48 results about "Rotational invariance" patented technology

The invention belongs to the crossing field of information and ocean science and technology, and discloses a DOA estimation method for a UUV broadside parallel co-prime array. According to the method, firstly, a virtual extension array is generated by utilizing an array auto-covariance and cross-covariance matrix, then, Toeplitz completion is carried out on'holes' in the virtual array through trace norm regularization and nuclear norm regularization constraints, a complete covariance structure is recovered, and finally, an extension matrix is constructed, and rotation invariance of a signal subspace of the extension matrix is utilized, so that the covariance structure of the virtual array is obtained. The pitch angle and the azimuth angle of the target are jointly solved, automatic angle pairing is achieved, and the effectiveness and the reliability of the method are verified through simulation results. According to the method, aiming at the application challenges that the UUV broadside array space is limited and the underwater environment is complex, all virtual array elements of the broadside parallel co-prime array are fully utilized, the array freedom degree and estimation precision are remarkably improved, the target detection capacity of the UUV is effectively enhanced, and the method has high practical engineering application value.

The invention belongs to the technical field of image processing, and particularly relates to a glass substrate rotating jig alignment method and system based on visual guidance, and the method comprises the steps: constructing a complex gradient vector field according to the gradient of each pixel point in a glass substrate grayscale image, carrying out the four-order phase mapping of the complex gradient vector field, constructing a harmonic transformation field, and carrying out the four-order phase mapping of the harmonic transformation field; calculating a square frame saliency map according to the harmonic transformation field, calculating a circular saliency map based on a complex gradient vector field by using gradient convergence characteristics of solid dots, performing dual fusion on the square frame saliency map and the circular saliency map, constructing a target probability potential energy field, and searching extreme points in the target probability potential energy field to determine true reference point coordinates. And the rotating jig is driven to execute alignment compensation. According to the invention, the interference of metal grid textures, linear scratches and circular stains can be resisted, the rotation invariance is realized, and the high-precision visual alignment is realized.

This application relates to a fault diagnosis method for abnormal edge trajectory of wind turbine blades. It employs an improved wavelet packet multi-scale analysis of blade operating state images for filtering and noise reduction; it obtains blade trajectory edge features through invariant moment features to achieve translational, scaling, and rotational invariance recognition; and it utilizes a particle swarm optimization algorithm to obtain images with large edge trajectory deviations, calibrating abnormal states for fault diagnosis. This method solves the problems in existing technologies, such as the inability of edge detection operator thresholds to adapt, poor noise resistance, and difficulty in extracting abnormal blade edge trajectory features, leading to ineffective fault diagnosis. It improves fault diagnosis accuracy and, by filtering redundant data through the optimization algorithm, balances the accuracy and efficiency of intelligent recognition, providing reliable technical support for wind turbine blade fault diagnosis.

The invention discloses a geomagnetic gradient tensor depth representation learning method and system, and belongs to the technical field of information processing, and the method comprises the steps: obtaining a geomagnetic three-component data matrix of a magnetic object, and building an original gradient tensor data set; generating a gradient matrix based on the original gradient tensor data set, and performing orthogonal decomposition on the gradient matrix to obtain mutually independent rotation isovariant features and direction sensitive features; inputting the rotation isovariant features and the direction sensitive features into a rotation isovariant convolutional neural network for feature extraction; constructing a geometric constraint loss function including an angle constraint loss function and a direction constraint loss function, and optimizing the training network; the geomagnetic gradient tensor data is subjected to deep representation based on the trained network, the innovative gradient matrix orthogonal decomposition technology solves the problem that rotation invariance and direction sensitivity are difficult to consider at the same time, the feature representation accuracy is improved by about 30-40%, the processing speed is improved by 2-3 times, and the anti-interference capability is remarkably enhanced.

The invention relates to the technical field of artistic pattern recognition, and provides an artistic pattern recognition system based on artificial intelligence, which comprises a data acquisition module, a feature extraction module, a feature selection module, a texture analysis module and a result evaluation module.The artistic pattern recognition system overcomes the defects in the prior art and is reasonable in design and high in practicability. A digital image of the surface of an artwork is obtained through high-resolution digital imaging equipment, denoising, white balance correction and image enhancement processing are carried out to ensure image quality, then texture feature vectors are extracted through a rotation invariant local binary pattern method, texture features can be effectively extracted, rotation invariance is achieved, and classification robustness is improved; the screened features are input into the pre-trained deep learning model, the artistic pattern category probability is output, the deep learning model is used for learning texture features, and the generalization ability and classification performance of the model are improved.

