207 results about "Gradient magnitude" patented technology

Near infra-red images of natural scenes usually have better contrast and contain rich texture details that may not be perceived in visible light photographs. The contrast and rich texture details form a NIR image corresponding to a visible light image are useful for enhancing the visual quality of the visible light image. To enhance the visual quality of a visible light image using its corresponding near infra-red image, a computer-implemented method computes a weight region mask from the visible light image, transfers contrast data and texture data from the near infra-red image to the visible light image guided by the weighted region mask. The contrast data is computed from the low frequency subbands of the visible light image and corresponding infra-red image after a wavelet transform by matching the histogram of gradient magnitude. The texture data is computed from the high frequency subbands of both images after wavelet transform.

The technique of the invention calculates an edge gradient magnitude a(x,y) at each pixel position (x,y) and an edge width w(x,y) from luminance values of an object image, and computes an out-of-focus evaluation value M(x,y) at each pixel position (x,y) from the calculated edge gradient magnitude a(x,y) and edge width w(x,y). The technique then divides the object image into a preset number of blocks, determines a representative out-of-focus evaluation value Y(m,n) in each block, and compares the representative out-of-focus evaluation value Y(m,n) with a preset threshold value for block classification to categorize each block as an in-focus block or an out-of-focus block. The technique eventually determines the object image as in-focus or out-of-focus, based on the total number of blocks included in in-focus block areas, the total number of blocks included in out-of-focus block areas, and the relative positions of the in-focus block areas and the out-of-focus block areas to the whole image.

A method and system detects candidate targets or objects from a viewed scene by simplifying the data, converting the data to gradient magnitude and direction data which is thresholded to simplify the data. Horizontal edges within the data are softened to reduce their masking of adjacent non-horizontal features. One or more target boxes are stepped across the image data and the number of directions of gradient direction data within the box is used to determine the presence of a target. Atmospheric attenuation is compensated. The thresholding used in one embodiment compares the gradient magnitude data to a localized threshold calculated from the local variance of the image gradient magnitude data. Imagery subsets are containing the candidate targets may then be used to detect and identify features and apply a classifier function to screen candidate detections and determine a likely target.

A face detecting unit detects a person's face from input image data, and a parameter setting unit sets parameters for generating a gradient histogram indicating the gradient direction and gradient magnitude of a pixel value based on the detected face. Further, a generating unit sets a region (a cell) from which to generate a gradient histogram in the region of the detected face, and generates a gradient histogram for each such region to generate feature vectors. An expression identifying unit identifies an expression exhibited by the detected face based on the feature vectors. Thereby, the facial expression of a person included in an image is identified with high precision.

A method of object identification comprising, for an image in which an object is to be identified, determining for each of a set of location in the direction in which the gradient of intensity change to an adjacent location is greatest together with the magnitude of that gradient, modifying the gradient magnitude using a nonlinear function, the modified gradient and the associated direction for the set of location providing a vector representative of the image, and comparing the vector with a previously generated statistical model which provides identification of the object.

The invention discloses video noise reduction device and a video noise reduction method. The method comprises the following steps of: obtaining a brightness difference histogram of a current image by using a denoising result of a previous frame of image and a gradient magnitude histogram of the current image; carrying out noise level evaluation on the current image according to the brightness difference histogram; calculating the spatial distance of any two pixel points in the current image, so as to obtain the spatial similarity of the any two pixel points; carrying out denoising on the current image according to the spatial similarity; calculating a pixel time domain distance between any pixel point in the current image and the pixel point at the position corresponding to the previous frame of denoised image, and calculating the corresponding time domain similarity; carrying out three-dimensional recursive denoising on the video image according to the obtained time domain similarity, the spatial similarity denoising result and the previous frame of denoising result. By adopting the device and method disclosed by the invention, three-dimensional recursive denoising is carried out by using the pertinence of the pixel in space and time, so that strong complicated noise can be removed; an image detail can be kept; the stability of the denoising effect also can be ensured.

