386 results about "Sobel operator" patented technology

The invention discloses a method for calculating angle of inclination and correcting inclination by using image contour information and combing Hough conversion. The method comprises: a) using an image collection device to obtain a certificate image; b) reducing the obtained certificate image; c) carrying out grey treatment on the certificate image; d) using an Sobel operator module to detect the edges of the gray image to obtain a thicker wide profile image; e) carrying out detailing operation on the treated image to obtain a profile image with thin edges and one pixel width; f) carrying out Hough conversion on the obtained profile image with thin edges to detect the line parameter of a frame, and using detected parameter to calculate to obtain the angle of inclination of an original certificate; and g) using the obtained angle of inclination to rotate the image. The invention fully utilizes reduced image contour information and uses Hough conversion to calculate the angle of inclination, which greatly reduces points participating in operation, improves algorithm efficiency and ensures high accuracy, strong robustness and high reliability of algorithm detection.

The invention discloses a method for detecting lane change of a vehicle based on a vehicle-mounted camera. The method comprises the following steps: firstly, initializing a read-in image, and converting the read-in image to a gray space; secondly, segmenting an sky region and a ground region of the image, and acquiring the image of the ground region; thirdly, carrying out edge detection by utilizing the sobel operator; fourthly, carrying out binaryzation by utilizing an Otsu's method; fifthly, restricting a fitting range, wherein hough transformation is restricted by the minimum fitting points, and extracting a lane line equation; sixthly, determining the type of a lane line; seventhly, classifying lane line treatment results; and eighthly, determining the current lane change situation ofthe vehicle. According to the invention, the current lane change situation of the vehicle is detected by adopting the mode of images, the existing vehicle-mounted camera of a driving school is utilized, has low cost, high practicability, diversity detection data, and has accurate and objective evaluations of lane change level for students, the equipment is simple to install, and has wide applications.

The present invention includes a method for analyzing an image wherein elements defining a path within a two-dimensional image are received from a prescreener. A Sobel operator may be applied to the region around each of the elements of the chain to obtain a corresponding array of gradient directions. An angle correction may be applied to any of the gradient directions that goes beyond the highest value (in radian measure; the Pi −Pi transition), to obtain an array of gradient directions free of any artificial jumps in value. The gradient direction array (Sobel chaincode) can have its bandwidth taken to determine a single number of straightness so as to identify extremely straight edges, (manmade objects) from less straight edges (natural objects). A similar process can be used to analyze contours for straight sections, which are also parallel. These two and other filters applied to the gradient array can be part of a feature suite, for feature space analysis.

The invention discloses an automatic identification system of a number plate on the basis of a simplified convolutional neural network. The convolutional neural network comprises an input layer, a convolutional layer, a pooling layer, a hidden layer and a classification output layer and solves the problem of number plate identification under a daily background. The number plate identification comprises the following steps: positioning, segmenting and identifying. The invention puts forward a positioning method which extracts colorful edges by colorful edge information and colorful information. Since parameters in the method are set on the basis of color features, noise in the daily background can be effectively inhibited, and input images of different sizes can be subjected number plate extraction. The automatic identification system omits a front convolutional layer of a traditional depth convolutional neural network and only keeps one layer of convolutional layer and one hidden layer. As the supplementation of a missing convolutional layer and the strengthening of input features, a gray level edge image obtained by a Sobel operator is used as the input of a colorful image, i.e., coarsness features which are artificially extracted replace features extracted by multiple convolutional layers of the traditional convolutional neural network.

The invention provides a smart phone photographing guiding method based on image matching, which can solve the problem of difficulty in framing when one person carries out self-photographing by adopting a postpositional phone camera in the prior art. The smart phone photographing guiding method comprises the steps of: presetting a closed region X on a phone photographing interface before photographing, so as to be used for displaying an expected position (of a person to be photographed) on a final image; and then photographing a dynamic image of the person to be photographed by using a phone camera, carrying out image outline Y(i) extraction on each frame of the dynamic image by adopting a sobel operator, directly or indirectly guiding the camera to rotate or the person to be photographed to move according to the distance between the closed region X and the center point of an image outline Y(i) region of each frame and a position correlation between the closed region X and the image outline Y(i) through a voice prompt module or the flicker frequency of a flashlight until that the closed region X is basically coincident with the image outline Y(i), and extracting the fame of image as a final image to be stored.

