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Journal ArticleDOI

Empirical investigation of starling flocks: a benchmark study in collective animal behaviour

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TLDR
This work measured the individual three-dimensional positions in compact flocks of up to 2700 birds and investigated the main features of the flock as a whole (shape, movement, density and structure); current models and theories of collective animal behaviour can now be tested against these data.
About
This article is published in Animal Behaviour.The article was published on 2008-07-01. It has received 422 citations till now. The article focuses on the topics: Flock & Flocking (behavior).

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Citations
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Journal ArticleDOI

The Mechanics and Statistics of Active Matter

TL;DR: In this paper, a unified view of the many kinds of active matter is presented, encompassing not only living systems but inanimate analogs, including all living organisms and their motile constituents such as molecular motors.
Journal ArticleDOI

Scale-free correlations in starling flocks

TL;DR: It is suggested that flocks behave as critical systems, poised to respond maximally to environmental perturbations, through scale-free behavioral correlations, which provide each animal with an effective perception range much larger than the direct interindividual interaction range, thus enhancing global response to perturbation.
Journal ArticleDOI

Statistical mechanics for natural flocks of birds

TL;DR: It is shown how a quantitative microscopic theory for directional ordering in a flock can be derived directly from field data, and the minimally structured (maximum entropy) model is constructed consistent with experimental correlations in large flocks of starlings.
Journal ArticleDOI

Are biological systems poised at criticality

TL;DR: This work reviews the surprising successes of this “inverse” approach to statistical mechanics models of biological systems directly from real data, using examples from families of proteins, networks of neurons, and flocks of birds.
Journal ArticleDOI

Are biological systems poised at criticality

TL;DR: In the past few years, new, larger scale experiments have made it possible to construct statistical mechanics models of biological systems directly from real data as discussed by the authors, and the surprising successes of this "inverse" approach, using examples from families of proteins, networks of neurons, and flocks of birds.
References
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Book

Multiple view geometry in computer vision

TL;DR: In this article, the authors provide comprehensive background material and explain how to apply the methods and implement the algorithms directly in a unified framework, including geometric principles and how to represent objects algebraically so they can be computed and applied.
Journal ArticleDOI

Coordination of groups of mobile autonomous agents using nearest neighbor rules

TL;DR: A theoretical explanation for the observed behavior of the Vicsek model, which proves to be a graphic example of a switched linear system which is stable, but for which there does not exist a common quadratic Lyapunov function.
Proceedings ArticleDOI

Flocks, herds and schools: A distributed behavioral model

TL;DR: In this article, an approach based on simulation as an alternative to scripting the paths of each bird individually is explored, with the simulated birds being the particles and the aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock; the birds choose their own course.
Journal ArticleDOI

Novel Type of Phase Transition in a System of Self-Driven Particles

TL;DR: Numerical evidence is presented that this model results in a kinetic phase transition from no transport to finite net transport through spontaneous symmetry breaking of the rotational symmetry.
Journal ArticleDOI

A computer algorithm for reconstructing a scene from two projections

TL;DR: A simple algorithm for computing the three-dimensional structure of a scene from a correlated pair of perspective projections is described here, when the spatial relationship between the two projections is unknown.
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