The invention discloses a rapid sub pixel edge detection and locating method based on machine vision. The method includes the following steps that firstly, a detection image is acquired; secondly, denoising pretreatment is conducted on the image; thirdly, the gradient Gx of each pixel point in the horizontal direction and the gradient Gy of each pixel point in the vertical direction are calculated; fourthly, the gradient magnitude G0 and the gradient direction Gtheta of each pixel points under polar coordinates are calculated; fifthly, neighborhood pixel points of each pixel point are determined; sixthly, pixel-level edge points are determined; seventhly, the distance between a sub pixel edge point of each pixel-level edge point in the eight-gradient direction and the pixel-level edge point is calculated; eighthly, the distance d between each sub pixel edge point in the actual gradient direction Gtheta and the corresponding pixel-level edge point is calculated; ninthly, a cosine lookup table method is adopted for calculating rectangular coordinates of each sub pixel edge point in the actual gradient direction Gtheta, so that the image edge points are detected and sub-pixel-level localization is conducted. The whole method is high in calculation accuracy and speed.

Systems and methods are provided for detecting edges in an image. In an example method, the image is smoothed using a filter. A gradient magnitude and angle are determined for each pixel of the image. A non-maximum suppression is performed on the resulting image data. A double threshold with an upper and a lower threshold are applied to the resulting image data to determine the edges in the image. The upper and the lower thresholds are calculated automatically using a distribution of intensity values of the image. In example systems, an image is acquired by a camera, or other image acquisition units. An image processing unit is configured to detect the edges in the image using an adaptive threshold edge detection method.

The invention discloses a margin-oriented self-adaptive image interpolation method and a VLSI implementation device thereof. The method comprises the steps that the gradient magnitude and the gradient direction of a source image pixel are computed, and marginal information is obtained by comparing the gradient magnitude and a local self-adaptive threshold value, wherein the marginal direction is perpendicular to the gradient direction; the marginal direction is classified, filtering is conducted through the marginal information, and an image is divided into a regular marginal area and a non-marginal area; the regular marginal area interpolation is conducted in the marginal direction, and an improved bicubic interpolation method, a slant bicubic interpolation method and a slant bilinear interpolation method based on local gradient information are adopted to conduct image interpolation according to the classification of the marginal information; image interpolation is conducted on the non-marginal area through the improved bicubic interpolation method based on the local gradient information. The VLSI implementation device comprises a marginal information extraction module, a self-adaptive interpolation module, an input line field synchronous control module and an after-scaling line field synchronous control module. The margin-oriented self-adaptive image interpolation method and the VLSI implementation device of the margin-oriented self-adaptive image interpolation method can effectively improve the effect of image interpolation with high-magnification scaling, and is beneficial to integrated circuit framework achieving.

The invention discloses a method for evaluating medical image quality in combination with phase consistency, gradient magnitude and structural prominence. The method comprises the following steps: 1) calculating a phase consistency map of a reference image and a distorted image by using a log-Gabor wavelet; 2) calculating a gradient magnitude map of the reference image and the distorted image by using a Scharr operator; 3) obtaining a local image quality map in combination with the phase consistency map obtained in the step 1) and the gradient magnitude map obtained in the step 2); 4) calculating a structural prominence map, based on a human eye visual feature, of the reference image; 5) performing weighting summation on the image quality map in the step 3) by using the structural prominence map as an aggregation policy to obtain an image quality score; and 6) when the image quality score is higher than a preset score threshold, judging that the medical image quality is relatively high. The method is relatively high in reliability and high in practicality.

The invention provides an image definition detecting method and device. The image definition detecting method comprises the following steps: acquiring an original image and converting the original image into a grey level image; selecting M image blocks from the gray level image, wherein the M is an integer larger than or equal to one; calculating a gradient magnitude graph of each image block in the M image blocks, converting the gradient magnitude graph into a one-dimension image edge gradient magnitude signal and a one-dimensional image edge grey level signal, and then calculating a definition metric of each image block; when the M equals one, enabling the definition metric to serve as a definition detecting result of the original image, when the M is larger than one, calculating a mean of the definition metrics of all image blocks to obtain a comprehensive definition metric, and enabling the comprehensive definition metric to serve as a definition detecting result of the original image. According to the image definition detecting method, an index for measuring the image definition is established with the edge gradient serving as the basis, and the method has generality to various image scenes.

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.

System, apparatus, and method embodiments are provided for detecting the presence of a pedestrian in an image. In an embodiment, a method for determining whether a person is present in an image includes receiving a plurality of images, wherein each image comprises a plurality of pixels and determining a modified center symmetric local binary pattern (MS-LBP) for the plurality of pixels for each image, wherein the MS-LBP is calculated on a gradient magnitude map without using an interpolation process, and wherein a value for each pixel is a gradient magnitude.

