Results 201 to 210 of about 39,091 (262)

A scalable benchmark to evaluate the robustness of image stitching under simulated distortions. [PDF]

Sci Rep
Liu Y, Wang Y, Wang D, Wu W, Li X, Sun W, Ren X, Song H.   +7 more
europepmc   +1 more source

An intelligent MRI data fusion framework for optimized diagnosis of spinal tumors. [PDF]

Front Med (Lausanne)
Shi Z, Jiang J, Li M, Zhao X.
europepmc   +1 more source

Clindamycin susceptibility and virulence characterization of <i>Listeria monocytogenes</i> strains isolated from meat and meat-processing environments. [PDF]

Front Microbiol
Pérez-Baltar A, Hernández-Gelabert B, López P, Pérez-Boto D, Arqués JL, Martínez-Suárez JV, Montiel R.   +6 more
europepmc   +1 more source

Cross-Detector Visual Localization with Coplanarity Constraints for Indoor Environments. [PDF]

Sensors (Basel)
Matez-Bandera JL, Jaenal A, Gomez C, Hernandez AC, Monroy J, Araújo J, Gonzalez-Jimenez J.   +6 more
europepmc   +1 more source

Population structure and domestication history of the Javan banteng.

Curr Biol
Wang X, Aninta SG, Garcia-Erill G, Li Z, Khan A, Liu X, Bertola LD, Dharmayanthi AB, Yulianto, Yonathan, Rossi C, Cauble-Sims R, Rosen BD, Hagen DE, Heaton MP, Smith TPL, Lenstra JA, Martins NFG, Sinding MS, Agil M, Purwantara B, Hvilsom C, Semiadi G, Heller R.   +23 more
europepmc   +1 more source

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Comparison of SIFT, Bi-SIFT, and Tri-SIFT and their frequency spectrum analysis

Machine Vision and Applications, 2017
This paper aims to explore frequency behavior of isotropic (regular SIFT) and anisotropic (Bi-SIFT and Tri-SIFT) versions of the scale-space keypoint detection algorithm SIFT. We introduced a new smoothing function Trilateral filter that can be used in formation of a scale-space as an alternative to the Gaussian scale-space.
Kazim Sekeroglu, Ömer M. Soysal
exaly   +2 more sources

On SIFTs and their scales

2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012
Scale invariant feature detectors often find stable scales in only a few image pixels. Consequently, methods for feature matching typically choose one of two extreme options: matching a sparse set of scale invariant features, or dense matching using arbitrary scales.
Tal Hassner, Viki Mayzels, Lihi Zelnik-Manor   +2 more
openaire   +1 more source

Sifted Disks

Computer Graphics Forum, 2013
AbstractWe introduce the Sifted Disk technique for locally resampling a point cloud in order to reduce the number of points. Two neighboring points are removed and we attempt to find a single random point that is sufficient to replace them both. The resampling respects the original sizing function; In that sense it is not a coarsening.
Mohamed S. Ebeida, Ahmed H. Mahmoud, Muhammad A. Awad, Mohammed A. Mohammed, Scott A. Mitchell, Alexander Rand, John D. Owens   +6 more
openaire   +1 more source