16 results about "Scale-invariant feature transform" patented technology

The application provides a kind of building facade disease image recognition and positioning method, by unmanned aerial vehicle with camera lens is directly opposite the angle of building facade and is shot to obtain multiple building facade local images;Data set is obtained from positive image sample and negative image sample;Building facade disease image recognition network model is constructed, and building facade disease image recognition network model includes skeleton network, neck network and detection head network;The optimal building facade disease image recognition network model is obtained;Disease prediction image is obtained, and scale invariant feature transform algorithm SIFT and random sample consensus algorithm RANSAC are used for splicing to obtain the final building facade overall recognition image.The method can realize the rapid and accurate identification of building facade disease, can improve the detection efficiency, has relatively accurate identification effect, has strong practicality and engineering significance.

The application discloses a target identification method based on monitoring video and relates to the technical field of target identification. The method comprises the following steps: acquiring a plurality of regional monitoring videos, extracting a preliminary monitoring image key frame sequence by optimizing an interframe difference method, and screening an optimized monitoring image key frame sequence by a secondary clustering method; adopting a scale invariant feature transformation algorithm to perform feature matching on the optimized monitoring image key frame sequence, obtaining a monitoring image sequence with the same feature, and performing image splicing on the monitoring image sequence by a video splicing method based on a random sample consensus algorithm, thereby obtaining a spliced monitoring image sequence; performing fusion on the spliced monitoring image sequence by a video fusion algorithm based on an adaptive threshold, thereby obtaining a fused monitoring image sequence; and performing identification on the optimized key frame sequence and the fused monitoring image sequence respectively by a target identification algorithm, obtaining a first confidence degree and a second confidence degree, and obtaining a corrected confidence degree by using an adaptive weighted fusion method.

The embodiment of the invention discloses a material label detection method and device, electronic equipment and a storage medium, and relates to the technical field of industrial visual inspection.The method comprises the steps that a label sample of a to-be-detected material is obtained, a drawing template is obtained according to a product number, and region frame selection is conducted on the drawing template through a yolov8 model; carrying out feature point detection by adopting SIFT (Scale Invariant Feature Transform) to obtain feature points, matching the feature points to obtain matching points, and mapping the label sample to the space of the drawing template according to the matching points; comparing the label sample with a drawing template through contour comparison, pixel absolute value comparison and text comparison, combining differences and filtering allowable offset to obtain a difference region of the drawing template and the material label sample; and switching and displaying the difference areas by using a GIF dynamic display technology, and carrying out frame selection and highlighting on the difference areas. The problems that in the prior art, manual comparison is relied on, efficiency is low, mistakes and omissions are prone to occurring, and identification and rapid difference positioning are difficult are solved.

The application provides a computer vision-based automatic identification and positioning method for chip components, which includes the following steps: establishing the conversion relationship between the pixel coordinate system and the manipulator coordinate system, constructing a matching template based on the scale invariant feature transform (SIFT), and calculating the angle recognition error through rotation calibration; the formal target identification and positioning includes the following steps: preprocessing (the preprocessing includes converting the color space, bilateral filtering, enhancing the contrast, performing the morphological opening and closing operation, and detecting the contour using the Canny operator), matching the target by using the matching algorithm provided by OpenCV, identifying the chip component, and then calculating the coordinates and angle of the identified chip component in the manipulator coordinate system to achieve the purpose of positioning the identified chip component. The application has stronger universality, wider application range, and can better protect the chip component to be tested. In addition, the application is also applicable to non-polar chip components, and only the corresponding template needs to be replaced.

The invention is suitable for the technical field of computer vision, and provides a GraphMama-based dual-view image matching method, which comprises the following steps of: constructing a model which comprises an InitialProjection unit, a Preminarial Local ContextEncoding unit, a Consensus-Aware Learning Block unit, a GraphMama unit and an Inlier Predictor unit, and constructing a model which comprises an InitialProjection unit, a Preminarial Location unit, a Consensus-Aware Learning Block unit, a GraphMama unit and an Inlier Predictor unit; the method comprises the following steps of: performing high-dimensional feature mapping, local neighborhood enhancement, global consistency optimization and long-range dependence modeling by taking initial matching point pairs of a double-view image extracted by SIFT (Scale Invariant Feature Transform) as input, and performing Consensus-Aware Learning Block and GraphMama iteration for four times; and training the model by using supervised data sets such as YFCC100M, SUN3D and the like, and finally outputting correct matching point pairs and an essential matrix. According to the GraphMama-based double-view-angle image matching method, through the limitation that traditional MLP independently processes matching pairs and RANSAC robustness is insufficient, long-range dependence of long-sequence matching pairs is accurately captured, on YFCC100M and SUN3D data sets, mAP indexes of relative attitude estimation and feature matching are remarkably superior to those in the prior art, complex scenes such as view angle changes and shielding can be dealt with, and the method has the advantages of being high in adaptability, high in robustness and the like. The method can adapt to computer vision tasks such as image stitching and vision positioning.

