1054 results about "Gradient direction" patented technology

A cardiovascular prosthetic valve, the valve comprising an inflatable cuff comprising at least one inflatable channel that forms, at least in part, an inflatable structure, and a valve coupled to the inflatable cuff, the valve configured to permit flow in a first axial direction and to inhibit flow in a second axial direction opposite to the first axial direction, the valve comprising a plurality of tissue supports that extend generally in the axial direction and that are flexible and/or movable throughout a range in a radial direction.

Embodiments of the present invention relate to methods and systems for detection and delineation of text characters in images which may contain combinations of text and graphical content. Embodiments of the present invention employ intensity contrast edge detection methods and intensity gradient direction determination methods in conjunction with analyses of intensity curve geometry to determine the presence of text and verify text edge identification. These methods may be used to identify text in mixed-content images, to determine text character edges and to achieve other image processing purposes.

The present invention provides a fast high-precision geometric template matching method enabling rotation and scaling functions. According to the method, based on image edge information, with the sub-pixel edge points of a template image adopted as feature points, the tangent directions of the gradient directions of a target image adopted as feature lines, and based on the local extremum points of a similarity value, the similarity and the candidate positions of the template image and the target image are calculated from fine to rough through adopting a Pyramid algorithm and according to a similarity function; and pixel-level positioning accuracy is obtained at the bottommost layer of a Pyramid, the least squares method is adopted to carry out fin adjustment, so that sub-pixel positioning accuracy, higher-precision angle and size scaling accuracy can be achieved. The method can realize fast, stable and high-precision positioning and identification of target images which are moved, rotated, scaled and is partially shielded, and where the brightness of illumination changes, illumination is uneven, and cluttered background exists. The method can be applied to situations which require machine vision to carry out target positioning and identification.

Object segmentation and tracking are improved by including directional information to guide the placement of an active contour (i.e., the elastic curve or ‘snake’) in estimating the object boundary. In estimating an object boundary the active contour deforms from an initial shape to adjust to image features using an energy minimizing function. The function is guided by external constraint forces and image forces to achieve a minimal total energy of the active contour. Both gradient strength and gradient direction of the image are analyzed in minimizing contour energy for an active contour model.

A computed tomography apparatus has a gantry with a measuring opening, with an x-ray source movable around the measuring opening for irradiating an examination area from different directions, a detector for registering corresponding sets of projection data, and at least one support table having a support plate. The gantry can be moved into a use position independently of the support table and the support table is fashioned such that the support plate extends through the measuring opening when the gantry assumes a use position.

A method and system generate an enhanced output image. A first image is acquired of a scene illuminated by a first illumination condition. A second image is acquired of the scene illuminated by a second illumination condition. First and second gradient images are determined from the first and second images. Orientations of gradients in the first and second gradient images are compared to produce a combined gradient image, and an enhanced output image is constructed from the combined gradient image.

A lane robust identification method for warning departure is that first conducts conditional edge detection on the left and right local windows, and constructs the corresponding gradient direction map. Then, according to the histogram processing method, the gradient direction range of lane edge points on every local window. Aiming at the continuity features of direction and space of lane edge points, the filtering algorithm of the candidate and effect point collection is put forward and every window is filtered. Finally, according to the established effective point collection corresponding to each window, the rapid and accurate extraction of lane symbol is realized though the Kalman filter method based on scalar processing.

A method of characterizing a word image includes traversing the word image stepwise with a window to provide a plurality of window images. For each of the plurality of window images, the method includes splitting the window image to provide a plurality of cells. A feature, such as a gradient direction histogram, is extracted from each of the plurality of cells. The word image can then be characterized based on the features extracted from the plurality of window images.

The invention discloses a face recognition method of a deep convolutional neural network, which reduces the time complexity, and enables a weight in the network to still have a high classification capacity under the condition of reducing the number of training samples. The face recognition method comprises a training stage and a classification stage. The training stage comprises the steps of (1) randomly generating a weight wj between an input unit and a hidden unit and an offset bj of the hidden unit, wherein j equals to 1,...,L and represents the number of the weight and the offset, and the total number is L; (2) inputting a training image Y and a label thereof, by using a forward conduction formula hw, b(x)=f(W<T>x), wherein hw, b(x) is an output value, x is input, and an output value hw, b(x<(i)>) of each layer is calculated; (3) calculating the offset of the last layer according to a label value and an output value of the last layer; (4) calculating the offset of each layer according to the offset of the last layer, and acquiring the gradient direction; and (5) updating the weight. The classification stage comprises the steps of (a) keeping all parameters in the network to be unchanged, and recording a category vector outputted by the network of each training sample; (b) calculating a residual error delta, wherein delta=||hw, b(x<(i)>)-y<(i)>||<2>; and (c) classifying a tested image according to the minimum residual error.