The invention discloses a point cloud semantic segmentation method and system based on rotation invariance and a dynamic weighting mechanism, and the method comprises the steps: obtaining large-scale point cloud data in an automatic driving scene, and obtaining preliminarily purified point cloud data through a density-curvature double-constraint filtering algorithm; acquiring a neighborhood point set of the preliminarily purified point cloud data, splicing and fusing features of the preliminarily purified point cloud data to obtain rotation invariant local features, and forming a coordinate matrix by coordinate information of all neighborhood points in the neighborhood point set; processing the coordinate matrix to obtain structure sensing features; constructing a dynamic graph structure, and processing the structure perception features based on the structure to obtain context enhancement features; statistical information of the rotation invariant local features, the structure perception features and the context enhancement features is processed, and the probability that each point in the preliminarily purified point cloud data belongs to different semantic categories is obtained. According to the method, high-precision segmentation is guaranteed, and meanwhile, the requirement for computing resources can be reduced.

The application discloses a point cloud 3D object detection method not influenced by rotation transformation, comprising the following steps: (1) regarding the network weight of the first layer of the neural network as a vector set distributed in a feature space with the same dimension as the point cloud feature; (2) performing seed point sampling and neighborhood aggregation on the input point cloud data to obtain the local point cloud around each seed point; (3) performing principal component analysis on the network weight and the local point cloud; (4) aligning the weights of the network weight and the local point cloud to obtain a feature with rotation invariance; (5) inputting the local point cloud feature of step (4) into the neural network for feedforward transmission, detecting the prediction of the head output 3D object frame of the network; (6) training the neural network through gradient back propagation; and (7) after the training is completed, performing a 3D object detection task on the point cloud. According to the application, the classification accuracy of the point cloud under arbitrary rotation transformation can be greatly improved, and thus the accuracy of the 3D object detection task can be improved.

The invention discloses a magnetic target attitude measurement method based on gradient tensor. According to the method, gradient tensor measurement information on a measurement surface is utilized, the plane distribution of the equivalent attenuation magnetic moment is obtained through conversion calculation, the local maximum point projection can be regarded as the horizontal position of the target, and the influence of a slowly-varying background magnetic field is eliminated by utilizing the difference characteristic of the gradient tensor; the influence of attitude jitter of the measurement platform is overcome by using the rotation invariance of the equivalent attenuation magnetic moment information, and the projection of the maximum value point of the measurement surface has no systematic deviation; building a quasi-linear equation between the measured value and the target vertical depth and the equivalent magnetic moment by using the equivalent attenuation magnetic moment of the effective measurement point, and further accurately and quickly obtaining the target vertical depth and the equivalent magnetic moment; meanwhile, the possible solution of the equivalent magnetic moment direction of the target is estimated by using the gradient tensor characteristic values and the characteristic vectors of the plurality of effective measurement points, and the real magnetic moment direction of the target is screened out through statistical analysis, so that the target attitude measurement is realized, and the result is steady.

The application provides a two-dimensional DOA estimation method for arbitrary array single-base MIMO radar based on data rearrangement, and relates to the technical field of array sensor direction finding. The rank structure of a coherent signal covariance matrix is recovered by rearranging a received data matrix through arbitrary array element positions of a transmitting array and a receiving array; S2: eigenvalue decomposition is performed on the signal covariance matrix to obtain a signal subspace; S3: reliable rough estimation is obtained through rotation invariance; S4: an integer ambiguity vector corresponding to spatial phase ambiguity caused by array element spacing greater than half a wavelength is solved; S5: the phase of an array flow pattern with phase ambiguity is compensated, and an accurate estimation value of the 2D-DOA of the target is calculated; the high-precision 2D-DOA estimation problem in a complex scene of a coherent signal source, arbitrary array geometry and phase ambiguity caused by part of array element spacing greater than half a wavelength is solved, unambiguous and high-precision angle parameter estimation is realized, and the estimation accuracy and algorithm reliability are improved.