The invention provides a lane line detection method which comprises the steps of: S1, acquiring an image, converting the color image into a grayscale image, S2, selecting a region of interest of the image, dividing the region of interest into a left side image and a right side image, S3, calculating a greyscale binarization threshold of each row of each of the left side image and the right side image by binarization, extracting pixel point sets with grayscale values greater than or equal to the greyscale binarization thresholds from the left side image and the right side image, S4, obtaining edge images of the left side image and the right side image by using a one-dimensional sobel operator, calculating edge binarization thresholds of the edge images, extracting inner edge point sets of the left side edge image and the right side edge image, S5, selecting an intersection of the pixel point set in the left side image and the inner edge point set as an inner edge point of a left lane line, selecting an intersection of the pixel point set in the right side image and the inner edge point set as an inner edge point of a right lane line, and S6, calculating the left lane line and the right lane line according to the inner edge point of the left lane line and the inner edge point of the right lane line.

The invention brings forward a rapid and automatic mosaic technology of aerial video in a search and tracking system. The technology provided by the invention comprises the following steps: two continuous frames of color images in an aerial video sequence are converted to grayscale images; three-valued processing is carried out on the grayscale images by an image three-valued method, edge feature information images are extracted by an edge detection sobel operator and feature points are extracted by an Harris algorithm; feature point matching is completed by an optical flow pyramid algorithm; optimal homography matrix of the color images is calculated by combining a DLT algorithm and a RANSAC algorithm; panoramic image mosaics is completed after image correction of the color images by correction geometric transition matrix; and dynamic regulation of image three-valued upper and lower thresholds and a threshold of the edge detection sobel operator is carried out during the process of panoramic image mosaics. According to the invention, image mosaic accuracy and robustness are raised.

The invention discloses a face feature point detection method. According to the face feature point detection method, a novel three-channel GEH (Gray-Edge-Hog) mode image with the attitude detection task acting as the constraint and multiclass feature graphs acting for fusion acts as input. Considering that face three-dimensional attitude information has considerable influence on detection of face global feature points under the condition of large attitude deflection especially and the detection complexity of the contour feature points can be effectively reduced by adding Hog feature information reflecting face image local feature presentation and edge image information used for edge detection and extracted by the Sobel operator, the novel GEH three-channel image is generated by extracting the image grayscale value, the edge information and the Hog features to act as the input, and the auxiliary task of three-dimensional attitude estimation acts as the constraint information to perform face feature point detection.

The invention discloses a vehicle detecting method based on Gauss difference multi-scale edge fusion. The method includes the steps that Gauss scale transformation is performed on images to obtain four Gauss images in adjacent scales; according to the four Gauss images in the adjacent scales, the difference operation is performed between the images in the adjacent scales to obtain three Gauss difference images different in scale, edge detection is performed on the obtained three Gauss difference images through a Sobel operator, then edge fusion with the scale upward searching is performed to remove a lot of background edges while edge information of a vehicle is obtained as much as possible, and expansion, closed operation, hole filling and other series of morphological operation are performed on the fused edge images to obtain a connected domain image representing the vehicle; an outside rectangle of the position where the vehicle is located is determined in the original image according to the position information of a connected domain to detect the vehicle. The images in multiple scales are processed, so that algorithm complexity is reduced, operation amount is reduced, efficiency of vehicle detection is effectively improved, and a good detection result is obtained.

The invention discloses a face identification method based on multiscale weber local descriptor and kernel group sparse representation. The face identification method comprises the following steps: firstly normalizing the size of face images and smoothing the images by utilizing a gaussian filter; extracting differential excitation ingredients of the multiscale weber local descriptor of the images and extracting direction information by utilizing an Sobel operator; extracting the multiscale weber local descriptor of the face images according to the multiscale differential excitation and the direction information and mapping the multiscale weber local descriptor to a kernel space by utilizing a histogram intersection kernel; then with a kernel matrix obtained by a training sample as a sparse dictionary, calculating group sparse representation coefficients of a kernel vector obtained by a test sample; and finally reconstructing a multiscale weber local descriptor vector of the test sample according to the group sparse representation coefficients and distinguishing the test sample by utilizing the minimum reconstruction error. According to the face identification method, the multiscale weber local descriptor and the kernel group sparse representation algorithm are fused for face identification, and the identification accuracy rate is greatly improved.