An image processing apparatus includes a contour-candidate-edge detecting unit that detects, as contour candidate edges, edges based on a gradient magnitude of each pixel in a target image; a contour-edge detecting unit that detect contour edges by performing thresholding on gradient magnitudes of the contour candidate edges; an interpolation-line generating unit that generates interpolation lines for connecting end points of respective end-point pairs based on gradients of pixel values between the end points while each of the end point pairs is made up of an end point of an identical contour edge as a connection base and an end point of a different contour edge as a connection destination; and a contour-edge interpolating unit that selects one of the interpolation lines based on the gradients of pixel values on the interpolation lines and interpolates a contour edge between the end points with the selected interpolation line.

The invention discloses an image sub-pixel edge extraction method having extensive adaptability, comprising steps of adopting an adaptive high-low threshold value calculation method, executing a local maximum central value choosing operation on a gradient image by combining with gradient direction information of a pixel point after obtaining a gradient image, establishing a relative coordinate with a random pixel point as an origin point, taking eight-neighborhood pixels around the origin point as local maximum central value choosing data samples, obtaining a comparison result between the adjacent neighborhoods according to a gradient direction, determining whether the current pixel position is a boundary point candidate position, adopting a Hessian matrix method based on a Steger surface fitting method to solve a sub-pixel position of the boundary point and connecting boundary points for forming a line to constitute a set of directed continuity points. Determination whether an extreme value of a local gradient magnitude is a boundary point can be made by combining with a specific threshold value; if the local extreme value is greater than a given threshold value, the local extreme value is marked as a boundary point, and if the local extreme value is smaller than a given threshold value, the local extreme value is a noise point or a background point The invention has great instantaneity.

The invention relates to a fingerprint image segmentation method based on gradient projection and morphology, which pertains to the technical field of image processing. Firstly, lateral and longitudinal first difference images of an original fingerprint image Gx and Gy and a gradient image Del f, are calculated; then the lateral and longitudinal projection curves of the gradient image Del f are utilized to determine a foreground area a rough fingerprint; the foreground area of the rough fingerprint image is divided and fuzzy areas are removed by utilizing the consistency threshold value at the gradient direction; the gradient magnitude of the rough fingerprint foreground area image with fuzzy areas removed is binarized and a module, the size of which is more than the distance between two adjacent ridge lines is utilized to carry out close operation and open operation over the gradient binarized image so as to obtain a fingerprint segmentation template; finally, the original fingerprint image and the fingerprint segmentation template carry out point multiplication to obtain the final fingerprint foreground area image. The fingerprint image segmentation method of the invention is established on the gradient map of fingerprint, and therefore the calculated amount is relatively small and the fingerprint foreground contour after being operated by the morphology is relatively smooth.

The invention discloses a measurement method for image Gaussian Blur. The method is that the edge width of a Gaussian Blur image acts as a parameter for judging the image blurring degree. The implementation includes the steps of acquiring a grayscale of an image; acquiring a gradient magnitude diagram and a gradient direction angle of the grayscale; constructing a multi-scale and multi-direction Gaussian-Laplace filter; acquiring an energy diagram and a scale diagram correspondingly by carrying out convolution on the gradient magnitude diagram and the multi-scale and multi-angle Gaussian-Laplace filter; determining the position of an edge point through acquiring the position of a maximum value of each pixel on the energy diagram in the gradient direction; and finally acquiring the edge width of the original grayscale through calculation, thereby realizing measurement of the image Gaussian Blur. According to the method disclosed by the embodiment of the invention, the ratio between the increasing rate of the edge width acquired according to the method and the increasing rate of the blurring degree maintains certain stability, thereby avoiding the circumstance that an error occurs in a traditional evaluation method.

The invention relates to a vehicle detecting algorithm in an intelligent traffic system, in particular to a vehicle detecting algorithm based on intrinsic image decomposition, which is universal at day and night. The specific practice thereof comprises the steps of: at first, carrying out derivation and filter to an initial input image on a log-domain to convert the initial input image to a gradient domain; then calculating the difference between the gradient map of a current image and the gradient map of a background image on the gradient domain to obtain the gradient map of a moving foreground image; using the method of intrinsic image decomposition to process the gradient map of the moving foreground image to obtain the gradient map of a photogram and the gradient map of a target image; extracting the pixels with the gradient amplitude larger than a threshold T in the gradient map of the target image and using the pixels as moving target points; and aiming at a certain region, considering that the vehicle passes through the region if the number of the moving target points exceeds a certain proportion of the total number of the pixels in the region. The vehicle detecting algorithm based on intrinsic image decomposition has the beneficial effects of removing the effects of shadows and illumination and being capable of accurately carrying out detection to the vehicles at day and night in real time.