The application discloses a geomagnetic gradient tensor deep characterization learning method and system, and belongs to the technical field of information processing. The method comprises the following steps: acquiring a geomagnetic three-component data matrix of a magnetic object and establishing an original gradient tensor data set; generating a gradient matrix based on the original gradient tensor data set, orthogonally decomposing the gradient matrix, and obtaining mutually independent rotation invariance features and direction sensitive features; inputting the rotation invariance features and the direction sensitive features into a rotation invariance convolutional neural network for feature extraction; constructing a geometric constraint loss function comprising an angle constraint loss function and a direction constraint loss function, and optimizing a training network; and performing deep characterization on geomagnetic gradient tensor data based on the trained network. The innovative gradient matrix orthogonal decomposition technology solves the problem that rotation invariance and direction sensitivity cannot be considered simultaneously, the feature characterization accuracy is improved by about 30-40%, the processing speed is improved by 2-3 times, and the anti-interference capability is significantly enhanced.

The application discloses a kind of fast and multi-modal image matching method with scale and rotation invariance, comprising the following steps: step 1, local intensity binary conversion, step 2, FAST feature detection and projection-based scale space construction, step 3, rotate local image block and use SIFT-like descriptor to describe feature points, step 4, with Euclidean distance as the measure, use the nearest neighbor distance ratio-free brute force matching algorithm to match.The application first proposes a kind of local intensity binary conversion model, the similarity between multi-modal images after the conversion is significantly improved, can effectively reduce the nonlinear radiation difference between multi-modal images.Meanwhile the application also proposes a new scale space construction method, i.e.projection-based scale space, not only realizes scale and rotation invariance, but also can realize fast high-precision matching on multi-type large-size multi-modal images, has higher application value and wide application prospect.

The invention discloses a deep watermarking technology robust to any rotation angle, and aims to solve the problem that the robustness of digital watermarking is reduced due to rotation attack in an image transmission process. The method comprises an encoder module which is used for embedding secret information into a host image to generate a watermark-containing image; the double-noise-layer strategy comprises a rotation noise layer and a conventional noise layer, and the robustness to rotation attack and conventional attack is synchronously improved through adversarial training; the decoder module is used for integrating a rotation feature normalization device and a deformable convolutional network and enhancing rotation invariance through multi-angle feature aggregation and adaptive spatial sampling; the loss function is used for optimizing watermark invisibility and extraction accuracy in combination with embedding loss, decoding loss and adversarial loss. Compared with the existing deep watermarking technology, the method provided by the invention has the advantages that the characteristics of the rotation feature normalization device and the deformable convolutional network are fully utilized, the high watermarking extraction accuracy at any rotation angle is realized, and meanwhile, the high visual fidelity is kept.

The application relates to the technical field of image recognition, in particular to a target recognition method and device, electronic equipment and a storage medium. The application obtains a plurality of transformed feature maps by performing a plurality of affine transformations on a first feature map of a first frame of image, determines a target rotation angle through rotation invariance scores between each transformed feature map and a second feature map, and then inputs the transformed feature map corresponding to the target rotation angle into a trained target rotation equivalence encoder to obtain a template feature vector. Further, based on the rotation invariance score between a query feature vector of a second frame of image and the template feature vector, it is determined whether a candidate object in the second frame of image is a target object. In this way, continuous recognition of a target object performing three-dimensional rotation in a target video can be realized, and the accuracy of target object recognition can be improved.

The invention provides a multi-modal image matching method for scene matching navigation, which relates to the technical field of image matching, and comprises the following steps of: filtering a first modal image and a second modal image to obtain even symmetry and odd symmetry of each pixel point, further obtaining the maximum moment of each pixel point, and further obtaining boundary information; and further obtaining feature points, obtaining feature descriptors of the feature points, and performing feature matching to obtain a final matching result of the first modal image and the second modal image. The method is insensitive to radiation distortion, has rotation invariance, and solves the matching problem caused by non-linear radiation distortion of different-source images.

