Xiangru Li

Rejecting Mismatches by Correspondence Function

A novel method ICF (Identifying point correspondences by Correspondence Function) is proposed for rejecting mismatches from given putative point correspondences. By analyzing the connotation of homography, we introduce a novel concept of correspondence function for two images of a general 3D scene, which captures the relationships between corresponding points by mapping a point in one image to its corresponding point in another. Since the correspondence functions are unknown in real applications, we also study how to estimate them from given putative correspondences, and propose an algorithm IECF (Iteratively Estimate Correspondence Function) based on diagnostic technique and SVM. Then, the proposed ICF method is able to reject the mismatches by checking whether they are consistent with the estimated correspondence functions. Extensive experiments on real images demonstrate the excellent performance of our proposed method. In addition, the ICF is a general method for rejecting mismatches, and it is applicable to images of rigid objects or images of non-rigid objects with unknown deformation.

A note on the convergence of the mean shift

Mean shift is an effective iterative algorithm widely used in computer vision community. However, to our knowledge, its convergence, a key aspect of any iterative algorithm, has not been rigorously proved up to now. In this paper, by further imposing some commonly acceptable conditions, its convergence is proved.

SDSS/SEGUE SPECTRAL FEATURE ANALYSIS FOR STELLAR ATMOSPHERIC PARAMETER ESTIMATION

Large-scale and deep sky survey missions are rapidly collecting a large amount of stellar spectra, which necessitate the estimation of atmospheric parameters directly from spectra and makes it feasible to statistically investigate latent principles in a large dataset. We present a technique for estimating parameters

An autoencoder of stellar spectra and its application in automatically estimating atmospheric parameters

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LINEARLY SUPPORTING FEATURE EXTRACTION FOR AUTOMATED ESTIMATION OF STELLAR ATMOSPHERIC PARAMETERS

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Estimating stellar atmospheric parameters based on LASSO and support-vector regression

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Parameterizing Stellar Spectra Using Deep Neural Networks

and [Fe/H] from stellar spectra. With this technique, we first extract features from stellar spectra using the LASSO algorithm; then, the parameters are estimated from the extracted features using the SVR. On a subsample of 20~000 stellar spectra from SDSS with reference parameters provided by SDSS/SEGUE Pipeline SSPP, estimation consistency are 0.007458 dex for log

Spectral Feature Extraction for DB White Dwarfs Through Machine Learning Applied to New Discoveries in the Sdss DR12 and DR14

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A Novel Iterative SIFT Points Matching Method

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A Robust IHC Color Image Automatic Segmentation Algorithm

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