The invention provides a digital ray image splicing and identification method and system based on high contrast characteristics. The method comprises the following steps: carrying out Gaussian filtering denoising and adaptive histogram equalization preprocessing on an aerospace digital ray image; extracting multi-scale high-contrast features by using a local binary pattern algorithm and carrying out series fusion; matching feature points through a scale invariant feature transformation algorithm, and estimating an affine transformation model in combination with a random sampling consistency algorithm to realize panoramic stitching; and constructing a convolutional neural network, and training the model by using a cross entropy loss function to complete panoramic image intelligent identification. The system correspondingly comprises a preprocessing module, a feature extraction module, a panoramic stitching module, an intelligent identification module and a data storage module. According to the method, the problems of low splicing precision and low recognition accuracy when the spaceflight digital ray image is processed by a traditional method are effectively solved, the image recognizability, the splicing precision and the target recognition accuracy are improved, and the requirements of detection of the internal quality of spaceflight parts and defect recognition are met.

The application discloses a kind of aerial photograph image difference comparison method, device, equipment and medium, by obtaining the first original aerial photograph image and second original aerial photograph image to be compared, using scale invariant feature transform algorithm to the first original aerial photograph image after pre-processing and the second original aerial photograph image after pre-processing are handled, obtain first feature image and second feature image;The similarity index between the first feature image and the second feature image is calculated;When the similarity index exceeds preset similarity threshold, the difference gray scale chart between the first original aerial photograph image and the second original aerial photograph image is calculated, and the difference gray scale chart is segmented according to threshold value to obtain gray contour line, and is labeled according to gray contour line, obtains aerial photograph image difference comparison result, the difference point between the aerial photograph of different period can be quickly identified and labeled in the present application, reduce the workload of relevant personnel, improve production efficiency.

The invention relates to the technical field of soil moisture detection, in particular to a method and a system for detecting moisture distribution pattern and moisture content among soil particles. Firstly, a stress balance method is adopted for preparing and drying a test soil sample, and the structural integrity of the soil sample is kept; gradually adding water by using a dripping method, and synchronously collecting the weight change of the soil sample and microscopic image data; performing registration on the image sequence by using an SIFT (Scale Invariant Feature Transform) algorithm, including feature point detection, descriptor generation, feature point matching and RANSAC (Random Sample Consensus) optimization, and eliminating the influence of expansion deformation of the soil sample; and finally, establishing a moisture-optical correlation model, converting the image into an HSV color space, establishing a mapping relationship between the pigment tracer parameter and the moisture content by using a convolutional neural network, and generating a moisture distribution thermodynamic diagram. According to the method, interference of detection on moisture distribution is avoided, registration under the expansion condition is achieved, and the moisture distribution pattern is captured more accurately.

The invention relates to a multispectral image registration method, in particular to a multispectral image registration method based on improved SIFT (Scale Invariant Feature Transform) features. The method comprises the following steps: step 1, preprocessing multispectral remote sensing image data, wherein the multispectral remote sensing image data comprises a reference multispectral image and a to-be-registered multispectral image; step 2, extracting image gradient features based on the preprocessed multispectral remote sensing image data; extracting edge corner point features based on image gradient features; step 3, extracting an improved SIFT feature based on the image gradient feature and the edge corner feature; 4, performing homonymy point matching calculation on the reference multispectral image and the multispectral image to be registered based on the improved SIFT features to obtain a transformation matrix; and step 5, converting the multispectral image to be registered into the reference multispectral image based on the transformation matrix to obtain a final registration result. According to the method, the number of effective feature points in the image registration process can be increased, and rapid and accurate image registration is realized.

The embodiment of the application discloses a seal image space registration method and device, which can be used in the financial field or other technical fields, and the method comprises the following steps: extracting scale invariant feature transform features of a reserved seal image to obtain a first feature set; extracting scale invariant feature transform features of a to-be-inspected seal image to obtain a second feature set; matching each feature in the first feature set and the second feature set, in the feature matching process, automatically selecting appropriate hyperparameters by using adaptive hyperparameter search, screening feature pairs according to the hyperparameters to obtain a first matching feature pair, and then generating a first homography transformation matrix according to the first matching feature pair, wherein the hyperparameters comprise a feature pair matching threshold; and performing perspective transformation on the to-be-inspected seal image based on the first homography transformation matrix to obtain a space-registered to-be-inspected seal image. The application helps to improve the efficiency of seal authenticity inspection.

