33,399 research outputs found
Optimizing feature extraction in image analysis using experimented designs, a case study evaluating texture algorithms for describing appearance retention in carpets
When performing image analysis, one of the most critical steps is the selection of appropriate techniques. A huge amount of features can be extracted from several techniques and the selection is commonly performed based on expert knowledge. In this paper we present the theory of experimental designs as a tool for an objective selection of techniques in image analysis domain. We present a study case for evaluating appearance retention in textile floor coverings using texture features. The use of experimental design theory permitted to select an optimal set of techniques for describing the texture changes due to degradation
Robust Region-of-Attraction Estimation
We propose a method to compute invariant subsets of the region-of-attraction for asymptotically stable equilibrium points of polynomial dynamical systems with bounded parametric uncertainty. Parameter-independent Lyapunov functions are used to characterize invariant subsets of the robust region-of-attraction. A branch-and-bound type refinement procedure reduces the conservatism. We demonstrate the method on an example from the literature and uncertain controlled short-period aircraft dynamics
Covering point sets with two disjoint disks or squares
Open archive-ElsevierWe study the following problem: Given a set of red points and a set of blue points on the plane, find two unit disks CR and CB
with disjoint interiors such that the number of red points covered by CR plus the number of blue points covered by CB is maximized.
We give an algorithm to solve this problem in O(n8/3 log2 n) time, where n denotes the total number of points. We also show that
the analogous problem of finding two axis-aligned unit squares SR and SB instead of unit disks can be solved in O(nlog n) time,
which is optimal. If we do not restrict ourselves to axis-aligned squares, but require that both squares have a common orientation,
we give a solution using O(n3 log n) time
On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems
The use of orthogonal projections on high-dimensional input and target data
in learning frameworks is studied. First, we investigate the relations between
two standard objectives in dimension reduction, preservation of variance and of
pairwise relative distances. Investigations of their asymptotic correlation as
well as numerical experiments show that a projection does usually not satisfy
both objectives at once. In a standard classification problem we determine
projections on the input data that balance the objectives and compare
subsequent results. Next, we extend our application of orthogonal projections
to deep learning tasks and introduce a general framework of augmented target
loss functions. These loss functions integrate additional information via
transformations and projections of the target data. In two supervised learning
problems, clinical image segmentation and music information classification, the
application of our proposed augmented target loss functions increase the
accuracy
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