37 results about "Pyramid algorithm" patented technology

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.

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 relates to the cross field of geographic information and information technology, in particular to a distributed algorithm for quickly establishing a massive remote sensing image pyramid applied to the construction of National Spatial Data Infrastructure. The method realizes the algorithm for quickly establishing the massive remote sensing image pyramid by means of data distribution storage and parallel computation capabilities based on a PC (Personal Computer) cluster, through the steps of dividing N*M grids, performing pyramid establishment task distribution and scheduling and pyramid establishment and correctly dividing grids and computing grid information, and by means of a communication transfer task between a control node and a computation node; and the method mainly aims to solve the related technical problem of how to divide the N*M grids, etc. The distributed algorithm for quickly establishing the massive remote sensing image pyramid has the following advantages that: based on the data distribution storage and parallel computation capabilities, the speed of generating the massive remote sensing image pyramid is increased; and the problem of black edges causedby image block documents crossing grids is solved; therefore, the purpose of increasing the pyramid establishing speed is achieved and the pyramid establishment accuracy is ensured.

An irregular connectivity mesh representative of a surface having an arbitrary topology is processed using a non-uniform relaxation procedure. The non-uniform relaxation procedure minimizes differences between vectors normal to faces of pairs of triangles having a common edge and located within a designated neighborhood of a given vertex. The relaxation procedure may be used to construct subdivision and pyramid algorithms for performing processing operations such as upsampling, downsampling and filtering on irregular connectivity meshes. The signal processing algorithms may be utilized in applications such as smoothing, enhancement, editing, texture mapping and compression.

The invention relates to a gesture recognition method based on an image space pyramid bag of features. The method comprises the following steps of carrying out gesture division of an image with a gesture; extracting and describing features of the divided gesture image; using extracted features to train a histogram intersection core support vector machine; obtaining a gesture type to which a feature vector of the gesture image belongs according to the support vector machine, in order to achieve gesture recognition. The method combines a space pyramid algorithm and a bag of features algorithm, and describes features of overall quantity and distribution of feature points of the gesture image. By means of the histogram intersection core support vector machine, classification of gesture features is achieved, and gesture recognition is achieved further. In addition, the rate of recognizing gestures of multiple similar types is improved.

The invention discloses an identification method for a tiny leakage signal of an oil transportation pipeline, relating to the field of pipeline signal processing. The identification method comprises the following steps: selecting a db4-10 series wavelet function as a wavelet base for wavelet conversion and determining that the quantity of layers of wavelet decomposition is four; carrying out four-layer discrete wavelet decomposition on an original sound signal by adopting a MALLAT pyramid algorithm; selecting detail signals of a third scale and a fourth scale and carrying out wavelet reconstruction to obtain de-noised signals; uniformly dividing the de-noised signals according to time to obtain divided sound signal segments; carrying out short-time Fourier transform on each divided sound signal segment to obtain a conversion matrix; manufacturing a normalized energy pattern of the sound signals by utilizing the conversion matrix; and judging whether the oil transportation pipeline has tiny leakage or not according to the normalized energy pattern. According to the identification method disclosed by the invention, effective identification of the leakage signal is realized and various requirements in actual application are met.

The invention provides a tropical cyclone intensity objective determination method based on a satellite cloud chart and an RVM. The method is used for constructing a tropical cyclone (TC) intensity objective determination model based on the satellite cloud chart and the RVM (relevance vector machine). The method mainly comprises the following two aspects: 1) carrying out fusion on infrared and water vapor channel cloud charts by utilizing a Laplacian pyramid algorithm, constructing a deviation angle-gradient co-occurrence matrix with the TC center as a reference point, constructing characteristic factors closely related to TC intensity by utilizing a plurality of statistical parameters in the co-occurrence matrix and information of TC kernel scale and center latitude and the like, and establishing the TC intensity objective determination model by utilizing the RVM; and 2) based on the fused satellite cloud chart, and with each point as the reference point in sequence, constructing a deviation angle-gradient co-occurrence matrix and calculating a minimum value, a median value and a mean value of a co-occurrence matrix statistical parameter array. The method constructs the characteristic factors closely related to TC intensity by utilizing the plurality of statistical parameters of the co-occurrence matrix parameter array and information of TC kernel scale and center latitude and the like, and establishes the TC intensity objective determination model by utilizing the RVM.