The application discloses a point cloud scene semantic instance joint segmentation method based on adaptive feature fusion, and specifically comprises the following steps: constructing an LPR-AFFN network; constructing a local polar representation module LPR, and performing LPR operation on the original point cloud; performing coordinate conversion according to Z-axis rotation invariance, calculating a neighborhood centroid point matrix, updating a polar angle, and combining the polar coordinates, geometric distances and original point cloud information to obtain a feature matrix; constructing a feature representation module, inputting the feature matrix into the feature representation module, defining a local centroid point, finding the neighborhood points of the centroid point to construct a local group; preliminarily performing feature extraction on the input point cloud; constructing a feature extraction module, obtaining new discriminative features after the extracted features pass through the feature extraction module; and constructing an adaptive feature fusion module, and using the adaptive feature fusion module to integrate the new discriminative feature information to construct the global perception ability of the point cloud.

The invention discloses a target object track movement mode recognition method, device and equipment and a medium, and relates to the technical field of data analysis, and the method comprises the steps: obtaining the track data of a target object, carrying out the linear interpolation and low-pass filtering smoothing processing of the track data, and obtaining a track sequence; calculating an instantaneous displacement direction and a discrete curvature based on the trajectory sequence, and determining direction quadrant information and trajectory curvature information of the trajectory sequence by using the instantaneous displacement direction and the discrete curvature; mapping and compressing the trajectory sequence direction quadrant information and the trajectory curvature information to generate a character sequence; constructing sequence templates of different track types, and comparing the sequence templates of different track types with the character sequence; the sequence template is a template representing a track moving mode of the target object; and the track movement mode of the target object is identified based on the comparison result, multiple target object track movement modes with movement and rotation invariance can be identified, and the efficiency and universality of track movement mode identification are improved.

The application discloses a kind of generation methods of rotation invariant multi-scale annular feature descriptor, to overcome the challenge that feature recognition technology is difficult to accurately describe key features due to image rotation problem in image recognition field.First, multi-scale annular domain is constructed, for the segmentation local feature around feature point, to obtain multi-scale annular feature;Then the eigenvalue of annular feature is calculated with annular feature as basic unit;Finally, the eigenvalue obtained from multiple scales is sorted to form a feature vector with rotation invariance.The feature description scheme provided by the application avoids obtaining a feature vector with rotation invariance by estimating the dominant direction of the feature point.Compared with the scheme based on the dominant direction, the feature vector provided by the application will not be disturbed by the error from the dominant direction, avoids the operation of rotating all local features to the same direction according to the dominant direction, simplifies the calculation steps of describing local features, and reduces the algorithm complexity.

The application discloses a two-stage heterogeneous remote sensing image registration method with radiation and rotation invariance, comprising the following steps: performing WLD filtering on a group of images to be registered to obtain a WLD structure saliency map and a WLD direction map sequence of the group of images to be registered; constructing a feature description vector of the group of images to be registered based on the WLD structure saliency map and the WLD direction map sequence; matching feature points between the group of images to be registered according to the feature description vector to obtain a rotation reference point pair; extracting a WLD structure feature description vector of the group of images to be registered according to the rotation reference point pair, and matching feature points between the group of images to be registered again according to the WLD structure feature description vector to obtain a plurality of pairs of final matching point pairs; and registering the group of images to be registered according to the final matching point pairs. The application can enhance registration accuracy and effect.

The application discloses a kind of based on graph semantic redirection module's remote sensing image ship target detection method and system, comprising: input remote sensing image to target detection model, carry out feature extraction by stacked convolution block, obtain shallow, middle and deep feature map and utilize feature pyramid network to carry out feature fusion;The improved detection head of feature map after fusion is introduced into graph semantic redirection module, graph semantic inference is carried out by introducing multilayer perceptron, pixel-level semantic similarity is used to construct graph structure to extract key features with rotation invariance, global semantic consistency integration is carried out using the feature integration mechanism based on exponential moving average, decoupling feature map obtains target class and position information;Target detection model is trained and tested for target detection task, and ship target detection is carried out on the remote sensing image to be measured;The application realizes task feature decoupling in detection head stage and enhances the extraction of rotation invariance semantic features, so as to realize high-precision remote sensing ship detection.

