000195668 001__ 195668
000195668 005__ 20190316235824.0
000195668 0247_ $$2doi$$a10.1137/140974559
000195668 02470 $$2ISI$$a000346854900033
000195668 037__ $$aARTICLE
000195668 245__ $$aAnalysis of Image Registration with Tangent Distance
000195668 269__ $$a2014
000195668 260__ $$c2014
000195668 336__ $$aJournal Articles
000195668 520__ $$aThe computation of the geometric transformation between a reference and a target image, known as image registration or alignment, corresponds to the projection of the target image onto the transformation manifold of the reference image (the set of images generated by its geometric transformations). It often takes a nontrivial form such that exact computation of projections on the manifold is difficult. The tangent distance method is an effective alignment algorithm that exploits a linear approximation of the transformation manifold of the reference image. As theoretical studies about the tangent distance algorithm have been largely overlooked, we present in this work a detailed performance analysis of this useful algorithm, which can eventually help the selection of algorithm parameters. We consider a popular image registration setting using a multiscale pyramid of lowpass filtered versions of the (possibly noisy) reference and target images, which is particularly useful for recovering large transformations. We first show that the alignment error has a nonmonotonic variation with the filter size, due to the opposing effects of filtering on manifold nonlinearity and image noise. We then study the convergence of the multiscale tangent distance method to the optimal solution. We finally examine the performance of the tangent distance method in image classification applications. Our theoretical findings are confirmed by experiments on image transformation models involving translations, rotations and scalings. Our study is the first detailed study of the tangent distance algorithm that leads to a better understanding of its efficacy and to the proper selection of design parameters.
000195668 6531_ $$aImage registration
000195668 6531_ $$atangent distance
000195668 6531_ $$aimage analysis
000195668 6531_ $$ahierarchical registration methods
000195668 6531_ $$aperformance analysis
000195668 700__ $$0242951$$aVural, Elif$$g185439
000195668 700__ $$0241061$$aFrossard, Pascal$$g101475
000195668 773__ $$j7$$k4$$q2860-2915$$tSIAM Journal on Imaging Sciences
000195668 8564_ $$s878391$$uhttps://infoscience.epfl.ch/record/195668/files/TD_analysis.pdf$$yn/a$$zn/a
000195668 909C0 $$0252393$$pLTS4$$xU10851
000195668 909CO $$ooai:infoscience.tind.io:195668$$pSTI$$particle$$qGLOBAL_SET
000195668 917Z8 $$x185439
000195668 917Z8 $$x101475
000195668 917Z8 $$x101475
000195668 917Z8 $$x253578
000195668 937__ $$aEPFL-ARTICLE-195668
000195668 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000195668 980__ $$aARTICLE