3,457 research outputs found
Disconnected Skeleton: Shape at its Absolute Scale
We present a new skeletal representation along with a matching framework to
address the deformable shape recognition problem. The disconnectedness arises
as a result of excessive regularization that we use to describe a shape at an
attainably coarse scale. Our motivation is to rely on the stable properties of
the shape instead of inaccurately measured secondary details. The new
representation does not suffer from the common instability problems of
traditional connected skeletons, and the matching process gives quite
successful results on a diverse database of 2D shapes. An important difference
of our approach from the conventional use of the skeleton is that we replace
the local coordinate frame with a global Euclidean frame supported by
additional mechanisms to handle articulations and local boundary deformations.
As a result, we can produce descriptions that are sensitive to any combination
of changes in scale, position, orientation and articulation, as well as
invariant ones.Comment: The work excluding {\S}V and {\S}VI has first appeared in 2005 ICCV:
Aslan, C., Tari, S.: An Axis-Based Representation for Recognition. In
ICCV(2005) 1339- 1346.; Aslan, C., : Disconnected Skeletons for Shape
Recognition. Masters thesis, Department of Computer Engineering, Middle East
Technical University, May 200
Extracting curve-skeletons from digital shapes using occluding contours
Curve-skeletons are compact and semantically relevant shape descriptors, able to summarize both topology and pose of a wide range of digital objects. Most of the state-of-the-art algorithms for their computation rely on the type of geometric primitives used and sampling frequency. In this paper we introduce a formally sound and intuitive definition of curve-skeleton, then we propose a novel method for skeleton extraction that rely on the visual appearance of the shapes. To achieve this result we inspect the properties of occluding contours, showing how information about the symmetry axes of a 3D shape can be inferred by a small set of its planar projections. The proposed method is fast, insensitive to noise, capable of working with different shape representations, resolution insensitive and easy to implement
Active skeleton for bacteria modeling
The investigation of spatio-temporal dynamics of bacterial cells and their
molecular components requires automated image analysis tools to track cell
shape properties and molecular component locations inside the cells. In the
study of bacteria aging, the molecular components of interest are protein
aggregates accumulated near bacteria boundaries. This particular location makes
very ambiguous the correspondence between aggregates and cells, since computing
accurately bacteria boundaries in phase-contrast time-lapse imaging is a
challenging task. This paper proposes an active skeleton formulation for
bacteria modeling which provides several advantages: an easy computation of
shape properties (perimeter, length, thickness, orientation), an improved
boundary accuracy in noisy images, and a natural bacteria-centered coordinate
system that permits the intrinsic location of molecular components inside the
cell. Starting from an initial skeleton estimate, the medial axis of the
bacterium is obtained by minimizing an energy function which incorporates
bacteria shape constraints. Experimental results on biological images and
comparative evaluation of the performances validate the proposed approach for
modeling cigar-shaped bacteria like Escherichia coli. The Image-J plugin of the
proposed method can be found online at http://fluobactracker.inrialpes.fr.Comment: Published in Computer Methods in Biomechanics and Biomedical
Engineering: Imaging and Visualizationto appear i
A model-based approach to recovering the structure of a plant from images
We present a method for recovering the structure of a plant directly from a
small set of widely-spaced images. Structure recovery is more complex than
shape estimation, but the resulting structure estimate is more closely related
to phenotype than is a 3D geometric model. The method we propose is applicable
to a wide variety of plants, but is demonstrated on wheat. Wheat is made up of
thin elements with few identifiable features, making it difficult to analyse
using standard feature matching techniques. Our method instead analyses the
structure of plants using only their silhouettes. We employ a generate-and-test
method, using a database of manually modelled leaves and a model for their
composition to synthesise plausible plant structures which are evaluated
against the images. The method is capable of efficiently recovering accurate
estimates of plant structure in a wide variety of imaging scenarios, with no
manual intervention
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