The application discloses a kind of weak light image enhancement-based crop three-dimensional reconstruction method, its method includes weak light crop image enhancement and crop three-dimensional reconstruction: the weak light crop image enhancement includes acquisition weak light crop image, weak light crop image enhancement, main function is to enhance the brightness of crop image under the weak light environment collected, to improve image quality;The crop three-dimensional reconstruction includes scale invariant feature transform algorithm (SIFT) feature detection and matching based on, based on motion in structure (SFM) spatial point cloud reconstruction, main function is to obtain key point as feature point and obtain feature point description vector, application Euclidean distance determination obtains matching point pair set, combined with camera internal and external parameter obtains spatial point cloud and camera pose and carries out crop three-dimensional reconstruction;Realize the accurate perception of crop of agricultural machinery equipment in weak light operation environment.

The invention discloses a terrestrial ecosystem carbon monitoring satellite three-vision stereo image construction and dense matching DSM acquisition method, which comprises the following steps: carrying out 5 * 5 block SIFT registration and RANSAC gross error removal on 19-degree foresight, rearview panchromatic and multispectral images, establishing an eight-parameter affine model, carrying out bicubic convolution resampling, and then carrying out DDS (Dense Scale Invariant Feature Transform) calculation on the DDS; multiplicative fusion is carried out in a W * W window according to a red-green-blue three-waveband linear fitting coefficient, and a 3.5 m four-waveband fusion image is generated; converting the four-band DN values of the front-view, front-view and rear-view fused image into a single band in a geometric average manner, and binding a corresponding RPC (Remote Procedure Call); initializing the equal-height layer grid; constructing n layers of 2-time resolution decreasing pyramids; from the top layer to the bottom layer, elevation searching is carried out at 1.5 times of grid step length, three-view images are projected through RPC, the minimum cost of 5 * 5 window correlation coefficients is taken, SGM is used for aggregating the optimal elevation, and DSM is refined and output layer by layer. According to the method, the consistency of the resolution and the spectrum of the three-view image of the carbon monitoring satellite is realized, the DSM precision and efficiency are improved, and the method is suitable for inversion of forest canopy height and carbon reserves.

The invention discloses a deep sea bottom image splicing method based on Bayesian optimization and an HVS attribute model, and the method comprises the following steps: carrying out the image feature extraction through employing a scale invariant feature transform (SIFT) algorithm, and carrying out the feature matching; a Bayesian model is adopted to optimize image features, and the image registration precision is improved; performing visual perception quantification by adopting a human vision system HVS, establishing an attribute relation model, and detecting an optimal seam line; and carrying out multi-band fusion to obtain a spliced image. According to the method, a mode of optimal suture line detection and multi-band image fusion is adopted, so that smooth transition of the image in an overlapping region is natural, a seam line is obviously eliminated, actual engineering requirements can be met, and a new solution is provided for large-range environment perception of a deep seabed exploration sampling vehicle.

The invention discloses a double-view feature matching method based on space and channel feature enhancement, which comprises the following steps of: 1, giving an image pair consisting of two images, and firstly extracting key points and corresponding descriptors from the two images by utilizing scale invariant feature transformation; 2, calculating an initial disordered motion vector set from the initial matching set; and step 3, designing a filtering module by using a feature enhancement technology, gradually correcting wrong motion vectors, and effectively capturing context information at the same time. And 4, recovering the ordered vector into a disordered motion vector by using a graph attention network. And 5, predicting a correct / wrong matching classification result by using a multi-layer perceptron. And step 6, estimating an essential matrix by using a weighted eight-point algorithm according to a classification result. And iteratively executing for 6 times, jointly optimizing network training through the cross entropy loss of the classification result and the real category and the regression loss of the prediction and the real essential matrix, and finally constructing a high-performance feature matching model.

Implementation schemes typically involve evaluating similar content-based images. In some implementation schemes, one method includes receiving a first image, wherein the first image includes at least one first object. The method also includes receiving a second image, wherein the second image includes at least one second object. The method further includes calculating a Structural Similarity Index (SSIM) value based on at least one first object and at least one second object. The method also includes calculating a Scale Invariant Feature Transform (SIFT) value based on at least one first object and at least one second object. The method further includes calculating a histogram value based on at least one first object and at least one second object. The method also includes calculating a similarity score based on the SSIM value, SIFT value, and histogram value.

The application provides a solid-state laser radar-camera tightly coupled pose estimation method, step one: performing scale invariant feature transform (SIFT) feature point detection and descriptor extraction on two adjacent image frames of a camera; step two: projecting non-repeated scanning point clouds of two adjacent solid-state laser radars on corresponding image frames to obtain depth maps, and then converting the depth maps into three-dimensional matching points; step three: performing a RANSAC algorithm based on rigid transformation on the three-dimensional matching points to obtain adjacent camera poses and calculate adjacent solid-state laser radar poses; step four: calculating the curvature of the solid-state laser radar point clouds, and respectively constructing a point-to-plane iterative closest point (ICP) algorithm and a point-to-line ICP laser radar factor according to the division of planar points and edge points according to the curvature; step five: extracting 2D registration points from the image to construct a camera factor based on epipolar geometry; step six: fusing laser radar optimization factors and camera optimization factors, and performing factor graph optimization to finally obtain fine solid-state laser radar pose estimation.