10,604 research outputs found
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
Inference of hidden structures in complex physical systems by multi-scale clustering
We survey the application of a relatively new branch of statistical
physics--"community detection"-- to data mining. In particular, we focus on the
diagnosis of materials and automated image segmentation. Community detection
describes the quest of partitioning a complex system involving many elements
into optimally decoupled subsets or communities of such elements. We review a
multiresolution variant which is used to ascertain structures at different
spatial and temporal scales. Significant patterns are obtained by examining the
correlations between different independent solvers. Similar to other
combinatorial optimization problems in the NP complexity class, community
detection exhibits several phases. Typically, illuminating orders are revealed
by choosing parameters that lead to extremal information theory correlations.Comment: 25 pages, 16 Figures; a review of earlier work
A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects
Recently, Minimum Cost Multicut Formulations have been proposed and proven to
be successful in both motion trajectory segmentation and multi-target tracking
scenarios. Both tasks benefit from decomposing a graphical model into an
optimal number of connected components based on attractive and repulsive
pairwise terms. The two tasks are formulated on different levels of granularity
and, accordingly, leverage mostly local information for motion segmentation and
mostly high-level information for multi-target tracking. In this paper we argue
that point trajectories and their local relationships can contribute to the
high-level task of multi-target tracking and also argue that high-level cues
from object detection and tracking are helpful to solve motion segmentation. We
propose a joint graphical model for point trajectories and object detections
whose Multicuts are solutions to motion segmentation {\it and} multi-target
tracking problems at once. Results on the FBMS59 motion segmentation benchmark
as well as on pedestrian tracking sequences from the 2D MOT 2015 benchmark
demonstrate the promise of this joint approach
A video object generation tool allowing friendly user interaction
In this paper we describe an interactive video object segmentation tool developed in the framework of the ACTS-AC098 MOMUSYS project. The Video Object Generator with User Environment (VOGUE) combines three different sets of automatic and semi-automatic-tool (spatial segmentation, object tracking and temporal segmentation) with general purpose tools for user interaction. The result is an integrated environment allowing the user-assisted segmentation of any sort of video sequences in a friendly and efficient manner.Peer ReviewedPostprint (published version
A Triclustering Approach for Time Evolving Graphs
This paper introduces a novel technique to track structures in time evolving
graphs. The method is based on a parameter free approach for three-dimensional
co-clustering of the source vertices, the target vertices and the time. All
these features are simultaneously segmented in order to build time segments and
clusters of vertices whose edge distributions are similar and evolve in the
same way over the time segments. The main novelty of this approach lies in that
the time segments are directly inferred from the evolution of the edge
distribution between the vertices, thus not requiring the user to make an a
priori discretization. Experiments conducted on a synthetic dataset illustrate
the good behaviour of the technique, and a study of a real-life dataset shows
the potential of the proposed approach for exploratory data analysis
Multi-Modal Mean-Fields via Cardinality-Based Clamping
Mean Field inference is central to statistical physics. It has attracted much
interest in the Computer Vision community to efficiently solve problems
expressible in terms of large Conditional Random Fields. However, since it
models the posterior probability distribution as a product of marginal
probabilities, it may fail to properly account for important dependencies
between variables. We therefore replace the fully factorized distribution of
Mean Field by a weighted mixture of such distributions, that similarly
minimizes the KL-Divergence to the true posterior. By introducing two new
ideas, namely, conditioning on groups of variables instead of single ones and
using a parameter of the conditional random field potentials, that we identify
to the temperature in the sense of statistical physics to select such groups,
we can perform this minimization efficiently. Our extension of the clamping
method proposed in previous works allows us to both produce a more descriptive
approximation of the true posterior and, inspired by the diverse MAP paradigms,
fit a mixture of Mean Field approximations. We demonstrate that this positively
impacts real-world algorithms that initially relied on mean fields.Comment: Submitted for review to CVPR 201
Image segmentation evaluation using an integrated framework
In this paper we present a general framework we have developed for running and evaluating automatic image and video segmentation algorithms. This framework was designed to allow effortless integration of existing and forthcoming image segmentation algorithms, and allows researchers to focus more on the development and evaluation of segmentation methods, relying on the framework for encoding/decoding and visualization. We then utilize this framework to automatically evaluate four distinct segmentation algorithms, and present and discuss the results and statistical findings of the experiment
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