38results about How to "Reduce outliers" patented technology

The invention discloses a binocular three-dimensional reconstruction method comprising the following steps of: 1) acquiring an image of an object to be reconstructed by two image acquisition apparatuses with the same model in order to obtain a left image and a right image; 2) calibrating the image acquisition apparatuses by a chessboard method, computing the internal and external parameter and a lens distortion coefficient, processing the left image and the right image to remove distortion in the images; 3) extracting features from the two processed images in the step 2) to obtain the feature points of the two images; 4) matching the feature points by using the feature points of the two images in the step 3) to obtain feature point pairs; 5) performing epipolar geometric constraint detection on the obtained feature point pairs to remove mismatched feature point pairs; and 6) computing three-dimensional coordinates of corresponding points, in a world coordinate system, of the obtained feature points by using the reserved feature point pairs. The binocular three-dimensional reconstruction method may reduce abnormal points and acquires an accurate three-dimensional reconstruction model.

The invention discloses a differentially expressed gene identification method based on combined constraint non-negative matrix factorization. The method comprises the following steps of 1, representing a cancer-gene expression data set with a non-negative matrix X, 2, constructing a diagonal matrix Q and an element-full matrix E, 3, introducing manifold learning in the classical non-negative matrix factorization method, conducting orthogonal-constraint sparseness and constraint on a coefficient matrix G, and obtaining a combined constraint non-negative matrix factorization target function, 4, calculating the target function, and obtaining iterative formulas of a basis matrix F and the coefficient matrix G, 5, conducting semi-supervision non-negative matrix factorization on the non-negative data set X, and obtaining the basis matrix F and the coefficient matrix G after iteration convergence, 6, obtaining an evaluation vector (the formula is shown in the description), sorting elements in the evaluation vector (the formula is shown in the description) from large to small according to the basis matrix F, and obtaining differentially expressed genes, 7, testing and analyzing the identified differentially expressed genes through a GO tool. The identification method can effectively extract the differentially expressed genes where cancer data is concentrated, and be applied in discovering differential features in a human disease gene database. The identification method has important clinical significance for early diagnosis and target treatment of diseases.

The method comprises:

• • generating a set of cost functions for foreground, background and shadow segmentation classes or models, where the background and shadow segmentation costs are based on chromatic distortion and brightness and colour distortion; and
  • applying to the pixels of an image said set of generated cost functions;

The method further comprises, in addition to a local modelling of foreground, background and shadow classes carried out by said cost functions, exploiting the spatial structure of content of at least said image in a local as well as more global manner; this is done such that local spatial structure is exploited by estimating pixels' costs as an average over homogeneous colour regions, and global spatial structure is exploited by the use of a regularization optimization algorithm.

The system is adapted to implement at least part of the method.

The invention relates to a method for purification of nucleic acids, to a kit for performing the method according to the invention and to a new application of magnetic particles for purification of a biological sample. The method according to the invention comprises the following steps: a) accommodating of the sample in a first sample vessel in an aqueous solution and lysing of the sample under non-chaotropic conditions; suspending of first magnetic particles in the solution and inserting of the first sample vessel in a sample vessel holder, wherein the sample vessel is inserted in the annular interior space of a ring magnet associated with the sample vessel holder; separating of the solution from the magnetic particles; and isolating of the nucleic acids from the solution.

The invention discloses a TDOA (Time difference of Arrival)-IMU (Inertial Measurement Unit) data adaptive fusion positioning device and method. The device comprises an inertial measurement unit used for acquiring components of the speed and the acceleration of a measured object relative to a geographic coordinate system; a TDOA positioning system used for preliminarily positioning the measured object so as to obtain position information of the measured object; an adaptive Kalman filter used for filtering a TDOA positioning system according to an adaptive Kalman algorithm, and correcting the position information acquired by the TDOA positioning system; a long short-term memory neural network unit used for predicting the position of the measured object according to a long short-term memory neural network model; and an accurate position determination module used for carrying out weighted averaging on the position information corrected by the adaptive Kalman filter and the position information predicted by the long short-term memory neural network unit, and an average value is a positioning result. The TDOA-IMU data adaptive fusion positioning device and the method provided by the invention can improve the positioning accuracy.

The invention discloses a method for fast testing the contents of chemical ingredients in Eucalyptus urophylla*E. tereticornis wood. According to the method, a mathematical model is built by using the existing near infrared spectral feature absorption peak of an Eucalyptus urophylla*E. tereticornis wood sample and the relationship between contents of each chemical ingredient, and the goal of fast testing the content of other wood chemical ingredients to be tested in Eucalyptus urophylla*E. tereticornis wood is achieved. The method comprises the following steps of (1) sample sampling; (2) sample crushing and near infrared spectrum collection; (3) sample wood chemical ingredient standard measurement; (4) spectrum treatment and modeling; (5) model inspection; (6) model application. The method for fast testing the contents of ingredients of cellulose, hemicelluloses and lignin of the Eucalyptus urophylla*E. tereticornis wood has the advantages that the implementation is simple, convenient and fast; the accuracy and the reliability are high; the test cost can be obviously reduced; the tree damage and the chemical medicine consumption are reduced, so that the goal of testing the contents of the chemical ingredients in a great amount of Eucalyptus urophylla*E. tereticornis wood can be achieved.