Point cloud identification device (300), comprising: a point cloud acquisition unit (320) to acquire point cloud information specifying N (N ≥ 2) points in k (k ≥ 2) dimensions; a model acquisition unit (310) to acquire a model that has a learning parameter; a rotation invariance conversion unit (330) to perform an orthogonalization of each of the basis vectors for each of the points specified in the point cloud information, and to compute a rotation invariance feature using data after orthogonalization; an inference unit (340) to identify a point cloud specified in the point cloud information using the rotational invariance feature and the model; and a result output unit (350) to output a classification result by identification by the inference unit (340).

The invention provides a wafer defect multi-angle equivalent detection method based on rotation invariance of light, and relates to the technical field of wafer defect detection. The method comprises the following steps: firstly, carrying out physical modeling according to a change rule of scattered light intensity generated by wafer defects under different oblique incidence angles after incident light irradiates a wafer; constructing a wafer defect image data set; secondly, designing two parallel and independent detection algorithm schemes, respectively adapting to two types of industrial scenes of real-time high throughput and high-precision detection, and constructing a corresponding network model; and finally, data enhancement and multi-task learning are carried out: corresponding network models are respectively trained for two parallel and independent detection algorithm schemes, so that wafer defect multi-angle equivalent detection is realized.

The application belongs to the technical field of array signal processing of near-field sources, and more particularly relates to a multi-cumulant matrix rotation-invariant near-field COLD accurate model direction finding method. A uniform COLD array is used to construct a fourth-order cumulant matrix and cascade to obtain a joint cumulant long matrix. The covariance matrix of the joint matrix is calculated and eigenvalue decomposition is performed. According to the rotation invariance between the signal subspaces, the spatial domain information and the polarization information are successfully separated from the spatial domain polarization domain. The least square method and the polarization vector relationship are used to step by step solve the azimuth angle and distance parameters and the polarization auxiliary angle and polarization phase difference parameters. The method considers the parameter estimation problem under the accurate model, considers the amplitude attenuation of the signal on different array elements, can effectively use the information of all array elements in the array, and has higher estimation accuracy.

This invention discloses a polarization-agile interference suppression method based on a polarization-frequency-controlled array radar, belonging to the field of radar anti-jamming and array signal processing technology. Under the PFDA-MIMO radar system, this invention achieves spatial position parameter estimation and globally optimal self-pairing for polarization-agile interference by utilizing the array spatial manifold rotation invariance and cyclic measure algorithms. Furthermore, it combines spatial decoupling to eliminate crosstalk between multiple interferences and uses the least squares criterion to obtain instantaneous polarization information under a single snapshot. It further reconstructs the instantaneous interference plus noise covariance matrix under a single snapshot, obtains the optimal weight vector, and performs joint space-polarization adaptive filtering. In the scenario of polarization-agile interference, it achieves high-fidelity target extraction and deep cancellation of interference energy, effectively improving the system's output signal-to-interference-plus-noise ratio and target detection capability.

The application discloses a pollen image feature extraction and recognition method based on deep learning, relates to the technical field of computer vision and image recognition, and comprises the following steps: an image preprocessing step: obtaining a target pollen image and converting the target pollen image into a single-channel grayscale data matrix; a manifold field reconstruction step: inputting the matrix into a surface reconstruction encoder to generate a pseudo-three-dimensional geometric feature map representing surface space structure; a topological feature extraction step: using a geodesic line convolution unit to perform non-Euclidean space sampling and extracting surface geometric curvature features with rotation invariance; and a Riemann classification recognition step: inputting the features into a classifier based on Riemann metric, calculating geodesic line distance in a manifold space, and determining a category label; the application solves the problem of topological structure information loss caused by rotation of a two-dimensional image by constructing a pseudo-three-dimensional geometric feature map and applying geodesic line convolution, and high-precision recognition of close species is realized.

The application discloses a kind of based on graph semantic redirection module's remote sensing image ship target detection method and system, comprising: input remote sensing image to target detection model, carry out feature extraction by stacked convolution block, obtain shallow, middle and deep feature map and utilize feature pyramid network to carry out feature fusion;The improved detection head of feature map after fusion is introduced into graph semantic redirection module, graph semantic inference is carried out by introducing multilayer perceptron, pixel-level semantic similarity is used to construct graph structure to extract key features with rotation invariance, global semantic consistency integration is carried out using the feature integration mechanism based on exponential moving average, decoupling feature map obtains target class and position information;Target detection model is trained and tested for target detection task, and ship target detection is carried out on the remote sensing image to be measured;The application realizes task feature decoupling in detection head stage and enhances the extraction of rotation invariance semantic features, so as to realize high-precision remote sensing ship detection.