A sub-pixel edge of an ellipse target positioning method mainly relates to a image processing and machine vision such as calculation of accurate parameters and calibration and matching of a pick-up camera of the ellipse target and the like, the method is mainly divided into three main steps: the first step includes noise elimination of images, edge detection by Sobel operators and extraction of edge points of the ellipse target; the second step includes calculation of geometric parameters of the ellipse target; and the third step includes position of the sub-pixel edge, wherein, the position of the sub-pixel edge can be divided into four parts of calculating target grayness and background grayness of the edge model, calculating edge angles, calculating the distance between edge points and real edge points and calculating accurate position of sub-pixel edge points. The sub-pixel edge of positioning method comprehensively utilizes geometric parameters of the ellipse target, distribution characteristics of grayness of the ellipse target and two-dimensional edge models. The method not only effectively improves the accuracy and the robustness of the edge positioning, but also greatly reduces arithmetic quantity so as to enhance the rapidity.

The utility model relates to a detection method of eye state based on cascade sort and Hough circle transform, belonging to the field of pattern recognition, which is characterized in the following steps: to acquire a face image; to carry out skin color segmentation in the YCbCr color space to acquire the position information of the skin color area using an ellipse skin model; to detect the rectangular eye area with the method of eye detecting window traversal with a cascade eye classifier; to merge a rectangle by rectangular merge method to get a merged eye rectangular linked list; to operate edge detection and binarization in turn on every rectangular eye area in the merged eye rectangular linked list with a Sobel operator to get an binary image; to detect the eye state of every binary image in turn with horizontal projection method; to further detect the eye state by Hough circle transform detection method if whether the present state is eye closure is not certain. The utility model increases the speed of eye detection, tracks and analyzes the eye state with the skin color segmentation, which is suitable for attention detection and fatigue detection.

The invention relates to a kilowatt-hour meter image automatic identification method which comprises the following steps: 1. image preprocessing: detecting vertical texture of a panel image by using Sobel operator, preliminarily removing the background area by a projection method, extracting the area with abundant vertical texture by an expansion method, and carrying out binarization treatment on the image by a adaptive threshold segmentation method based on an integral projection method; 2. precise positioning of indicating value and bar code: by combining an intelligent judgment method on the basis of indicating value intervals and length-width ratio characteristic of numeric characters under the complex image background, adapting to precise positioning of indicating values of different types of kilowatt-hour meters on the basis of vertical edge detection of the Sobel operator and morphological treatment; carrying out horizontal scanning on the bar code area to extract the bar code characteristic area; 3. bar code identification: identifying different character bar codes by using a similar edge distance normalization method; and 4. indicating value identification: extracting the indicating value by a PCA (principal component analysis) method. By using the PCA character recognition method, various character indicating values can be precisely identified, including identification of half-character.

The invention provides a face anti-counterfeiting method based on face depth information and edge image fusion, and the method comprises the steps: respectively extracting the edge information and depth image information of a face image through a double-flow network, carrying out the fusion of two types of features, and then carrying out the learning and classification through a feature fusion classification network, wherein a Sobel operator is used for extracting edge information of a face image, a PRNe is used for acquiring three-dimensional structure information of a face of a preprocessedliving body object, and adopting a Z-Buffer algorithm for projection to obtain corresponding living body face depth label. Depth information extraction network branches in the double-flow network extract differentiated depth information of living and non-living faces, and a weighting matrix and an entropy loss supervision mode are adopted to enhance the depth discrimination between a face area anda background area. Compared with the prior art, the method is slightly influenced by factors such as image quality and illumination, the problem that the hardware depth information extraction cost ishigh is solved, the characteristics of background information are expanded, and learning of redundant noise is weakened.

The invention discloses an earthquake image structure guiding noise reduction method based on regularization mixed norm filtering. The earthquake image structure guiding noise reduction method includes the following steps that a gradient structure tensor is solved for an input three-dimensional earthquake image; regularization mixed norm filtering is conducted on the gradient structure tensor; a diffusion tensor is designed according to the eigenvalue and eigenvector of the filtered gradient structure tensor; continuity factors are calculated, the continuity factors at the position of a boundary fault feather edge and the like are close to zero, and the maintain performance of the structure is achieved; a sobel operator serves as a derivation operator so that divergence can be calculated. By means of the earthquake image structure guiding noise reduction method based on regularization mixed norm filtering, the textured edge information of earthquake-related data can be reserved, Gaussian noise, ultra Gaussian noise and sub Gaussian noise can be effectively suppressed, and therefore an efficient noise reduction method is achieved.

