290 results about "Canny edge detector" patented technology

The invention discloses a circle center locating method of a circular target, mainly relating to a great deal of target identification and target location. Firstly, rough circle center location is carried out to an image by using a simple contour centroid method, a key square region is extracted as a region of interest according to the information of a rough location circle center and a rough location radius, and pixel-level edge location is carried out to the circular target in the region of interest by a canny operator; then sub-pixel location is carried out to the circular target according to the geometric features and the gray information of the circle, therefore, a precise coordinate of sub-pixel edge points is obtained; after that, a curvature filtering method and an average filtering method are respectively used for filtering 'isolated points' and noise occurring in the sub-pixel edge points; finally, a least squares method is utilized to fit a circle to the filtered sub-pixel edge points so as to obtain the final circular center and radius. The method not only effectively improves the precision of circle center location, but also improves the robustness thereof, thus further improving the measurement precision of a measurement system and perfecting the stability of the measurement system.

The invention discloses a lane line predicating and early warning method suitable for a city road. According to the method, firstly, down sampling is performed on a collected road image, a detection region of interest is set and grayed, filtering enhancement is performed on the region of interest, edge detection is performed through a Canny operator, binarization is performed on the image after edge detection, then the image is mapped on a Hough plane, and lane lines are extracted through improved Hough conversion; then, parameters of the lane line are predicated by the adoption of a classical Kalman filtering algorithm, a region of interest is established, and the lane lines in the region of interest are fitted through a least square method to obtain the optimal predication value; finally, road surface information and current position information of automobiles are combined to establish a lane line departure warning model based on transverse distance, and therefore departure warning is achieved. The recognition method for the lane lines is good in real-time performance and robustness, according to the lane line departure warning model, calculation is easy, and warning is accurate.

The invention relates to a weld seam surface defect feature extraction method based on the grayscale image morphology, which comprises the steps of setting shooting parameters of a miniature CCD camera according to an acquired image; converting the acquired true color image into a grayscale image, and carrying out median filtering processing on the image; eliminating a white interference region generated by residue noise and background texture by adopting a minimum area deleting method; avoiding influences imposed on edge line extraction by an undetermined black region through region filling processing; processing a completely filled weld seam region through an expansion algorithm so as to acquire a weld seam region whose area is identical to the actual weld seam area; extracting an edge line of the filled and expanded weld seam region by adopting a Canny operator so as to realize positioning for the weld seam region; and drawing a cross section grayscale B scanning curve which is vertical with the weld seam edge, wherein a gray-scale value of the weld seam surface changes obviously on the B scanning curve when the weld seam surface has defects such as a hole and an overlap, so that different types of defects at the weld seam surface are judged. The weld seam surface detect feature extraction method realizes accurate positioning for the weld seam edge and accurate recognition for the defects such as overlaps and holes.

The invention discloses a TFT-LCD Mura defect machine vision detecting method based on B spline surface fitting and belongs to the field of LCD display defect detecting. The method includes: using a CCD camera to collect the grayscale image of a lighted to-be-detected LCD; filtering the original image; extracting an interested area; using double three-time B spline surface fitting to fit an image background; using the original image to subtract the background image so as to obtain the image with the background, with uneven brightness, being removed; using a Canny operator to detects Mura defects; determining defect level. The method has the advantages that the method is reliable, high in accuracy and time saving in calculation.

The invention relates to a datum point positioning method based on machine vision, which belongs to the field of image processing and aims to solve the problems of poor datum point positioning accuracy and high cost of surface mounting equipment. The method comprises the following steps that a vision obtaining device is adjusted to obtain a datum point regional image; the image is preprocessed, and a threshold is set to establish a binary image; connected region labeling is performed on the binary image, so as to find out the largest connected region as a target region of a datum point; Canny edge detection is performed on an original image, so as to preliminarily determine coarse edge points; Hough transformation processing is performed on the coarse edge points, and an optimal existing circle in the image is found out; the distance between each edge point and the center of the circle is calculated; the position of a sub-pixel level of the edge point is obtained; and circular least-square fit is performed on the edge point in the position of the sub-pixel level, and then an accurate center and radius of the datum point are obtained and sent to a control system. The datum point positioning method can be widely applied to accurate positioning of the datum point.