The invention provides a Bayesian structure system identification method based on adaptive rotation element learning sampling. According to the method, a self-adaptive rotation element learning sampling method is provided, and on the basis of a detailed probability distribution identification result of a Bayesian structure system identification method, rotation invariance of posterior trend characteristics is utilized to adaptively rotate a sampling direction from each parameter direction related to a specific problem to each principal component direction consistent with a posterior trend, so that the probability distribution identification result of the Bayesian structure system identification method is identified. According to the method, the efficient sampler after neural network training in the method has wide universality irrelevant to problems, the re-training requirement during task change is avoided, the method is suitable for the recognition problem of a complex structure automatic refined system which is difficult to train, and therefore the method better serves the field of structure health detection.

The application belongs to the technical field of image processing, and particularly relates to a glass substrate rotating jig alignment method and system based on visual guidance, which comprises the following steps: constructing a complex gradient vector field according to the gradient of each pixel point in a glass substrate gray image, performing fourth-order phase mapping on the complex gradient vector field, constructing a harmonic transformation field, calculating a square box saliency map according to the harmonic transformation field, calculating a circular saliency map based on the complex gradient vector field by using the gradient convergence characteristics of a solid circle point, performing dual fusion on the square box saliency map and the circular saliency map, constructing a target probability potential field, searching for an extreme point in the target probability potential field to determine a true fiducial point coordinate, and driving a rotating jig to perform alignment compensation. The application can resist the interference of metal grid textures, straight-line scratches and circular stains, has rotation invariance, and realizes high-precision visual alignment.

The invention discloses a method for automatically extracting buildings in a high-resolution remote sensing image in a complex urban environment, which comprises the following steps of: acquiring spectrum, space structure and texture features of the high-resolution remote sensing image, constructing an original feature set, generating a multi-direction rotation feature cost matrix through a rotation invariance cost graph aggregation network, and dynamically aggregating the multi-direction rotation feature cost matrix; preliminary segmentation is completed through an edge constraint multi-task segmentation model, feature expression is optimized by using a scale consistency adaptive adjustment algorithm, hierarchical feature fusion is performed by relying on a multi-source remote sensing feature fusion analysis platform, and finally an extraction result is output through feature clustering and boundary optimization. The method comprehensively captures the multi-direction and multi-scale features of the building, strengthens the segmentation boundary identification degree, fully excavates the complementation value of the multi-source features, adapts to the complex urban environment, achieves the precise, efficient and automatic extraction of the building, and provides reliable space information support for the fields of urban planning, disaster emergency and the like.

The invention discloses an asteroid surface feature extraction and matching method, which comprises the following steps of: firstly, extracting candidate feature points with scale invariance and rotation invariance by using an SIFT (Scale Invariant Feature Transform) algorithm, then filtering the SIFT feature points through angular point positions detected by a FAST algorithm, and reserving the SIFT feature points which are close to FAST angular points and are consistent in direction; therefore, the reliability and matching precision of the feature points are improved. According to the method, the problem of feature matching under the conditions of cross-scale, cross-view and violent illumination change can be solved, and the method has high engineering application value.

This invention discloses an euploidy prediction method based on the fusion of blastocyst developmental dynamics and spherical harmonic decomposition. The method acquires multifocal plane image sequences of blastocysts using a time-difference imaging system and extracts dynamic parameters. After semantic segmentation and depth estimation, a three-dimensional surface model is obtained through surface reconstruction. The trophoblast cell instances are segmented to obtain three-dimensional centroid coordinates. These centroid coordinates are radially projected onto a unit sphere, and a spherical density function is constructed after excluding the inner cell mass mask. Spherical harmonic decomposition is then normalized using Monte Carlo zero-model normalization to obtain normalized power spectra at each degree. The basic morphological features, dynamic parameters, and spherical harmonic features are fused and filtered before training an ensemble learning model to output prediction results. This invention is the first to introduce spherical harmonic analysis into blastocyst assessment, capturing multi-scale spatial distribution information that global statistics cannot obtain. The power spectrum exhibits rotational invariance, and zero-model normalization eliminates cell number confounding.