56,433 research outputs found
Probabilistic Search for Object Segmentation and Recognition
The problem of searching for a model-based scene interpretation is analyzed within a probabilistic framework. Object models are formulated as generative models for range data of the scene. A new statistical criterion, the truncated object probability, is introduced to infer an optimal sequence of object hypotheses to be evaluated for their match to the data. The truncated probability is partly determined by prior knowledge of the objects and partly learned from data. Some experiments on sequence quality and object segmentation and recognition from stereo data are presented. The article recovers classic concepts from object recognition (grouping, geometric hashing, alignment) from the probabilistic perspective and adds insight into the optimal ordering of object hypotheses for evaluation. Moreover, it introduces point-relation densities, a key component of the truncated probability, as statistical models of local surface shape
Knowledge-based systems and geological survey
This personal and pragmatic review of the philosophy underpinning methods of geological surveying suggests that important influences of information technology have yet to make their impact. Early approaches took existing systems as metaphors, retaining the separation of maps, map explanations and information archives, organised around map sheets of fixed boundaries, scale and content. But system design should look ahead: a computer-based knowledge system for the same purpose can be built around hierarchies of spatial objects and their relationships, with maps as one means of visualisation, and information types linked as hypermedia and integrated in mark-up languages. The system framework and ontology, derived from the general geoscience model, could support consistent representation of the underlying concepts and maintain reference information on object classes and their behaviour. Models of processes and historical configurations could clarify the reasoning at any level of object detail and introduce new concepts such as complex systems. The up-to-date interpretation might centre on spatial models, constructed with explicit geological reasoning and evaluation of uncertainties. Assuming (at a future time) full computer support, the field survey results could be collected in real time as a multimedia stream, hyperlinked to and interacting with the other parts of the system as appropriate. Throughout, the knowledge is seen as human knowledge, with interactive computer support for recording and storing the information and processing it by such means as interpolating, correlating, browsing, selecting, retrieving, manipulating, calculating, analysing, generalising, filtering, visualising and delivering the results. Responsibilities may have to be reconsidered for various aspects of the system, such as: field surveying; spatial models and interpretation; geological processes, past configurations and reasoning; standard setting, system framework and ontology maintenance; training; storage, preservation, and dissemination of digital records
Probabilistic Search for Object Segmentation and Recognition
The problem of searching for a model-based scene interpretation is analyzed
within a probabilistic framework. Object models are formulated as generative
models for range data of the scene. A new statistical criterion, the truncated
object probability, is introduced to infer an optimal sequence of object
hypotheses to be evaluated for their match to the data. The truncated
probability is partly determined by prior knowledge of the objects and partly
learned from data. Some experiments on sequence quality and object segmentation
and recognition from stereo data are presented. The article recovers classic
concepts from object recognition (grouping, geometric hashing, alignment) from
the probabilistic perspective and adds insight into the optimal ordering of
object hypotheses for evaluation. Moreover, it introduces point-relation
densities, a key component of the truncated probability, as statistical models
of local surface shape.Comment: 18 pages, 5 figure
Twenty-five years of two-dimensional rational conformal field theory
In this article we try to give a condensed panoramic view of the development
of two-dimensional rational conformal field theory in the last twenty-five
years.Comment: A review for the 50th anniversary of the Journal of Mathematical
Physics. Some references added, typos correcte
Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies
In motion analysis and understanding it is important to be able to fit a
suitable model or structure to the temporal series of observed data, in order
to describe motion patterns in a compact way, and to discriminate between them.
In an unsupervised context, i.e., no prior model of the moving object(s) is
available, such a structure has to be learned from the data in a bottom-up
fashion. In recent times, volumetric approaches in which the motion is captured
from a number of cameras and a voxel-set representation of the body is built
from the camera views, have gained ground due to attractive features such as
inherent view-invariance and robustness to occlusions. Automatic, unsupervised
segmentation of moving bodies along entire sequences, in a temporally-coherent
and robust way, has the potential to provide a means of constructing a
bottom-up model of the moving body, and track motion cues that may be later
exploited for motion classification. Spectral methods such as locally linear
embedding (LLE) can be useful in this context, as they preserve "protrusions",
i.e., high-curvature regions of the 3D volume, of articulated shapes, while
improving their separation in a lower dimensional space, making them in this
way easier to cluster. In this paper we therefore propose a spectral approach
to unsupervised and temporally-coherent body-protrusion segmentation along time
sequences. Volumetric shapes are clustered in an embedding space, clusters are
propagated in time to ensure coherence, and merged or split to accommodate
changes in the body's topology. Experiments on both synthetic and real
sequences of dense voxel-set data are shown. This supports the ability of the
proposed method to cluster body-parts consistently over time in a totally
unsupervised fashion, its robustness to sampling density and shape quality, and
its potential for bottom-up model constructionComment: 31 pages, 26 figure
Coupling different levels of resolution in molecular simulations
Simulation schemes that allow to change molecular representation in a
subvolume of the simulation box while preserving the equilibrium with the
surrounding introduce conceptual problems of thermodynamic consistency. In this
work we present a general scheme based on thermodynamic arguments which ensures
thermodynamic equilibrium among the molecules of different representation. The
robustness of the algorithm is tested for two examples, namely an adaptive
resolution simulation, atomistic/coarse-grained, for a liquid of tetrahedral
molecules and an adaptive resolution simulation of a binary mixture of
tetrahedral molecules and spherical solutes
Coarse-Graining Auto-Encoders for Molecular Dynamics
Molecular dynamics simulations provide theoretical insight into the
microscopic behavior of materials in condensed phase and, as a predictive tool,
enable computational design of new compounds. However, because of the large
temporal and spatial scales involved in thermodynamic and kinetic phenomena in
materials, atomistic simulations are often computationally unfeasible.
Coarse-graining methods allow simulating larger systems, by reducing the
dimensionality of the simulation, and propagating longer timesteps, by
averaging out fast motions. Coarse-graining involves two coupled learning
problems; defining the mapping from an all-atom to a reduced representation,
and the parametrization of a Hamiltonian over coarse-grained coordinates.
Multiple statistical mechanics approaches have addressed the latter, but the
former is generally a hand-tuned process based on chemical intuition. Here we
present Autograin, an optimization framework based on auto-encoders to learn
both tasks simultaneously. Autograin is trained to learn the optimal mapping
between all-atom and reduced representation, using the reconstruction loss to
facilitate the learning of coarse-grained variables. In addition, a
force-matching method is applied to variationally determine the coarse-grained
potential energy function. This procedure is tested on a number of model
systems including single-molecule and bulk-phase periodic simulations.Comment: 8 pages, 6 figure
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