The invention discloses a sample clustering and feature recognition method based on integrated non-negative matrix factorization. The method comprises: 1, X = {X1, X2... XP} representing multi-view data composed of P different omics data matrixes of the same cancer; 2, constructing a diagonal matrix Q; 3, introducing graph regularization and sparse constraints into the integrated non-negative matrix factorization framework to obtain target functions O1 and O2; 4, solving the target function O1 to obtain a fusion feature matrix W and a coefficient matrix HI; solving the target function O2 to obtain a feature matrix WI and a fusion sample matrix H; 5, constructing an evaluation vector according to the fusion feature matrix W, and identifying common difference features according to the vector; 6, performing functional explanation on the identified common difference characteristics by using GeneCards; and 7, performing sample clustering analysis according to the fusion sample matrix. According to the method, the complementary and difference information of the multiple omics data can be fully utilized to identify the common difference characteristics, clustering analysis can be carriedout on the sample data provided by the multiple omics data, and a calculation method basis is provided for integrated research of different types of omics data.

The invention relates to a signal processing device (100) for processing three-dimensional [3D] image data, the 3D image data comprising two-dimensional [2D] image data (150) and thereto corresponding 2D depth data (170), and the signal processing device comprising an input (120) for receiving the 2D image data and the 2D depth data, a first outlier detector (160) for establishing a statistical depth deviation (178) of a depth value from other depth values in a first spatial neighborhood of the 2D depth data, a second outlier detector (140) for establishing a statistical image deviation (158) of an image value from other image values in a second spatial neighborhood of the 2D image data, the depth value representing a depth of the image value, and a depth value generator (180) for, in dependence on the statistical depth deviation and the statistical image deviation, providing a replacement depth value (190) when the statistical depth deviation exceeds the statistical image deviation, the replacement depth value having a lower statistical depth deviation than the depth value.

The invention discloses a method of determining physical properties of Eucalyptus cloeziana, comprising the steps of (1), collecting a sawdust sample; (2), subjecting the sawdust sample to near-infrared spectral scanning; (3), standardly determining various physical properties of wood; (4), carrying out spectral pretreatment and establishing and optimizing a model; (5), carrying out model outside inspection and selection; (6), applying the model. The method of determining modulus of elasticity, bending strength and parallel-to-grain compressive strength of Eucalyptus cloeziana according to the invention not only can provide significantly improved operating efficiency and greatly saved manpower, material and financial resources, can also ensure reduced damage to woods and give full play to the advantages of near-infrared rapid analysis, and therefore, quick determination is achieved for property indexes of large batch of wood samples of Eucalyptus cloeziana.

The invention provides a network reconstruction method and device, and the method comprises the steps: representing the state and action of an intelligent agent through a node in an original network,and carrying out the reinforcement learning of the intelligent agent in the original network based on environment return information, enabling the intelligent agent to learn structural topology information in the original network and rules for removing noise information in the original network; and obtaining a moving path formed by migrating the agent subjected to reinforcement learning from a current node to a next node in the original network, taking the next node as the current node, and repeatedly executing the step of obtaining the moving path until the nodes in the original network are traversed; taking the network formed by connecting all the moving paths as the reconstruction network of the original network, wherein the reconstruction network can effectively reduce noise and abnormal edges of the original network while reserving an important topological structure of the original network, and meanwhile some potential relation edges are added.

The invention discloses a seismic interval velocity determination method and device, and the method comprises the steps: obtaining seismic data after pre-stack time migration, determining the root-mean-square velocity and quality factor of each sample point in the seismic data, and adjusting the quality factor of each sample point to the magnitude same with that of the root-mean-square velocity according to the range of the root-mean-square velocity of each sample point; segmenting the seismic data along the time direction; extracting root-mean-square velocities corresponding to a starting point and an ending point of each section of seismic data, determining a correction coefficient of the quality factor of each sample point in each section of seismic data according to the root-mean-square velocities, and correcting the adjusted quality factor of each sample point in each section of seismic data according to the correction coefficient; and determining the seismic interval velocity according to the corrected quality factor. According to the method, high-precision seismic interval velocity can be obtained according to the corrected quality factor, and a foundation is laid for obtaining accurate results of time domain-depth domain conversion and pre-stack depth migration.

The invention provides a three-dimensional palmprint recognition method integrating multiple features and principal component analysis, which extracts geometrical features based on classification coding of shape indexes, self-adaptive correction competition coding and curved surface type feature coding, and effectively reduces the influence on the recognition rate caused by wrong distribution dueto threshold classification. Competitive coding is improved, so that the direction characteristics of the three-dimensional palmprint are expressed more accurately, block histogram statistics and principal component analysis are utilized to reduce the feature dimension and remove redundant information, so that the features are more robust to micro displacement and the discrimination is improved, Euclidean distances of the three features are used as a matching distance by weighted fusion, so that the precision and robustness of the algorithm are effectively improved, and meanwhile, a K nearestneighbor algorithm is used for realizing classification, so that the influence of abnormal values and noise in the data is effectively reduced, and the comparison time is shortened.