@article{3331,
abstract = {Computing the topology of an algebraic plane curve C means computing a combinatorial graph that is isotopic to C and thus represents its topology in R2. We prove that, for a polynomial of degree n with integer coefficients bounded by 2ρ, the topology of the induced curve can be computed with bit operations ( indicates that we omit logarithmic factors). Our analysis improves the previous best known complexity bounds by a factor of n2. The improvement is based on new techniques to compute and refine isolating intervals for the real roots of polynomials, and on the consequent amortized analysis of the critical fibers of the algebraic curve.},
author = {Kerber, Michael and Sagraloff, Michael},
journal = { Journal of Symbolic Computation},
number = {3},
pages = {239 -- 258},
publisher = {Elsevier},
title = {{A worst case bound for topology computation of algebraic curves}},
doi = {10.1016/j.jsc.2011.11.001},
volume = {47},
year = {2012},
}
@article{3965,
abstract = {The elevation function on a smoothly embedded 2-manifold in R-3 reflects the multiscale topography of cavities and protrusions as local maxima. The function has been useful in identifying coarse docking configurations for protein pairs. Transporting the concept from the smooth to the piecewise linear category, this paper describes an algorithm for finding all local maxima. While its worst-case running time is the same as of the algorithm used in prior work, its performance in practice is orders of magnitudes superior. We cast light on this improvement by relating the running time to the total absolute Gaussian curvature of the 2-manifold.},
author = {Wang, Bei and Edelsbrunner, Herbert and Morozov, Dmitriy},
journal = {Journal of Experimental Algorithmics},
number = {2.2},
pages = {1 -- 13},
publisher = {ACM},
title = {{Computing elevation maxima by searching the Gauss sphere}},
doi = {10.1145/1963190.1970375},
volume = {16},
year = {2011},
}
@inproceedings{3266,
abstract = {We present a joint image segmentation and labeling model (JSL) which, given a bag of figure-ground segment hypotheses extracted at multiple image locations and scales, constructs a joint probability distribution over both the compatible image interpretations (tilings or image segmentations) composed from those segments, and over their labeling into categories. The process of drawing samples from the joint distribution can be interpreted as first sampling tilings, modeled as maximal cliques, from a graph connecting spatially non-overlapping segments in the bag [1], followed by sampling labels for those segments, conditioned on the choice of a particular tiling. We learn the segmentation and labeling parameters jointly, based on Maximum Likelihood with a novel Incremental Saddle Point estimation procedure. The partition function over tilings and labelings is increasingly more accurately approximated by including incorrect configurations that a not-yet-competent model rates probable during learning. We show that the proposed methodologymatches the current state of the art in the Stanford dataset [2], as well as in VOC2010, where 41.7% accuracy on the test set is achieved.},
author = {Ion, Adrian and Carreira, Joao and Sminchisescu, Cristian},
booktitle = {NIPS Proceedings},
location = {Granada, Spain},
pages = {1827 -- 1835},
publisher = {Neural Information Processing Systems Foundation},
title = {{Probabilistic joint image segmentation and labeling}},
volume = {24},
year = {2011},
}
@article{3267,
abstract = {We address the problem of localizing homology classes, namely, finding the cycle representing a given class with the most concise geometric measure. We study the problem with different measures: volume, diameter and radius. For volume, that is, the 1-norm of a cycle, two main results are presented. First, we prove that the problem is NP-hard to approximate within any constant factor. Second, we prove that for homology of dimension two or higher, the problem is NP-hard to approximate even when the Betti number is O(1). The latter result leads to the inapproximability of the problem of computing the nonbounding cycle with the smallest volume and computing cycles representing a homology basis with the minimal total volume. As for the other two measures defined by pairwise geodesic distance, diameter and radius, we show that the localization problem is NP-hard for diameter but is polynomial for radius. Our work is restricted to homology over the ℤ2 field.},
author = {Chen, Chao and Freedman, Daniel},
journal = {Discrete & Computational Geometry},
number = {3},
pages = {425 -- 448},
publisher = {Springer},
title = {{Hardness results for homology localization}},
doi = {10.1007/s00454-010-9322-8},
volume = {45},
year = {2011},
}
@article{3269,
abstract = {The unintentional scattering of light between neighboring surfaces in complex projection environments increases the brightness and decreases the contrast, disrupting the appearance of the desired imagery. To achieve satisfactory projection results, the inverse problem of global illumination must be solved to cancel this secondary scattering. In this paper, we propose a global illumination cancellation method that minimizes the perceptual difference between the desired imagery and the actual total illumination in the resulting physical environment. Using Gauss-Newton and active set methods, we design a fast solver for the bound constrained nonlinear least squares problem raised by the perceptual error metrics. Our solver is further accelerated with a CUDA implementation and multi-resolution method to achieve 1–2 fps for problems with approximately 3000 variables. We demonstrate the global illumination cancellation algorithm with our multi-projector system. Results show that our method preserves the color fidelity of the desired imagery significantly better than previous methods.},
author = {Sheng, Yu and Cutler, Barbara and Chen, Chao and Nasman, Joshua},
journal = {Computer Graphics Forum},
number = {4},
pages = {1261 -- 1268},
publisher = {Wiley-Blackwell},
title = {{Perceptual global illumination cancellation in complex projection environments}},
doi = {10.1111/j.1467-8659.2011.01985.x},
volume = {30},
year = {2011},
}