzed tension clamp image, calculates the cumulative distribution value of the gray level, linearly maps the cumulative distribution value of the gray level to the target gray level range, generates a new gray level after rounding, realizes image equalization through pixel gray level replacement, enhances the image through image equalization, makes the detailed features on the surface of the tension clamp more obvious in the equalized image, reduces the covering of the detailed features on the surface of the tension clamp by the rough background in the de-fuzzed tension clamp image, increases the difference between the gray level of the detailed features on the surface of the tension clamp and the surrounding rough background area, and uses the gray level to distinguish the detailed features and the rough background to increase the accuracy of the flaw testing.

The invention discloses an intelligent image defect repairing method based on deep learning, and relates to the technical field of image processing, and the method comprises the steps: converting an RGB image into an image of a logarithmic space; de-noising processing is carried out on the image, and smoothing processing is carried out on the image by using a filter; simulating a human eye vision system, and separating reflection components in the original image to obtain a reflection component image; carrying out linear normalization processing and nonlinear mapping on the reflection component image; performing color recovery, edge sharpening and contrast improvement processing on the reconstructed image; carrying out remapping on the gray level histogram of the image; and carrying out effect evaluation on the optimized image, adjusting algorithm parameters according to an evaluation result, improving image quality, and obtaining a target image. Through an image enhancement algorithm, a logarithmic space conversion technology, a denoising and smoothing processing technology and a histogram equalization technology, an original RGB image is optimized and repaired, so that a user obtains better visual experience.

The invention relates to the technical field of machine vision detection, and discloses a programmed automatic exposure method and system for machine vision long-distance displacement monitoring, and the method comprises the steps: obtaining a template picture corresponding to each target, and calculating a dark space ratio; the method comprises the following steps: capturing a camera video stream, performing standardized scaling on an image region of interest, and generating a gray histogram through transverse splicing or weighted accumulation; analyzing the grey level histogram, determining a dark area end point and a dark and dark area peak value based on a dark area proportion, calculating a boundary score and a double-peak difference value score, and weighting to obtain a final score; and determining an optimal exposure value and an optimal gain value step by step to form a program type automatic exposure parameter. The system corresponds to the method. By adopting the method and the device, the dynamic automatic optimization of the exposure parameters is realized, the stable imaging quality of the target is ensured, and the precision and the reliability of machine vision long-distance displacement monitoring are effectively improved.

The application discloses an image enhancement method and device, relates to the technical field of image enhancement processing, and is used for improving the effect of image enhancement. The method comprises the following steps: acquiring a gray level histogram of a luminance component of a current image frame; grouping the gray level histogram according to a human eye luminance sensitivity curve to obtain at least two histogram groups; the at least two histogram groups comprise a first histogram group and a second histogram group, the maximum gray level value corresponding to the first histogram group is smaller than the minimum gray level value corresponding to the second histogram group; determining a target stretching relationship according to stretching steps of the at least two histogram groups and the gray levels corresponding to the at least two histogram groups; the target stretching relationship is used for representing the relationship between the gray level corresponding to the current image frame and the gray level corresponding to a target image frame; the target image frame is an image frame obtained after the current image frame is enhanced; and the target image frame is obtained based on the target stretching relationship.

The embodiment of the application discloses a kind of magnetic resonance image gray histogram matching method and device, first, establish multiple image data sets, respectively according to the image of all samples in each image data set, the average gray histogram of corresponding image data set is generated.For each image data set, Gaussian process regression model between imaging parameter combination and average gray histogram is established.Then, the image to be matched and the physical parameter data and imaging parameter combination data of the image to be matched are obtained, the Gaussian process regression model corresponding to the image to be matched is determined according to the physical parameter data of the image to be matched.The imaging parameter combination data of the image to be matched is substituted into corresponding Gaussian process regression model, and the approximate gray histogram of the image to be matched is generated.Finally, a preset template histogram is obtained, and the gray level mapping function of the image to be matched is established using the approximate gray histogram and the template histogram.The gray level mapping function is used to correct the gray level of the image to be matched.

The application provides a license plate intelligent recognition method and system based on image enhancement, and belongs to the technical field of image processing. The method performs Fourier transform on license plate image data to determine the spectral distribution characteristics; then, a gray level histogram is constructed based on the characteristics, and when the gray level variance exceeds the preset variance threshold, it is determined that the license plate is affected by light changes and the dynamic characteristic index of the gray level distribution is determined; then, the index is analyzed using a convolutional neural network to generate modulation parameters of the contrast enhancement factor and the sharpening intensity; when the deviation between the spectral peak position and the preset peak value exceeds the preset deviation threshold, it is determined that the license plate is affected by motion blur, and an enhanced spectral representation is constructed; finally, the enhanced license plate image data is generated through inverse Fourier transform. The quality of the license plate image in a complex environment is significantly improved, the characters are clear and identifiable, and the license plate recognition accuracy and system robustness are effectively improved.

The invention relates to an underwater image intelligent real-time enhancement method and apparatus, and an electronic device. The method comprises the steps of obtaining a to-be-processed underwater image; rapid degradation analysis is carried out on the underwater image, a low-dimension statistical feature set is extracted, and the low-dimension statistical feature set is composed of a whole-frame gray level histogram mean value, a local contrast space mean value and a red-blue channel gray level difference value; comparing the low-dimension statistical feature set with a preset threshold set, and determining a degraded scene category of the underwater image; according to the degradation scene category, internal processing parameters of a lightweight Transform enhancement engine are dynamically modulated, and the internal processing parameters comprise feature fusion weights in a multi-scale feature fusion decoder; and performing enhancement processing on the underwater image by using the enhancement engine after parameter modulation, and outputting an enhanced image. According to the method, real-time processing of high-resolution images can be realized under the constraint of finite computing power, and various underwater image degradation scenes including composite degradation can be effectively identified and adaptively processed.