The invention discloses a driving safety monitoring system based on face orientation and visual focus, which comprises a vision sensor and an intelligent processor. The intelligent processor comprises an image acquisition module, a complexion area detection module, an edge extraction module, an eye detection module, a face orientation analysis module and a driver safety driving judgment module, wherein the complexion area detection module is used for converting an RGB color space into a YCrCb color space and detecting a complexion area; the edge extraction module is used for adopting a Canny operator to carry out edge extraction so as to acquire a head image outline; the eye detection module is used for carrying out Hough conversion for the marginalized face and positioning eyes; the face orientation analysis module is used for determining a mouth area, positioning the mouth, calculating left and right areas of the face respectively by using the eyes and the mouth as reference, and calculating the ratio of the left and right areas of the face; and the driver safety driving judgment module is used for judging an unsafe driving state if the calculated ratio of the left and right areas of the current face is out of a preset interval according to the preset interval of the ratio of the left and right areas of the face, and sending out an alarm command. The driving safety monitoring system has strong capacity of resisting disturbance and high accuracy.

The present invention relates to an iris recognition method for correcting a rotated iris image. The iris image is acquired by an image acquisition device using an infrared illuminator. The inner and outer boundaries of the iris are detected by analyzing the differences in pixels using a Canny edge detector and the image for the inputted iris image, so as to allow the boundaries of the iris to be more accurately detected from the eye image of a user. If the iris image acquired has been rotated at an angle with respect to the centerline of the iris, the rotated iris image is corrected for subsequent authentication purposes.

The invention discloses a method and device for acquiring a Kinect depth image. The method comprises the steps of sampling static scenes continuously by means of the Kinect to obtain a plurality of frames of depth images and colored images; carrying out median filtering and noise reduction on the depth images; carrying out edge detection on the first frame of colored image and the depth images treated with median filtering by means of the Canny operator; carrying out primary segmentation on the depth images by means of a user index value of Kinect depth data to obtain a mark sheet, wherein pixels with the same mark value belong to one region in the mark sheet; carrying out primary repairing on the depth images by means of a region growing method with edge limitation considered and updating the mark sheet according to edge information of the depth images and the colored images and a depth image segmentation result; carrying out image enhancement on the depth images by means of trilateral filtering according to the updated mark sheet. According to the method and device for acquiring the Kinect depth image, edge inconformity of the depth images and the colored images is effectively overcome, cavities in an original Kinect depth image are repaired, noise is eliminated at the same time, and the depth image which is good in quality can be obtained finally.

The present invention discloses a road detection algorithm of fusion of area and edge information. The algorithm comprises: collecting road image, and employing self-adaption median filtering and noise reduction to obtain enhanced images; selecting the R component of the enhanced images in the RGB color space, employing a maximum between-cluster variance (Otsu) method to realize the road area segment of the images to obtain a binary segmentation image, and optimizing the binary segmentation image through adoption of the serial mathematical morphology; detecting the binary segmentation image through adoption of the optimal edge detection Canny detector to detect the binary segmentation image and obtain the road edge detection information, and employing the Otsu method to calculate the dual threshold in the optimal edge detection Canny detector; and employing the binary segmentation image and the road edge detection information to obtain a road border line in the image. The road detection algorithm of fusion of area and edge information employs the image processing technology to detect and extract the road information for the autonomous navigation system of a cable tunnel robot; and moreover, the road detection algorithm of fusion of area and edge information is a self-adaption road border line segmentation fusion method and can obtain a smooth and accurate road edge.

The invention discloses a color image segmentation method, which comprises the following steps: processing an image to be segmented into a gray image; extracting a region profile map from the gray image by an Otsu threshold segmentation method, and determining an optimal threshold; taking the optimal threshold as a high threshold of a Canny operator, and extracting an edge map from the gray image by utilizing the Canny operator; and fusing the area profile map and the edge map, and outputting a segmentation result of a color image. The method adopts an operator threshold determining scheme combined with an adaptive strategy and an empirical value and overcomes the defect that an empirical value selecting method has application limitation; the local change intensity of a gray value is obtained through edge detection, so that over-segmentation of avoidable areas is limited; and residual edges are complemented through region segmentation, so that profiles of images after the segmentation are more clear and complete.

