45 results about "Robust least squares" patented technology

The present invention refers to 3D rotational X-ray imaging systems for use in computed tomography (CT) and, more particularly, to a fast, accurate and mathematically robust calibration method for determining the effective center of rotation (I) in not perfectly isocentric 3D rotational C-arm systems and eliminating substantially circular ring artifacts (RA) which arise when using such a CT scanner system for acquiring a set of 2D projection images of an object of interest to be three-dimensionally reconstructed. For this purpose, a C-arm based rotational CT scanner comprising at least one radiation detector (D) having an X-radiation sensitive surface exposed to an X-ray beam emitted by at least one X-ray tube (S), each rotating along a non-ideal circular trajectory (TF, TCD) about an object of interest to be three-dimensionally reconstructed from a set of 2D projection images is used for providing geometrical calibration data by scanning a calibration phantom from a plurality of distinct projection directions and calculating, for each projection direction, the 3D positions of the X-ray tube's focal spot and the X-ray detector's center. For approximating the exact 3D position and angular direction of the axis of rotation about which the at least one X-ray tube and the at least one radiation detector rotate, a circular regression technique using a number of mathematically robust least squares fits is applied.

The invention discloses a robust iris region segmentation method based on specific boundary detectors, which comprises the following steps: S1, constructing a left and right inner boundary, left and right outer boundary and upper and lower eyelid boundary iris specific boundary training data set and a candidate feature set; S2, constructing the six iris specific boundary detectors by using a cascaded self-adaptive learning algorithm; S3, positioning an iris inner boundary by utilizing the left and right inner boundary detectors and weighted Hough transform; S4, positioning an iris outer boundary by utilizing the left and right outer boundary detectors and the weighted Hough transform; and S5, positioning an upper eyelid boundary by utilizing the upper and lower eyelid boundary detectors and a robust least square method. By utilizing the robust iris region segmentation method, an iris effective area can be accurately segmented in an iris image containing a lot of noise, so that the accuracy, the robustness and the usability of an iris identification system are improved. The robust iris region segmentation method can be widely applied to various application systems for identity recognition and security defense by using iris recognition.

The invention discloses a method for detecting abnormal data of an aerodynamic data set, including S1, obtaining three sets of independent variables and dependent variables according to defined independent variable and dependent variable relationship pairs; S2, based on a robust least squares regression method, and at the same time Regression is performed on the three sets of independent variables and dependent variables to obtain three β i , i=1,2,3, where β i is the coefficient; S3, respectively calculate the standardized predicted value and standardized residual of the three groups of dependent variables, and obtain three new two-dimensional data sets; S4, perform single-link hierarchical aggregation on the three new two-dimensional data sets obtained in S3 Classes, the largest class is used as the pure data set for abnormal data set detection, and the abnormal data of the three-time clustering is merged as the abnormal data of the whole data.

The invention provides a method for processing global position system (GPS) deformation monitoring data. The method for processing the GPS deformation monitoring data comprises (1) obtaining present epoch observation data and broadcast ephemeris data of a base station and a monitoring station, and forming a double-difference observation equation by using phase observation values; (2) setting a maximum deformation quantity between two epochs; (3) adopting a Cholesky to resolve and construct an ambiguity search space; (4) searching the ambiguity, and enabling a variance ratio to be larger than 3; (5) obtaining fixed solution coordinates X, Y and Z of a single epoch whole cycle ambiguity and a convariance matrix of the single epoch whole cycle ambiguity of a monitoring point; (6) using an average gap method to carry out deformation detection on a deformation value between a present epoch and a previous epoch; (7) after finishing the detection of (6), using a robust least square static solution by superposition of two epoch observation equations as a result of the present epoch, or obtaining a result of the present epoch according to a robust sequential adjustment. The method for processing the GPS deformation monitoring data is accurate.

The invention relates to a robust TDOA positioning method for jointly estimating the target position and the non-line-of-sight error, and eliminates the quadratic item of the non-line-of-sight error by transforming the original measurement model. According to the transformed measurement model, a robust least squares problem is constructed with the non-line-of-sight error of the target position and the reference path as the estimated parameters; then, the convex positive semi-definite programming problem is obtained by using the S lemma and positive semi-definite relaxation technique; Utilize the interior point method solving tool commonly used at last to solve the semi-positive definite programming problem, thereby obtain the estimated value of the target source position in the established coordinate system; The advantage of the present invention is to overcome the non-line-of-sight error in the existing robust method The problems caused by the excessive boundary and the application of the triangle inequality have improved the positioning accuracy of the existing methods in the non-line-of-sight environment.

The invention discloses an image denoising fusion method and system based on high and low bit data of a camera. The method comprises the steps of reading an image, and performing preprocessing on theimage; estimating and fitting the inherent noise of each column in the image through a mode of robust least square fitting according to pre-estimated per-column noise between camera sensors so as to eliminate the inherent per-column noise between the camera sensors of the camera in the image; constructing double 11-bit mapping table, accomplishing one-to-one fusion on two images according to the corresponding mapping table, wherein adjustment for the construction of the mapping tables is accomplished through different relevant parameters, and finally realizing continuous weight construction byusing a corresponding power function. According to the method, problems such as poor image synthesis effect caused by the inherent noise of the sensors can be effectively avoided, smooth fusion of high and low bit images is realized, and problems such as generation of synthesis seams are avoided.