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 improved image edge detection method. The improved image edge detection method comprise steps of S1, performing smoothing processing on an image and using an improved media filter to suppress noise, S2, obtaining a difference between a horizontal direction and a perpendicular direction through first-order-partial derivatives of directions of x,y,45 degrees and 135 degrees and thus obtaining a gradient amplitude and a gradient direction, S3, performing non-maximum value inhibition on the gradient amplitude, S4, using a gradient histogram to solve a high threshold and a low threshold and then using a double-threshold algorithm to perform edge detection on the image, and S5, performing sharpening processing and connecting the edges to obtain a final edge image. The improved image edge detection method use weighted media filtering to replace gauss filtering, uses the partial derivatives of four directions, uses the gradient histogram to determine the high threshold and the low threshold, reduces detection errors, improves detection accuracy, and makes the counter of the edge image more clear and continuity better.

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.

The present invention is applied, for example, to resolution conversion. An edge gradient direction v1 with the largest gradient of pixel values and an edge direction v2 orthogonal to the edge gradient direction v1 are detected. Edge enhancement and smoothing processing are performed in the edge gradient direction v1 and in the edge direction v2, respectively, to generate output image data D2.

The invention discloses a real-time robust far infrared vehicle-mounted pedestrian detection method. The method comprises the steps of catching a potential pedestrian pre-selection area in an input image through a pixel gradient vertical projection, searching an interest area in the pedestrian pre-selection area through a local threshold method and morphological post-processing techniques, extracting a multi-stage entropy weighing gradient direction histogram for feature description of the interest area, inputting the histogram to a support vector machine pedestrian classifier for online judgment of the interest area, achieving pedestrian detection through multi-frame verification and screening of judgment results of the pedestrian classifier, dividing training sample space according to sample height distribution, building a classification frame of a three-branch structure, and collecting difficult samples and a training pedestrian classifier in an iteration mode with combination of a bootstrap method and an advanced termination method. According to the real-time robust far infrared vehicle-mounted pedestrian detection method, not only is accuracy of pedestrian detection improved, but also a false alarm rate is reduced, input image processing speed and generalization capacity of the classifier are improved, and provided is an effective night vehicle-mounted pedestrian-assisted early warning method.

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.

The invention discloses a random convolutional neural network-based high-resolution image scene classification method. The method comprises the steps of performing data mean removal, and obtaining a to-be-classified image set and a training image set; randomly initializing a parameter library of model sharing; calculating negative gradient directions of the to-be-classified image set and the training image set; training a basic convolutional neural network model, and training a weight of the basic convolutional neural network model; predicting an updating function, and obtaining an addition model; and when an iteration reaches a maximum training frequency, identifying the to-be-classified image set by utilizing the addition model. According to the method, features are hierarchically learned by using a deep convolutional network, and model aggregation learning is carried out by utilizing a gradient upgrading method, so that the problem that a single model easily falls into a local optimal solution is solved and the network generalization capability is improved; and in a model training process, a random parameter sharing mechanism is added, so that the model training efficiency is improved, the features can be hierarchically learned with reasonable time cost, and the learned features have better robustness in scene identification.

The invention relates to a high-voltage transmission line fault identification method based on computer vision, which relates to the technical field of high-voltage transmission line running state monitoring. The invention aims at solving the problem of high false alarm rate of the existing high-voltage transmission line on-line monitoring system. 11) carrying out edge detection on the transmission line image according to the edge detection algorithm, a strong edge image is obtained, and edge endpoints and edge directions are obtained from the strong edge image. Since the gradient direction ofthe edge endpoints is perpendicular to the edge direction, an edge connection window is selected according to the gradient direction of the edge endpoints, and edge connection points are selected inthe edge connection window according to a Hough transform method, and the edge connection points are connected into an edge image. Step 2, screening the transmission lines from the edge images of thetransmission line images by adopting a transmission line detection algorithm based on phase grouping; Step 3, the transmission conductor is processed to identify the fault on the transmission line. Itis used to identify transmission line faults.

The invention discloses a rapidly converged scene-based non-uniformity correction method, wherein the aim of non-uniformity correction is achieved by minimizing interframe registration error of two adjacent images. The method mainly comprises the following steps of: initializing gain and offset correction parameters and acquiring an uncorrected original image; acquiring a new uncorrected original image, and carrying out non-uniformity correction on the new uncorrected original image and the previous uncorrected original image by utilizing the current non-uniformity correction parameters; obtaining relative displacement, scene correlation coefficient and interframe registration error of two corrected images by utilizing an original point masking phase correlation method; and updating correction parameters along the negative gradient direction by adopting a steepest descent method. The method disclosed by the invention has the advantages of high correction accuracy, fast convergence speed, no ghost effect and low calculated amount and storage content and is especially applicable to being integrated into an infrared focal plane imaging system, and the effect of improving imaging quality, environmental suitability and time stability of an infrared focal plane array is achieved.