The invention discloses a license plate recognition method and system, and the method comprises the following steps: collecting a vehicle image; carrying out the filtering of the vehicle image, and carrying out the edge detection of the vehicle image through a Canny operator, so as to convert the vehicle image into an edge binary image; scanning the edge binary image, so as to obtain license plate candidate regions according to the edge spacing and edge jump; locating the license plate in the license plate candidate regions according to the vertical edge features and morphological expansion of the license plate; searching a plurality of characters from the license plate according to the contour features of a single character, and removing the pseudo characters in the characters according to a license plate template; carrying out the recognition of the characters through an SVM classifier, and determining the license plate number of a vehicle according to a recognition result. The method can carry out the precise positioning and character segmentation of the license plate, and improves the character recognition rate of the license plate.

The invention provides a lane line detection method for lane departure early warning. The method includes the edge detection step and the lane line recognition step. In the edge detection step, edge detection is conducted on images through a stepped edge detection algorithm Canny operator, and the high threshold value and the low threshold value of the Canny operator are determined through a dynamic threshold value; interference edges are removed, namely, a sliding window filter is arranged to filter perpendicular and transverse straight lines in the images, a Sobel gradient operator is utilized to calculate the direction gradient of pixel points in the images, the direction angle of the pixel points is acquired through arc tangent, and then direction gradient filtering is conducted according to the size of the angle. In the lane line recognition step, a plurality of straight lines are detected through Hough transformation, and lane lines are determined according to the positions, where the straight lines are located, of the images and the slope; whether the determined lane lines meet requirements or not is judged according to the relations of the positions and the slope of the left lane line and the right lane line and the included angle between the left lane line and the right lane line. According to the method, lane departure warning anti-noise and anti-interference capacity is improved, high robustness is achieved, and detection efficiency is improved.

A method of image processing is provided which includes non-recursive hysteresis thresholding in Canny edge detection. The non-recursive hysteresis thresholding reduces computational complexity and eliminates the potential for call stack overflow. More specifically, hysteresis thresholding is performed in a raster-scan order pass over the image data to connect edge segments to form continuous edges.

The invention discloses an intelligent identification and analysis method for an examination answer sheet system. The method comprises the following steps: carrying out item-by-item decomposition operation on image gray scale, image binaryzation, image denoising and morphological processing in an image preprocessing technology; an independent peak point is formed by using a monitoring image inclination and rotation correction technology, and an image is read for binaryzation; Edge monitoring operator monitoring is constructed by using an edge monitoring technology and through an original imagedifferential technology, and image filtering, image enhancement, image detection, a Canny operator and a Canny edge recognition algorithm are formed; and data image recognition analysis is carried out by adopting image recognition point positioning and affine transformation. The method can achieve the intelligent high-speed recognition of the answer sheet, is higher in image precision, is high inefficiency, is not limited to specific scanning equipment, achieves the mining and analysis of large data of batch scanning results, can intelligently recognize specific wrong knowledge points, and can achieve the intelligent statistical induction.

The invention relates to a laser two-dimensional code positioning method which performs image preprocessing, image binarization, Canny operator edge detection, two-dimensional code coarse positioning, image frame refinement, position positioning indicator detection, image geometrical distortion correction two-dimensional code accurate positioning, etc. on a metal part two-dimensional code image to obtain an accurate positioning two-dimensional code image. The method utilizes an image morphological method to perform closed operation on a two-dimensional code binary image and to detect the outer contour of a two-dimensional image so as to obtain a coarse positioning two-dimensional code image, then utilizes an image frame refinement algorithm to obtain a two-dimensional code position detection figure and the center thereof, and utilizes a geometrical distortion algorithm to extract a positioned two-dimensional code so as to obtain a better two-dimensional code image. At present, the position positioning indicator and correction figure of a laser two-dimensional code image are unclear or irregular caused by chemical reaction in indication, so that a two-dimensional code can not be positioned by directly utilizing the characteristics of the position positioning indicator and correction figure; and according to the invention, the problem is solved.

