that isophote lines arriving at the regions ’ boundaries are completed inside. In contrast with previous approaches, the technique here introduced does not require the user to specify where the novel information comes from. This is automatically done (and in a fast way), thereby allowing to simultaneously fill-in

An algorithm for the simultaneous filling-in of texture and structure in regions of missing image information is presented in this paper. The basic idea is to first decompose the image into the sum of two functions with different basic characteristics, and then reconstruct each one

An algorithm for the simultaneous filling-in of texture and structure in regions of missing image information is presented in this paper. The basic idea is to first decompose the image into the sum of two functions with different basic characteristics, and then reconstruct each one

A variational approach for filling-in regions of missing data in digital images is introduced in this paper. The approach is based on joint interpolation of the image gray-levels and gradient/isophotes directions, smoothly extending in an automatic fashion the isophote lines into the holes

A variational approach for filling-in regions of missing data in graylevel and color images is introduced in this paper. The approach is based on joint interpolation of the image gray-levels and gradient/isophotes directions, smoothly extending in an automatic fashion the isophote lines

. The presence of the He i D 3 in emission in coincidence with the enhancement of the Hff emission confirms the detection of a flare. The application of the spectral subtraction technique reveals that the core of the Na i D 1 and D 2 lines are also filled-in by chromospheric emission.

information matrix, therefore recalculating only the matrix entries that actually change. iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fill-in in the factor matrix by periodic variable reordering. Also, to enable data association in real-time, we provide efficient

We present a method for space-time completion of large space-time “holes ” in video sequences of complex dy-namic scenes. The missing portions are filled-in by sam-pling spatio-temporal patches from the available parts of the video, while enforcing global spatio-temporal consistency between all

shape and motion for each instant, learns the PPCA model parameters, and robustly fills-in missing data points. We then extend the model to model temporal dynamics in object shape, allowing the algorithm to robustly handle severe cases of missing data.

the fill-in of the overweight sub-domains and achieve a considerably better balance.