The present invention belongs to the digital image processing technical field and discloses a Harris corner detection image pyramid palm print ROI (region of interest) recognition method. The method includes the following steps that: an entered palm print ROI image and a template palm print ROI image are inputted, MFRAT filtering is performed on the two images; the Harris corner detection algorithm is used to extract corners distributed on palm print main lines and wrinkles from the filtered template palm print ROI image with only one corner reserved in each small block region; the image pyramid algorithm is adopted to obtain corners distributed on the filtered entered palm print image according to the corners on the filtered template palm print ROI image; and the average BLPOC (Band-Limited Phase-Only Correlation) algorithm is adopted to obtain a final matching score according to known corner distribution; and a fixed threshold value is set according to an experiment, if the matching score is larger than the threshold value, a palm print is a true match, otherwise, the palm print is a false match. With the Harris corner detection pyramid palm print ROI (region of interest) recognition method of the invention adopted, palm print image ROI matching can be performed accurately.

The invention discloses a solar cell panel crack detection method comprising collecting a solar cell panel image; dividing the solar cell panel image into a plurality of singlechips by horizontal vertical projection, and according to the horizontal projection of singlechip broken gates, cutting the singlechips into multiple blocks; based on Laplacian Pyramid, decomposing the blocks, and using canny algorithm for edge detection of the decomposed blocks to obtain the characteristics of a solar cell panel crack; performing denoising processing on the characteristics of the solar cell panel crack;determining the position of the solar cell panel crack based on the denoised characteristics of the solar cell panel crack. The result of the edge detection based on the Laplacian pyramid algorithm is more complete and smooth, influence of partial noises can be effectively removed, and the accuracy of detection is improved.

The invention discloses a fire-fighting unmanned aerial vehicle image fusion method based on visible light and infrared thermal imaging. The method comprises the steps: using a visible light camera and an infrared thermal imaging camera of a fire-fighting unmanned aerial vehicle to capture a visible light image and an infrared thermal image respectively; converting the two pictures into an image with the size of 1280 * 720 through a size function in the OpenCV; extracting ROI vertex coordinates in the infrared thermogram and displaying the ROI vertex coordinates in the visible light image by using a red rectangular frame; fusing an image in the edge obtained in the infrared thermogram and an image of a coordinate corresponding to the visible light by using a Laplace pyramid algorithm to obtain fire point information; and after the fire point is detected, calculating the three-dimensional position of the target according to the visual information, and accurately estimating the three-dimensional position information of the fire point under the coordinate system of the fire-fighting unmanned aerial vehicle by calculating the relative height and yaw angle between the unmanned aerial vehicle and the target. The method has the advantages of being simple in structure, convenient to operate and easy to implement.

The invention discloses a double fisheye image mosaic panoramic image seam fusion method. For two fisheye unfolded images that have undergone feature registration and geometric perspective transformation correction, and overlapping areas overlap, this method first uses the overlapping areas of the two images to calculate the brightness and color adjustment function between the two images, and then converts the two images The brightness and color of the two images are adjusted to a consistent level; and then the seams of the two images are fused by using the pyramid algorithm by reasonably setting the weighted values ​​at the seams of the two images. The method not only solves the problem of uneven brightness and color difference between the two images used for splicing, but also eliminates the seam at the splicing place of the two images, so that the two images are spliced ​​together perfectly.

The invention discloses a multi-scale X-ray image enhancement method based on the characteristics of the human visual system. , and perform hard copy simulation; grayscale processing, channel decomposition to obtain cone images and rod images; use Laplacian pyramid algorithm to perform multi-scale decomposition of images; perform brightness and contrast adaptation; Threshold processing; images are merged respectively, and inverse sensing and inverse gain processing are performed on the merged images to realize the image reconstruction of the Laplacian pyramid, and the reconstructed image is obtained; the reconstructed image is normalized, and the normalized image is obtained. Perform color space conversion and gamma correction to obtain the corrected RGB color space image and output. The invention can effectively enhance the contrast and details of the X-ray image, and can better reduce the influence of noise on the X-ray image.

The invention discloses a watermark detection method and system based on SURF and pyramid algorithms. The watermark detection method comprises the following steps: S1, scaling an original image to thesame size as a second sample image; s2, transforming the second sample image into the same scale space as the original image through an SURF algorithm; s3, positioning an ROI region in the transformed second sample image through a pyramid algorithm; s4, determining a watermark position; and S5, detecting the watermark. The method has the advantages of high matching precision, high matching speedand the like.