An apparatus and method of reconstructing a computed tomography (CT) image using multiple datasets of projective measurements, wherein the method of image reconstruction favors spatial correlations among the images respectively reconstructed from each of the corresponding multiple datasets. The multiple data sets each contain projective measurements of the same object taken in close temporal proximity, but taken with different detector type or configurations (e.g., different spectral components in spectral CT or different detector types in hybrid 3rd- and 4th-generation CT scanners). Reconstructed images minimizing a vectorial total variation norm satisfies the criteria of favoring images exhibiting spatial correlations among the reconstructed images and favoring a sparse gradient-magnitude image (i.e., edge enhancing image) for each reconstructed image.

The invention discloses a lane line detection method and a lane line detection device and belongs to the field of intelligent traffic. The lane line detection method comprises the following steps: acquiring an image to be detected, and determining a first region of interest in the image to be detected; determining an accumulated value of gradient magnitudes of all the pixels in the same angle interval, and determining a first gradient angle and a second gradient angle according to the accumulated value; carrying out convolution on the image in the first region of interest and a direction adjustable filter respectively substituted by the first gradient angle and the second gradient angle, so as to obtain a first matrix and a second matrix; getting absolute values of element values in the first matrix and the second matrix, and adding the first matrix with the second matrix after the absolute values of the element values are got, so as to obtain a third matrix; calculating mean value and variance of element values in the third matrix, so that a threshold value is obtained, and pixels corresponding to the element values higher than the threshold value are taken as feature points; and carrying out Hough transformation on an edge binarization image composed of the feature points, and determining a straight line model of a lane line. The lane line detection method and the lane line detection device have the advantage that accuracy of detection on the lane line is high.

The invention relates to a water sky line detection method based on gradient saliency. The method comprises steps as follows: a frame image is acquired by an optical imaging instrument, and if the image is a color image acquired by an ordinary camera, the image is subjected to standardization processing, and a 24-bit RGB (red green blue) color image is obtained; if the image is a gray image acquired by an infrared imaging instrument, the image is subjected to standardization processing, and an 8-bit gray image is obtained; the obtained standardized image is subjected to Gaussian downsampling and the like. A gradient magnitude matrix and a gradient direction matrix of the image are calculated respectively according to the type of the image acquired by the optical imaging instrument, all gradient information of an original image is reflected in the result, and accuracy of the water sky line detection result is guaranteed. Line segment detection based on a region growing algorithm is performed sequentially from high to low according to the gradient saliency, and the problem of severe noise interference due to the fact that gradient information is directly used for detection is solved.

A fissure detection method for lung lobe segmentation in 3D image data is disclosed. In this method, 3D lung image data is filtered using one or more filters based on at least one of planar structures coupled with vessel suppression, curvature computations, and local gradient magnitude and direction comparisons. Fissures are detected in the 3D lung image data based on the filtered 3D lung image data, and lung lobes are segmented from the 3D lung image data based on the detected fissures.

The invention discloses a color image edge detection method based on a quaternion theory and a canny algorithm, and belongs to the field of image processing and computer vision. The method comprises the steps that 1) an original color image is represented by a quaternion; 2) smooth filtering is carried out on the color image; 3) a color quaternion edge characteristic matrix is built and used for analyzing the edge characteristics and the structural characteristics of the color image; 4) non-maximum value restraining is carried out on the gradient magnitude of the image obtained at the step 3); 5) dual threshold method detection and edge connection are carried out. The algebraic operation and the spatial characteristic of the quaternion are utilized, all the pixel points of the color image are represented by the pure quaternion, the color quaternion edge characteristic matrix is built and used for analyzing the edge characteristics and the structural characteristics of the color image, edge neighbourhood brightness hopping and color hopping are fused, color information missing in a traditional algorithm is avoided, the introduction of noise is reduced, and locating precision and anti-noise performance are improved.