The present invention relates to a visible light and SAR image registration method based on linear features and control points. The method carries out edge detections respectively of visible light and an SAR image by using a Canny operator and an ROA (Ratio of Averages) operator according to features of visible light and the SAR image, extracts linear features by employing a multi-scale discrete fast Beamlet transform method, structures control points based on linear features, and realizes coarse-to-fine registration on images to be registered by using a coarse registration method based on consistent features and a fine registration method based on control points. The beneficial effect of the method lies in the fact that mismatching in single straight line matching caused by difference in length and location is prevented, point matching precision is improved, the method is suitable not only for SAR and visible light image registration, but also for registration of images with same edge features due to the employment of registration algorithm based on linear features and control points features.

The invention relates to a solar cell panel recognition method based on an improved Canny operator and a contour area threshold value. Firstly, interpolation sampling is carried out on the collected images, and the size and the pixel of the images are reduced; interference edges and the noise points are filtered through gaussian filtering, and a target area is highlighted. Secondly, the color space is converted, and the color information such as saturation and the like is conveniently extracted; and the purpose of reinforcing the target area and weakening the background area is achieved by eliminating irrelevant points. Finally, an improved Canny operator is used for acquiring a dynamic threshold value and carrying out edge detection on the images, and a binary contour image is obtained. Threshold segmentation is conducted on a contour area, a background region is excluded, and a target region is reserved. The accuracy of solar cell panel recognition can be improved.

The invention relates to an identification and deviation-detection method for a lane, which comprises the following steps: (1) acquiring a lane image and carrying out pretreatment on the lane image; (2) carrying out Canny operator edge detection on the lane image which is subjected to the pretreatment to obtain lane edge images; (3) determining the position of a lane mark according to the obtained lane edge images and a Kalman predictor-based lane tracking method, selecting Kalman prediction areas, filtering out a set of effective points by using a distance discrimination method, and extracting lane parameters on the basis of optimizing the set of effective points; (4) extracting the lane mark by using the Hough conversion with linear fitting according to the obtained lane parameters; and (5) counting the number of background points and lane mark points in the Kalman prediction areas by using the starting point position and the dynamic prediction of a lane which are determined in the step (3), and solving the ratio of the background points to the lane mark points. With the adoption of the identification and deviation-detection method for the lane provided by the invention, the monitoring for lane condition can be rapidly and stably realized.

The present invention relates to a semantic object segmentation method suitable for low field depth image, which includes: firstly introducing gradient histogram to figure out the distribution of image in the energy space, obtaining an energy focusing significance map in combination with the character of the low field depth map; using the two-sided filter and morphology instrument to rehandle the energy focusing significance map; and then setting self-adapting threshold value and processing to obtain an initial object mask map, combining with the edge information obtained by canny operator to obtain the corrected object mask, in order to raise the segmentation accuracy of the interesting object; finally using the Bayesian eclosion algorithm to obtain the ideal semantic object segmentation result, in order to delicately process the complicated image boundary, such as hairs. Accurate segmentation to the interesting object in the field depth scope in the image video sequence.

The invention relates to the technical field of biometric identification and discloses an iris positioning method and an iris identification system. The method and the system are based on mathematical morphology, Radon transform, canny operator and hough transform. The algorithm comprises the positioning of an inner edge and an outer edge, wherein in the positioning of the inner edge, based on a gray projection method of line detection, a binarized image is processed by using the principle of the mathematical morphology so as to remove the interference of noise; and in the positioning of the outer edge, an iris image is processed by using the principle of the mathematical morphology at first, then an upper eyelid and a lower eyelid are detected by the Radon transform to remove the interference, and finally, an edge is extracted by a canny algorithm and the external edge of the iris is detected by the hough transform. Therefore, large area blind searching can be avoided so as to improve positioning speed.