The invention discloses a character defect intelligent detection algorithm based on edge shape matching. The algorithm comprises the following steps: on the basis of gradient data obtained after Sobel operator filtering, determining an optimal edge segmentation threshold according to the length of an edge connected domain, and obtaining a gradient direction and a coordinate of an edge contour point as matching information; according to the contour point matching degree, searching defect points in a mode of combining coarse screening and fine screening, and then marking a character defect area in a mode of searching a connected domain based on defect point filling. In order to enable the matching algorithm to meet the real-time requirement, a self-adaptive image pyramid algorithm and a search strategy of ending in advance through threshold judgment are adopted. Similarity measurement adopted by the method is not influenced by shielding, chaos, linear and nonlinear illumination changes and the like, so that the character printing defects can still be quickly and accurately positioned in an illumination change environment.

The invention provides a high-rise structure dynamic displacement measurement method based on unmanned aerial vehicle aerial photography. The method comprises the steps of acquiring a dynamic displacement video of the top end of a high-rise structure shot by an unmanned aerial vehicle; capturing a plurality of to-be-measured points of the first frame of dynamic displacement image at the top end of the high-rise structure as measurement points; using a pyramid algorithm to carry out displacement moving point capture on the multiple frames of dynamic displacement images to obtain a plurality of optical streams; calculating optical flow vectors of the plurality of optical flows by using an optical flow method to obtain an optical flow field; performing threshold segmentation on the optical flow field to divide the multiple frames of dynamic displacement images into backgrounds and moving targets; extracting a pixel coordinate of a moving target in each frame of dynamic displacement image, and calculating a displacement coordinate of a measurement point by taking the measurement point of the first frame of dynamic displacement image as a zero point coordinate to obtain dynamic displacement initial data of the top end of the high-rise structure; and correcting initial data of dynamic displacement of the top end of the high-rise structure by using suspension displacement compensation of the unmanned aerial vehicle. According to the invention, the problem of a large measurement result error caused by the fact that the camera is far away from the moving part of the measured high-rise structure can be solved.

The invention discloses a fast intelligent tracking method and device based on classified learning. A target image is quickly tracked, a corresponding control angle is determined through calculation,the control angle is transmitted to a motor control module, the platform is driven to move to enable the lens center to be aligned with the moving target all the time, and therefore the image trackingof the target is achieved. A quaternion method is introduced to carry out attitude calculation and carry out dynamic error compensation, so that the real-time performance and the stability of systemattitude sense are provided. And a pyramid algorithm is improved to layer the image, so that an optimal matching point is quickly found, and the algorithm speed is greatly improved. The method and thedevice are suitable for the ground detection, target identification, tracking and the like of the small industrial unmanned aerial vehicles.

The invention provides a multi-layer template adaptive matching target tracking method for an infrared image, and belongs to the field of image processing. The method comprises the steps: obtaining an infrared image, and carrying out the image enhancement preprocessing of the infrared image, and obtaining a first layer of an image feature pyramid; manually marking a target position on the infrared image, namely giving a target frame of a first frame; extracting images of different layers in a target frame on the infrared image by using a feature pyramid algorithm: carrying out target search on each layer by using an SSDA template matching algorithm, frame-selecting the maximum possible position of the target, and recording confidence and target moving position information at the same time; judging the confidence coefficient through an error function, and if scale change occurs, determining a new template; if morphological transformation occurs, updating the template in time; and if shielding occurs, enlarging the search area. According to the method provided by the invention, the effective features of the target can still be stored when the target is subjected to scale change, form change and shielding, and good tracking is kept.

The invention discloses a power grid slice updating method and a terminal. The method comprises the following steps: obtaining a change record of power grid equipment, combining slice geographic blocks of the change record through employing a pyramid slicing algorithm, and obtaining index change data of the slice geographic blocks; determining a display layer according to the slice geographic block, and calculating associated slice data before and after the power grid equipment data is changed in real time according to the index change data and the display layer, so as to obtain incremental slice data; and according to a file storage model of a pyramid algorithm, generating an incremental slice file from incremental slice data, and updating power grid slices based on the incremental slice file, and dynamically completing generation and pushing of a vector slice file online, so as to realize online updating of slice map service. The problem that off-line map cutting and publishing need to be carried out regularly according to the newest full-amount power grid data in the traditional slice map service is solved, and real-time updating of the power grid slice file is realized.