A block input component of a video encoding pipeline may, for a block of pixels in a video frame, compute gradients in multiple directions, and may accumulate counts of the computed gradients in one or more histograms. The block input component may analyze the histogram(s) to compute block-level statistics and determine whether a dominant gradient direction exists in the block, indicating the likelihood that it represents an image containing text. If text is likely, various encoding parameter values may be selected to improve the quality of encoding for the block (e.g., by lowering a quantization parameter value). The computed statistics or selected encoding parameter values may be passed to other stages of the pipeline, and used to bias or control selection of a prediction mode, an encoding mode, or a motion vector. Frame-level or slice-level parameter values may be generated from gradient histograms of multiple blocks.

A device for determining an edge histogram of an image based on information about a gradient strength and a gradient direction of a local gradient in an image content of a partial image of the image includes an allocator which is implemented to allocate the information about the gradient strength and the gradient direction based on an allocation regulation to an edge image in order to obtain edge type information. The allocation regulation is selected such that with a predetermined gradient direction at least three different allocated edge types exist mirroring the different gradient strengths. The device further includes an edge histogram determiner which is implemented to determine the edge histogram based on the edge type information so that in the edge type histogram at least three edge types having different allocated gradient strengths may be differentiated.

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.

The invention provides a diffusion-tensor imaging method, which comprises the following steps of: respectively performing K space under sampling on an imaged target in each diffusion gradient direction in the same variable density sampling form to acquire K space under sampling data of each diffusion gradient direction; selecting the K space under sampling data of any diffusion gradient direction in the K space under sampling data of each diffusion gradient direction as reference K space data, and converting the reference K space data to acquire a reference image; making a difference between the K space under sampling data of each diffusion gradient direction and the reference K space data to acquire differential chart K space under sampling data of each diffusion gradient direction; rebuilding the differential chart K space under sampling data of each diffusion gradient direction to acquire a differential chart of each diffusion gradient direction; and combining the differential chart of each diffusion gradient direction and the reference image to acquire a diffusion-tensor image in each diffusion gradient direction. The invention also provides a diffusion-tensor imaging system at the same time.

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.

A guide wire assembly (1) includes a guide wire (2) having a main body section (22), a front-end angular section (21) and a base-end angular section (23). The guide wire assembly (1) further includes a storage section for storing the guide wire (2) inside a hollow section of a substantially annularly wound pipe body (51). The base-end angular section (23) is arranged so that it is dynamically stabilized in elasticity, in a state in which a bent direction thereof is aligned with a direction of curvature of the pipe body (51). When the base-end angular section (23) passes through the pipe body as the guide wire (2) is delivered from the storage section (5), a state is maintained in which the bent direction of the base-end angular section (23) is aligned with the direction of curvature of the pipe body (51), and the direction of the front-end angular section (21) is maintained at a predetermined direction with respect to the storage section (5).

The invention provides a target tracking method based on multi-characteristic adaptive fusion and kernelized correlation filtering technology. The method comprises steps of according to target position and the dimension of the previous frame tracking, acquiring a candidate region of target motion; extracting histogram characteristics and color characteristics in the gradient direction of the candidate region, fusing the two kinds of characteristics, carrying out Fourier transform so as to obtain a characteristic spectrum and then calculating kernelized correlation; determining the position and the dimension of the target at the current frame, and acquiring a target region; extracting histogram characteristics and color characteristics in the gradient direction of the target region, fusing the two kinds of characteristics, carrying out Fourier transform so as to obtain a characteristic spectrum and then calculating kernelized self-correlation; designing the adaptive target correlation and training a position filter model and a dimension filter model; and using a linear interpolation method to update the characteristic spectrums and the related filters. According to the invention, the discrimination capability of the models is improved; robustness of the target tracking of the target in a complex scene and the appearance change is improved; calculation complexity is reduced; and tracking timeliness is improved.

The invention provides a water surface optical visual image target area detection method based on gradient information fusion. The water surface optical visual image target area detection method based on gradient information fusion adopts two sliding window modes and calculates the longitudinal gradient and the transverse gradient of a water surface optical visual image. The information of the two gradients is fused, the position of an area is marked through a connected component detection method, and the target area is marked according to the final boundary of a target. The water surface optical visual image target area detection method based on gradient information fusion combines sea boundary area characteristics of the water surface image, respectively extracts target boundary information in the longitudinal gradient and the transverse gradient directions of the water surface optical image, determines the attribute of the sea boundary line and division of an image processing space, determines the area type attribute of the target boundary according to the fused information, and finishes scanning and classification of pixels according to the property of the boundary. As the water surface optical visual image target area detection method based on gradient information fusion fuses the information in the two gradient directions by combining the characteristics of the sea boundary lines, reduces the range of the processing area, reduces influence from noise, avoids calculation and processing of the whole image space, and saves calculation time.