The invention discloses an RADAR parallax image optimization method, a stereo matching parallax image optimization method and a stereo matching parallax image optimization system. The RADAR parallax image optimization method comprises the following steps: obtaining a color block graph: performing contrast enhancement on an initial image, converting the initial image from an RGB space into a CIELab space, and performing color blocking on the CIELab space by mean-shift color partitioning to obtain the color block graph; acquiring edge information of a parallax image: receiving an initial parallax image of the initial image, and extracting the edge information of the parallax image in the initial parallax image by combining Canny operator; optimizing the parallax image: performing inconsistent region detection on the color block graph and the edge information of the parallax image to obtain a problem region graph, performing OccWeight correction on the initial parallax image according to the problem region graph, and filtering to obtain a final parallax image. According to the method disclosed by the invention, after the initial parallax image is optimized, the error rate is reduced, and the accuracy of the final parallax image is improved.

The invention discloses a noise motion blurred picture recovery method based on radial neural net, which comprises the following steps: (1) making the noise motion blurred picture y(m, n) generate the corresponding smoothing picture s(m, n) after low-pass filtering with the two-dimensional median filter; (2) calculating the inaccuracy picture e(m, n)=y(m, n)-s(m, n) between the noise motion blurred picture and the smoothing picture; (3) detecting the edge with Canny operator in order to estimate the gradient f' (m, n) of y(m, n); (4) calculating regular parameter lambda (m, n) of every picture element according to the size of f' (m, n) and generating interpolation picture f lambda (m, n) by radial neural net RBFN according to the inaccuracy picture e(m, n); (5) acquiring the silent motion blurred picture f(m, n) by superimposing the interpolation picture f lambda (m, n) to the smoothing picture s(m, n) after low-pass filtering; (6) acquiring the width HL and the height VL of two-dimensional blurred picture by identifying the motion blurred direction and the motion blurred length of the motion blurred picture automatically and acquiring the recovery picture with the picture recovery method. The invention provides the automatic identification, the low complicated calculation and the good recovery effect.

The invention discloses an escalator passenger abnormity behavior detection algorithm base on machine vision. The algorithm comprises steps that 1), video image acquisition is carried out; 2), Canny edge detection on an acquired image is carried out; 3), a circle-like zone of an edge image is obtained by utilizing Hough circle transformation; 4), whether the circle-like zone is a human head is determined through a human head gray histogram template; 5), a human head of the image is tracked by utilizing a Mean Shift algorithm; 6), a moving direction and a moving speed of a center point of the circle-like zone of a head portion of each passenger are calculated; and 7), behaviors of passengers are determined through analyzing the moving directions and the moving speeds of the center points of the circle-like zones. Through the algorithm, safety quality of an escalator can be effectively improved, deterioration of situations of emergent events can be prevented, and an injury degree of fall-over passengers on the escalator can be reduced to a maximum degree.

Multiple volumes that are to be aligned to form a single volume are processed. The system and method use an equalization step, a edge detection step and a correlation step to determine the overlapping positions between the first volume and the second volume of a volume pair having a maximum correlation value, and the best alignment of the first volume and the second volume of the volume pair is determined by the correlation value. A coarse correlation step using lower resolution volumes can be performed first followed by a fine correlation step using higher resolution images to save processing time. Initial preprocessing steps such as volume shearing can be performed. Equalization involves equalizing voxel size and edge detection can be performed using a Canny edge detector.

The present invention relates to an iris recognition method which is one of biometric technologies. According to a non-contact-type human iris recognition method by correction of a rotated iris image of the present invention, the iris image is acquired by image acquisition equipment using an infrared illuminator. Inner and outer boundaries of the iris are detected by analyzing differences in pixels of a Canny edge detector and the image for the inputted iris image, so as to allow the boundaries of the iris to be more accurately detected from the eye image of a user. Thus, the iris image with a variety of deformation can be processed into a correct iris image, so that there is an advantage in that a false acceptance rate and a false rejection rate can be markedly reduce.