The invention belongs to the petroleum exploration field and relates to a seismic recognition method of low-order strike-slip faults in complex structural areas. The seismic recognition method of the low-order strike-slip faults in the complex structural areas includes the following steps that: post-stack seismic data quality is analyzed; processing is carried out to obtain an advantageous frequency division phase band; processing is carried out to obtain Sobel operators in main directions; processing is carried out to Sobel operators in arbitrary directions; a multi-direction lower-order strike-slip fault system is extracted; and the reliability of the low-order strike-slip faults is verified. The method of the invention is suitable for seismic recognition and verification reliability of lower-order strike slip faults in any complex structural belts and can directly reflect the combination modes and spatial locations of lower-order strike-slip faults on a plane; and the method is an effective measure to determine low-order hidden faults in low signal-to-noise ratio and low-frequency seismic data areas and can provide an important basis for re-understanding of hidden fault oil control rules, reservation and production improvement, development plan deployment and adjustment in complex structural oil and gas fields or fault block oil and gas fields.

The invention discloses a pedestrian clothing color identification method based on digital image processing. The method comprises the steps that (1) a pedestrian detection method which combines an HOG feature description operator and a SVM classifier is used to acquire a pedestrian image; (2) a Sobel operator is used to detect the edge contour shape of a pedestrian to acquire an image to be searched; (3) a pedestrian contour shape template is produced and matches a corresponding region in the image to be searched to acquire upper and lower body images of the pedestrian; (4) a seed filling method is used to carry out communication region labeling on clothing colors of upper and lower body regions of the pedestrian; (5) color feature extraction is carried out on a color communication region; and (6) the SVM classifier is used to carry out color classification discrimination to acquire the clothing color of the pedestrian, and a final result is output. According to the invention, the identification accuracy of the clothing color of the pedestrian is improved; standardized dress in a dangerous region is ensured; and safety risks are eliminated.

The invention discloses a three-dimensional image quality evaluation method based on gradient information guided binocular view fusion. According to the method, a Sobel operator and a LoG operator areutilized to construct a united statistical gradient map which serves as a weight map for binocular view fusion, and a corresponding intermediate reference image and a corresponding intermediate distortion image are obtained; then, image feature information extraction is performed on the intermediate images, wherein image feature information comprises edge information, texture information and contrast information, and depth information is extracted from a disparity map of a reference and distortion three-dimensional image pair; and last, a final image quality objective evaluation score is obtained through measurement of a feature similarity and SVR-based feature integration and quality mapping, and measurement of three-dimensional image quality loss is realized. Experiment results show that an algorithm proposed based on the method has good accuracy and robustness.

The invention discloses a depth map denoising method based on color image segmentation. The depth map denoising method firstly performs chromaticity space conversion and grey-scale conversion on color maps; secondly, adopting Sobel operators to convert a grey-scale map into a gradient map, performing watershed segmentation based on the gradient map and dividing the color map into a plurality of areas; then calculating the Euclidean distances of adjacent domains under an LUV space, calculating gradient mean value differences of the junction of the adjacent domains according to the gradient map, performing domain fusion by utilizing the two groups of information, combining the resemble domains and marking the communicated domains; and finally marking the depth map communicated domains according to the color map and the depth map, and performing cavity compensation and denoising treatment on the depth map by utilizing the characteristics that the depth of the same areas of the depth map are approximately equal. The depth map denoising method has the advantages of being remarkable in denoising effect, capable of being achieved through hardwares and the like.

The invention discloses a method for adaptively detecting and eliminating a shadow in video segmentation. The method comprises the following steps of: firstly, roughly detecting a moving changing region by using accumulated frame differences and constructing a motion template by using a static index; secondly, performing statistics on brightness information to establish a background model, performing updating by combining the motion template, and detecting a foreground and an edge of the foreground by using a background difference and a Sobel operator; thirdly, performing horizontal projection and vertical projection on a detected edge image respectively, performing statistics on the distribution condition of edge images and roughly determining a shadow position and a search direction by combining shadow characteristics with a spatial position; and finally, precisely positioning a shadow point along the search direction in a suspected shadow region by using a hydrometer method so as to precisely eliminate the shadow. Due to the combination of the characteristics of the edge and the spatial position of the shadow, the method for adaptively detecting and eliminating the shadow disclosed by the invention has the advantages of adaptively positioning the shadow region and precisely eliminating the shadow point under the condition of invariance and availability of chrominance, along with small calculation amount and high robustness.

