Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Veksler, Olga"'
Autor:
Gorelick, Lena, Veksler, Olga
Combining CNN with CRF for modeling dependencies between pixel labels is a popular research direction. This task is far from trivial, especially if end-to-end training is desired. In this paper, we propose a novel simple approach to CNN+CRF combinati
Externí odkaz:
http://arxiv.org/abs/1905.02163
Autor:
Veksler, Olga
Fully connected pairwise Conditional Random Fields (Full-CRF) with Gaussian edge weights can achieve superior results compared to sparsely connected CRFs. However, traditional methods for Full-CRFs are too expensive. Previous work develops efficient
Externí odkaz:
http://arxiv.org/abs/1809.04995
Autor:
Wang, Zhenyi, Veksler, Olga
CNNs have made a tremendous impact on the field of computer vision in the last several years. The main component of any CNN architecture is the convolution operation, which is translation invariant by design. However, location in itself can be an imp
Externí odkaz:
http://arxiv.org/abs/1807.07044
We propose an effective optimization algorithm for a general hierarchical segmentation model with geometric interactions between segments. Any given tree can specify a partial order over object labels defining a hierarchy. It is well-established that
Externí odkaz:
http://arxiv.org/abs/1703.10530
Akademický článek
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Overlapping colors and cluttered or weak edges are common segmentation problems requiring additional regularization. For example, star-convexity is popular for interactive single object segmentation due to simplicity and amenability to exact graph cu
Externí odkaz:
http://arxiv.org/abs/1602.01006
Curvature has received increased attention as an important alternative to length based regularization in computer vision. In contrast to length, it preserves elongated structures and fine details. Existing approaches are either inefficient, or have l
Externí odkaz:
http://arxiv.org/abs/1311.1838
Many computer vision problems require optimization of binary non-submodular energies. We propose a general optimization framework based on local submodular approximations (LSA). Unlike standard LP relaxation methods that linearize the whole energy gl
Externí odkaz:
http://arxiv.org/abs/1311.1856
Akademický článek
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Autor:
Veksler, Olga1 olga@csd.uwo.ca
Publikováno v:
International Journal of Computer Vision. Jun2012, Vol. 98 Issue 1, p1-14. 14p. 3 Black and White Photographs, 5 Diagrams, 4 Charts.