The invention provides a sub pixel edge based circle center detecting method. The method includes steps of 1, utilizing a Canny operator to performing rough positioning on an image edge, acquiring the gradient magnitude and gradient direction of integer pixel point edge; 2, establishing a positive direction taking the integer pixel point as the origin and the taking the gradient direction as the x axis, taking 7 pixel points in central symmetry in the gradient direction, wherein the symmetry center which is the integer pixel point is among the 7 pixel points, performing gray level fitting on the 7 pixel points by adopting a hyperbolic tangent function in a formula of I(x)=-b / 2 tanh(p(x-R))+h+b / 2, wherein I(x) indicates the gray level in x position, x indicates the distance between the pixel point to the origin, h indicates background gray level, b indicates the gray level difference value of the edge and the background, p is a fuzzy factor, and R is the edge position; and 3, obtaining a sub pixel edge point according to the formula in the step 2, performing least square fit on the sub pixel edge point according to a circle and obtaining circle center coordinates.

The invention discloses a finger vein recognition method by interested areas and directional elements. Existing vein recognition algorithms can be divided into two types, namely local feature utilization and global feature utilization, but the problems of long time consumption, large computing capacity and low image quality cannot be solved simultaneously. The method includes firstly reading a finger vein image, extracting an interested area by the interested area extraction method based on rotation correction, then constructing vectors by adopting gradient magnitude and direction of pixel points on finger vein lines to represent direction feature of the vein lines in the interested area, combining the direction feature and membership degree of image sub-blocks, creating feature vectors of the vein image, finally during the finger vein matching process, measuring similarity of different linear feature vectors by adopting cross-correlation coefficient, and figuring out matching results. The method is used for finger vein recognition.

The invention discloses an unmanned aerial vehicle vision wire patrol method based on gradient constraint Radon transform. The method comprises the following steps: 1) obtaining a high-voltage line image to be processed by utilizing an unmanned aerial vehicle image acquisition device and converting the obtained degraded image into a grayscale image F; 2) carrying out edge extraction on the grayscale image F to obtain an edge image F'; 3) carrying out gradient calculation on the edge image F' to obtain gradient magnitude Gk; 4) setting eight direction pixel points of the kth pixels in the edge image F' being ki, wherein i=0,1,...,7, and when the gradient magnitudes Gk of the adjacent two pixels ki are same, grouping the pixels to the same line support region; 5) for the line support region obtained in the step 4), carrying out fitting to obtain a straight line according with conditions, and carrying out follow-up processing on the fit straight line through mathematical morphology; and 6) detecting a power transmission line through Radon transform according to the fitting result obtained in the step 5). The method realizes detection accuracy of the power transmission line in the image, removes redundant edge information and improves accuracy of power transmission line identification.

The present invention provides a marked watershed segmentation-based steel grain boundary automatic extraction method. The method mainly comprises the steps of 1, subjecting a metallographic image to pre-treatment, including gradation conversion, bilateral filtering and noise reduction; 2, segmenting the pre-treated image based on the marked watershed segmentation algorithm, including the operations of obtaining a gradient magnitude image, marking foreground objects, marking background objects, correcting the gradient magnitude image, and conducting the watershed segmentation; 3, conducting the automatic extraction of a grain boundary mainly by using a canny operator. According to the technical scheme of the invention, the marked watershed segmentation algorithm is applied to the steel crystalline grain separation process, so that the over-segmentation phenomenon generated by the conventional watershed segmentation algorithm is greatly improved. The extraction accuracy of a high-precision crystalline boundary is effectively improved. The defect in the prior art that the conventional crystalline boundary is poor in effect can be overcome. Meanwhile, the method is wide in application range, high in accuracy and fast in operation speed.

Methods and apparatus for identifying objects in an image include processing the image with a gradient operator to produce a gradient magnitude and direction for each pixel. A number of different gradient directions in a portion of the processed image is determined. The portion of the processed image is identified as an object if the number of different gradient directions exceeds a threshold number of gradient directions. The determination of the gradient directions can be simplified by employing a lookup table.

Systems and methods are provided for detecting edges in an image. In an example method, the image is smoothed using a filter. A gradient magnitude and angle are determined for each pixel of the image. A non-maximum suppression is performed on the resulting image data. A double threshold with an upper and a lower threshold are applied to the resulting image data to determine the edges in the image. The upper and the lower thresholds are calculated automatically using a distribution of intensity values of the image. In example systems, an image is acquired by a camera, or other image acquisition units. An image processing unit is configured to detect the edges in the image using an adaptive threshold edge detection method.