6,235 research outputs found
Recognising Multidimensional Euclidean Preferences
Euclidean preferences are a widely studied preference model, in which
decision makers and alternatives are embedded in d-dimensional Euclidean space.
Decision makers prefer those alternatives closer to them. This model, also
known as multidimensional unfolding, has applications in economics,
psychometrics, marketing, and many other fields. We study the problem of
deciding whether a given preference profile is d-Euclidean. For the
one-dimensional case, polynomial-time algorithms are known. We show that, in
contrast, for every other fixed dimension d > 1, the recognition problem is
equivalent to the existential theory of the reals (ETR), and so in particular
NP-hard. We further show that some Euclidean preference profiles require
exponentially many bits in order to specify any Euclidean embedding, and prove
that the domain of d-Euclidean preferences does not admit a finite forbidden
minor characterisation for any d > 1. We also study dichotomous preferencesand
the behaviour of other metrics, and survey a variety of related work.Comment: 17 page
Contour Detection from Deep Patch-level Boundary Prediction
In this paper, we present a novel approach for contour detection with
Convolutional Neural Networks. A multi-scale CNN learning framework is designed
to automatically learn the most relevant features for contour patch detection.
Our method uses patch-level measurements to create contour maps with
overlapping patches. We show the proposed CNN is able to to detect large-scale
contours in an image efficienly. We further propose a guided filtering method
to refine the contour maps produced from large-scale contours. Experimental
results on the major contour benchmark databases demonstrate the effectiveness
of the proposed technique. We show our method can achieve good detection of
both fine-scale and large-scale contours.Comment: IEEE International Conference on Signal and Image Processing 201
Interactive interpretation of structured documents: Application to the recognition of handwritten architectural plans
International audienceThis paper addresses a whole architecture, including the IMISketch method. IMISketch method incorporates two aspects: document analysis and interactivity. This paper describes a global vision of all the parts of the project. IMISketch is a generic method for an interactive interpretation of handwritten sketches. The analysis of complex documents requires the management of uncertainty. While, in practice the similar methods often induce a large combinatorics, IMISketch method presents several optimization strategies to reduce the combinatorics. The goal of these optimizations is to have a time analysis compatible with user expectations. The decision process is able to solicit the user in the case of strong ambiguity: when it is not sure to make the right decision, the user explicitly validates the right decision to avoid a fastidious a posteriori verification phase due to propagation of errors.This interaction requires solving two major problems: how interpretation results will be presented to the user, and how the user will interact with analysis process. We propose to study the effects of those two aspects. The experiments demonstrate that (i) a progressive presentation of the analysis results, (ii) user interventions during it and (iii) the user solicitation by the analysis process are an efficient strategy for the recognition of complex off-line documents.To validate this interactive analysis method, several experiments are reported on off-line handwritten 2D architectural floor plans
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