8 results about "Laplacian regularization" patented technology

The present application belongs to the field of image denoising, and in particular to a three-dimensional point cloud denoising method. The device and method are based on Laplace regularization subspace non-local low rank learning, and extend the previously proposed low-dimensional flow model of pixel blocks to surface blocks in point clouds. The graph Laplace regularizer of the surface is used to obtain the manifold dimension of the 3D point cloud image. Then, based on the principle that the high-dimensional information of the image point cloud is located in a low-dimensional subspace, the subspace non-local low rank factor is used to study the non-local self-similarity of the subspace, estimate the three-dimensional tensor generated by the non-local similar three-dimensional surface, and finally use the three-dimensional tensor to construct a denoising model to obtain a denoised image with better visual and quantitative indicators. The denoised image point cloud obtained by the method can better preserve the visually significant structural features, and the quantitative indicators are also good.

The invention discloses an X-ray sparse new view synthesis method based on inline prior guidance three-dimensional Gaussian splashing. Compared with an existing method based on three-dimensional Gaussian splashing, the depth constraint inline prior constructed by the method solves the problem of insufficient structure information under a sparse view angle; the constructed mask reconstructs inline prior, and the problems of inherent suboptimum and detail missing of rendered image distribution and over-fitting of three-dimensional gauss to a limited view angle under the sparse view angle condition are solved; on the basis, improved graph Laplacian regularization inline prior is introduced, adaptive weighted constraint is performed on density difference of adjacent Gaussian points, and the problems of space inconsistency and artifacts caused by non-uniform density distribution are solved. Finally, a new solution is provided for X-ray sparse new view synthesis, and the new view synthesis performance and reconstruction quality under the sparse view angle condition are improved.

The application discloses a semantic-based automatic identification method for text content index, relates to the technical field of information retrieval, and comprises initialization of sparse projection and LSH signature, iterative optimization by using Lagrange dual form and gradient update, adjustment of hash bit number, obtaining of a sharding index through a k-d tree, construction of an inverted index, compression through Delta coding, construction of an index graph based on Jaccard similarity, compression through WebGraph, generation of a low-rank basis matrix and a low-rank coding matrix by using CSNMF combined with Z-Laplacian regularization, reconstruction of the compressed inverted index after iterative optimization, and generation of a reconstructed inverted index entry. The application improves the semantic expression capability and compression effect of the index structure, improves the precision and efficiency of the index, and realizes intelligent identification of the index content through multi-resolution hash table initialization, joint feature optimization, local adaptive quantization and low-rank index reconstruction.

This application provides a ToF depth image denoising method based on a Graph Laplacian Regularized Network (GLR). The method includes: acquiring raw data from a ToF sensor; removing noise from the raw data using a GLR to obtain a denoised depth map; and post-processing the denoised depth map to remove regions with confidence scores below a threshold, resulting in a final depth map. This approach utilizes the GLR to achieve better denoising performance and does not require actual data acquisition for training; it can be trained directly using simulated Gaussian noise, exhibiting good generalization ability.

The invention relates to the technical field of industrial equipment state monitoring and signal processing, and discloses a dynamic equipment signal noise self-adaptive suppression method for a complex working condition, and the method comprises the steps: receiving an original one-dimensional time sequence signal of dynamic equipment through calculation equipment, carrying out the multi-dimensional phase space dynamics reconstruction, and generating a phase space trajectory matrix; performing differential calculation and normalization processing on the phase-space state vector to obtain a dynamic tangent vector field; combining the manifold geodesic distance and the dynamic tangent vector field to construct an anisotropic geodesic neighbor graph, and generating an anisotropic weight matrix; and constructing a graph Laplacian regularization optimization model containing a data fidelity item and a manifold smoothing item based on the weight matrix, solving to obtain a denoised phase space trajectory matrix, and inversely mapping the denoised phase space trajectory matrix into a one-dimensional reconstruction signal. According to the method, the geometric distance and the dynamic evolution direction are fused, the false neighbor points are eliminated, and the signal-to-noise ratio and the feature fidelity of the nonlinear signal of the dynamic equipment under the complex working condition are improved.

The invention provides a two-stage vehicle damage segmentation method and system oriented to an abnormal illumination condition, and relates to the technical field of computer vision and image processing. According to the method, a source domain is constructed by using a normal illumination sample, a target domain is constructed by using an abnormal illumination sample, and target domain feature representation is obtained through a backbone network; global and local attention is introduced in the first-stage training, and hierarchical clustering is combined to generate a target domain category pseudo label and a mask pseudo label; in the second stage of training, the cross-illumination features are adaptively aligned by adopting a mask level domain, and graph Laplacian regularization is constructed after convergence so as to strengthen boundary consistency. And finally, weighting multiple losses to form total loss, and realizing stable segmentation of the vehicle damage area under the abnormal illumination condition.

The invention discloses a titanium alloy blade additive manufacturing reversible deformation compensation method and system based on double-grid B splines and graded regularization, and belongs to the field of selective laser melting (SLM) additive manufacturing. The method comprises the following steps: acquiring an original CAD model and a deformed actual measurement model; rigid alignment and three-constraint corresponding point matching are carried out, an observation displacement field is extracted, and a local concave area is intelligently detected; constructing a global sparse + local dense double-grid B-spline displacement field, balancing global torsion smoothness and local recess fitting precision by adopting a hierarchical Laplacian regularization strategy, and solving through least square iteration to obtain a high-precision reverse compensation field; and negative displacement is applied to the original model, the compensation amount constraint is optimized, and a printing model is output for remanufacturing. According to the method, the combined deformation of overall torsion and local depression coexisting in the titanium alloy blade printing process can be efficiently restrained, calculation is efficient, material constitutive parameters are not needed, the method can be directly embedded into an existing additive manufacturing execution system, and closed-loop quality control is achieved.

The invention discloses an underwater hyperspectral clustering method based on bidirectional square attenuation tensor regularization, and relates to the technical field of underwater image processing. Comprising the following steps: reconstructing multispectral data in a feature dimension and a sample dimension; performing singular value decomposition on the tensor of the feature projection matrix and the tensor of the sample similar matrix, and constructing square attenuation tensors of the feature projection matrix and the sample similar matrix based on singular values; introducing a Laplacian diagram into the sample similar matrix, and constructing a hyper-Laplacian regularization constraint; self-adaptive weight is introduced to balance the effect of each view sample; and solving the target function to obtain a comprehensive similarity matrix, and performing spectral clustering on the comprehensive similarity matrix to obtain a clustering result. According to the method, singular values can be contracted adaptively, important spectral features are reserved to the maximum extent, noise and redundant spectral information are strongly suppressed, the separability of different ground feature categories in a feature space is enhanced, and robustness is achieved for interference factors such as illumination changes and sensor noise.