The invention belongs to the image processing field and relates to a stereo image quality evaluation method based on GSSIM. The method includes the steps: (1) for left image and right image, gradient structure similarity values are respectively solved, the average of the two values is solved, so as to obtain stereo image quality evaluation value QE; (2) the following method is adopted to carrying out image stereoscopic perception objective evaluation: absolute difference image of the original image and processed image is calculated, Mu1 and Mu2 of the absolute difference image are solved; Sobel operator is used for solving the gradient amplitude image of the absolute difference image; filtering is carried out on the absolute difference image of the original image, and original image binocular parallax distribution situation is calculated; Dl(x, y), Dcg(x, y) and Dsg(x, y) values at binocular parallax position are solved; DSSIM value at the binocular parallax position is calculated; and image gradient structure similarity namely image stereoscopic perception objective evaluation value DE is calculated. The invention can be well applied to stereo image quality evaluation, and correlation of objective evaluation result and subjective evaluation result is strong.

The invention discloses a method for carrying out change detection on remote sensing images based on treelet fusion and level set segmentation, and mainly solves the problem that much pseudo-change information exists in the existing change detection methods. The method is implemented through the following steps: inputting two time-phase remote sensing images, then respectively carrying out mean shift filtering on each image so as to obtain two time-phase filtered images; respectively carrying out two-dimensional stationary wavelet decomposition on the two time-phase filtered images three times under different level numbers; carrying out subtraction on wavelet coefficient matrixes of corresponding directional son-bands of the filtered images with the same decomposition level number; carrying out enhancement and two-dimensional wavelet inverse transformation reconstruction on wavelet coefficient difference matrixes in horizontal and vertical directions by using a sobel operator; and fusing the reconstruction images with different decomposition level numbers so as to obtain a final difference map by using a treelet algorithm, then carrying out level set segmentation on the differencemap so as to obtain a change detection result. By using the method disclosed by the invention, the accuracy of the change detection result can be improved effectively, and the edge feature of a change area can be maintained better, therefore, the method can be applied to the fields of natural disaster analysis, land resource monitoring, and the like.

The invention discloses a method for automatically identifying a sulfur hexafluoride pressure instrument image. The method comprises the following steps: an image obtained through instrument video monitoring is pretreated and converted into a gray image; the OTSU is utilized to find a proper threshold value of the image, a target pointer in the instrument image is distinguished from a disc background; sobel operator edge detection is performed on the gray image, and then Hough Transform is utilized to obtain the coordinate and radius of a central point of a circular area of the image; according to the features of the instrument image, the reference point position and the reference terminal point coordinate of a dial plate are obtained; according to the obtained coordinate parameters, the deflection included angel of the pointer is calculated, and the pointer read is calculated by combining the reference point position of the dial plate to realize the automatic identification of instrument image read. According to the invention, the pointer read of the instrument can be accurately and rapidly identified, a template image database is not needed to be established in advance, which is remarkably different from other image identification technologies, and automatic read identification of the image pointer of the sulfur hexafluoride pressure instrument can be realized through feature separation and identification of the image.

The invention discloses a Mel frequency cepstrum coefficient (MFCC) underwater target feature extraction and recognition method. The method comprises the following steps of: 1) acquiring a data sequence x(n); 2) framing to obtain xi(n); 3) obtaining yi(n) through windowing operation; 4) calculating the one-sided power spectrum density pi(l) of a framed and windowed signal; 5) solving a transfer function Hm(f) of a triangular filter bank, and obtaining Q by using the triangular filter bank; 6) performing logarithmic transformation to obtain E; 7) performing 3*3 template block operation of a Sobel operator and a Laplace operator in a t direction and a f direction respectively to obtain A, B and C; 8) performing discrete cosine transform (DCT) respectively to obtain feature sets, namely CA, CB and CC, and combining features; and 9) performing underwater target identification by using a clustering classifier of an expectation-maximization (EM) algorithm-based Gaussian mixture model. The method is favorable for improving the recognition rate of underwater targets.

The invention provides a quick image stain removing method based on the image inpainting technology. Firstly, the camera with a stained lens is used for shooting a white paper image, the Sobel operator and extensive operation are combined to detect the position of the stain, and the white paper image serves as a shade; the shade is used for covering the image shot by the same camera to mark the stain area in the image; for every pixel on the stain area contour, a patch block is defined and the priority of each patch block is calculated; the patch block with the biggest priority is selected to search and find the most matched block to replace and repair; and the repair image information is updated and re-repaired till the whole stain area of the image is repaired. Through the adoption of the image stain removing method, the stained image can be quickly and automatically repaired, and the linear structure of the image can be satisfactorily retained.