97,863 research outputs found
Local Variation as a Statistical Hypothesis Test
The goal of image oversegmentation is to divide an image into several pieces,
each of which should ideally be part of an object. One of the simplest and yet
most effective oversegmentation algorithms is known as local variation (LV)
(Felzenszwalb and Huttenlocher 2004). In this work, we study this algorithm and
show that algorithms similar to LV can be devised by applying different
statistical models and decisions, thus providing further theoretical
justification and a well-founded explanation for the unexpected high
performance of the LV approach. Some of these algorithms are based on
statistics of natural images and on a hypothesis testing decision; we denote
these algorithms probabilistic local variation (pLV). The best pLV algorithm,
which relies on censored estimation, presents state-of-the-art results while
keeping the same computational complexity of the LV algorithm
Schwarz Iterative Methods: Infinite Space Splittings
We prove the convergence of greedy and randomized versions of Schwarz
iterative methods for solving linear elliptic variational problems based on
infinite space splittings of a Hilbert space. For the greedy case, we show a
squared error decay rate of for elements of an approximation
space related to the underlying splitting. For the randomized
case, we show an expected squared error decay rate of on a
class depending on the
probability distribution.Comment: Revised version, accepted in Constr. Appro
What Catholic educators can learn from the radical Christianity and critical pedagogy of Don Lorenzo Milani
This paper exposes some of the ideas expressed or associated with the work of don Lorenzo Milani and the School of Barbiana and discusses them in the light of the teachings of the gospels. It draws out the ramifications of these ideas for a critical education in the Christian spirit. The focus throughout is on education for social justice.peer-reviewe
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