The embodiment of the invention provides an image edge detection method and system. The method includes the steps of obtaining a gradient magnitude value of each pixel point in an original image according to a roberts operator detection algorithm and obtaining a gradient image, wherein roberts operators adopt a 3*3 operator template; conducting non-maximum value inhibition treatment on the gradient image and obtaining a candidate edge image, wherein pixel points serving as candidate edge points are marked in the candidate edge image; according to a canny operator detection algorithm, conducting edge detection on the candidate edge image to obtain an edge image. According to the embodiment, through the roberts operators adopting the 3*3 operator template, the accurate gradient magnitude values are figured out, and a great condition is created for applying the canny operator detection algorithm; by adopting canny operators for screening the pixel points, serving as the candidate edge points, of the border of the candidate edge image, the calculation amount of the canny operator detection algorithm can be decreased, the calculation precision can be improved, and meanwhile the smoothness and the advantages of extremum inhibition are preserved.

The invention discloses a round sub-pixel detection algorithm based on point Hough transform and Legendre moments, and provides a method for quickly positioning a circular mark in polymerase chain reaction (PCR) visual detection. The method comprises the following steps of: performing edge detection and Hough transform to acquire a circular mark pixel-level reference value by using a Canny arithmetic operator; filtering off noise points in edge points by using the circular mark pixel-level reference value; analyzing a sub-pixel edge positioning method based on Legendre orthogonal moments, and an error; evaluating sub-pixels of the edge points from which the noise points are filtered off by using the Legendre orthogonal moments and an error method; and finally fitting the edge points by a fast least square method to acquire accurate circle center positioning parameter and radius parameter of a round. By the method, the characteristic of high speed of point Hough transform operation is kept; and the method has the characteristics of high positioning accuracy, high noise resistance, accuracy, high speed and robustness and meets the requirements of high accuracy and real-time property in the PCB visual detection.

The invention discloses a weld surface defect feature extraction method based on an image gray scale B sweeping curve, and the method comprises the steps: setting photographing parameters of a miniature CCD camera according to an image collection standard; converting a collected true color image into a gray scale map, and carrying out the median filtering of the gray scale map; carrying out the preliminary extraction of the edge lines between a weld region and a background and between a non-welding region and the background in the gray scale map through employing a Canny operator edge line extraction algorithm based on a threshold value; judging the welding surface defects according to the number and shapes of edge lines, such as non-centered welding and weld bead distortion; reconstructing a continuous edge line of the weld region through Hough transformation, and achieving the accurate positioning of the weld region; drawing a section gray scale B sweeping curve perpendicular to the weld edge, wherein the difference of gray scale values of the B sweeping curve at two ends of the weld changes remarkably when there is misalignment on the weld surface, so as to judge the misalignment phenomenon of the weld surface. The method achieves the accurate positioning of the weld edge and the accurate recognition of defects of non-centered welding, weld bead distortion and misalignment.

Provided is a face key part fatigue detection method, comprising: first, utilizing a skin color model to detect a face area to provide initial positioning for an AAM; performing local human eyes and mouth tracking based on the AAM to obtain eye and mouth areas; utilizing a Canny operator to accurately position two areas to obtain fatigue detection parameters; and finally realizing fatigue detection according to a PERCLOS method. Face detection based on an HSV color model is not influenced by postures and angles, but is vulnerable to background interference; while the AAM has great face key point tracking effects, but is difficult to perform initial positioning; the method combines the HSV color model and thee AAM to realize eye and mouth accurate positioning and tracking. The method avoids body direct contact, employs AAM local texture search, reduces search time and obtains more accurate results compared with an ASM algorithm. Meanwhile, the method provides a driver spirit assessment module, can perform clear determination on driver states more easily.

The invention discloses an image registering method for a panoramic assisted parking system. The method comprises the following steps of: (1) acquiring image data of a reference image and an image to be registered; (2) performing histogram equalization and Gauss smooth filtering pre-processing; (3) selecting characteristic points with a Hrris comer extraction method respectively; (4) performing sub-pixel correction with a Taylor polynomial; (5) coarsely matching the reference image with the image to be registered; (6) accurately matching the reference image with an image to be matched with a Canny operator and a Hausdorff distance algorithm; (7) performing spatial transformation processing on image points to be matched with an affine transformation model; and (8) fusing an affine transformation coordinate with the reference image which is accurately matched in the step (6) so as to output a registering image, wherein each affine transformation coordinate corresponds to each image point in the to-be-matched image acquired in the step (7). The image registering method has the advantages of small calculation amount, high accuracy and high robustness in the splicing process.