Improving the matching precision of SIFT

International audienceMatching precision of scale-invariant feature transform (SIFT) is evaluated and improved in this paper. The aim of the paper is not to invent a new feature detector more invariant than the others. Instead, we focus on SIFT method and evaluate and improve the matching precision, defined as the root mean square error (RMSE) under ground truth geometric trans-form. Matching precision reflects to some extent the average relative localization precision between two images. For scale invariant feature detectors like SIFT, the matching precision decreases with the scale of features due to the sub-sampling in the scale space. We propose to cancel the sub-sampling to improve the matching precision. But in case of scale change, the improvement is marginal due to the coarse scale quanti-zation in the scale space. One more sophisticated method is also proposed to improve the matching precision in case of scale change. These modifications can be easily extended to other scale invariant feature detectors

Towards retrieving force feedback in robotic-assisted surgery: a supervised neuro-recurrent-vision approach

Robotic-assisted minimally invasive surgeries have gained a lot of popularity over conventional procedures as they offer many benefits to both surgeons and patients. Nonetheless, they still suffer from some limitations that affect their outcome. One of them is the lack of force feedback which restricts the surgeon's sense of touch and might reduce precision during a procedure. To overcome this limitation, we propose a novel force estimation approach that combines a vision based solution with supervised learning to estimate the applied force and provide the surgeon with a suitable representation of it. The proposed solution starts with extracting the geometry of motion of the heart's surface by minimizing an energy functional to recover its 3D deformable structure. A deep network, based on a LSTM-RNN architecture, is then used to learn the relationship between the extracted visual-geometric information and the applied force, and to find accurate mapping between the two. Our proposed force estimation solution avoids the drawbacks usually associated with force sensing devices, such as biocompatibility and integration issues. We evaluate our approach on phantom and realistic tissues in which we report an average root-mean square error of 0.02 N.Peer ReviewedPostprint (author's final draft

High-precision astrometry with VVV. I. An independent reduction pipeline for VIRCAM@VISTA

We present a new reduction pipeline for the VIRCAM@VISTA detector and describe the method developed to obtain high-precision astrometry with the VISTA Variables in the V\'ia L\'actea (VVV) data set. We derive an accurate geometric-distortion correction using as calibration field the globular cluster NGC 5139, and showed that we are able to reach a relative astrometric precision of about 8 mas per coordinate per exposure for well-measured stars over a field of view of more than 1 square degree. This geometric-distortion correction is made available to the community. As a test bed, we chose a field centered around the globular cluster NGC 6656 from the VVV archive and computed proper motions for the stars within. With 45 epochs spread over four years, we show that we are able to achieve a precision of 1.4 mas/yr and to isolate each population observed in the field (cluster, Bulge and Disk) using proper motions. We used proper-motion-selected field stars to measure the motion difference between Galactic disk and bulge stars. Our proper-motion measurements are consistent with UCAC4 and PPMXL, though our errors are much smaller. Models have still difficulties in reproducing the observations in this highly-reddened Galactic regions.Comment: 11 pages, 10 figures (some in low res), 1 table. Accepted for publication in MNRAS on March 25, 2015. The FORTRAN routine will be soon made available at http://groups.dfa.unipd.it/ESPG/ , and via email request to the first autho

Including parameter dependence in the data and covariance for cosmological inference

The final step of most large-scale structure analyses involves the comparison of power spectra or correlation functions to theoretical models. It is clear that the theoretical models have parameter dependence, but frequently the measurements and the covariance matrix depend upon some of the parameters as well. We show that a very simple interpolation scheme from an unstructured mesh allows for an efficient way to include this parameter dependence self-consistently in the analysis at modest computational expense. We describe two schemes for covariance matrices. The scheme which uses the geometric structure of such matrices performs roughly twice as well as the simplest scheme, though both perform very well.Comment: 17 pages, 4 figures, matches version published in JCA