40,665 research outputs found
Towards Structural Classification of Proteins based on Contact Map Overlap
A multitude of measures have been proposed to quantify the similarity between
protein 3-D structure. Among these measures, contact map overlap (CMO)
maximization deserved sustained attention during past decade because it offers
a fine estimation of the natural homology relation between proteins. Despite
this large involvement of the bioinformatics and computer science community,
the performance of known algorithms remains modest. Due to the complexity of
the problem, they got stuck on relatively small instances and are not
applicable for large scale comparison. This paper offers a clear improvement
over past methods in this respect. We present a new integer programming model
for CMO and propose an exact B &B algorithm with bounds computed by solving
Lagrangian relaxation. The efficiency of the approach is demonstrated on a
popular small benchmark (Skolnick set, 40 domains). On this set our algorithm
significantly outperforms the best existing exact algorithms, and yet provides
lower and upper bounds of better quality. Some hard CMO instances have been
solved for the first time and within reasonable time limits. From the values of
the running time and the relative gap (relative difference between upper and
lower bounds), we obtained the right classification for this test. These
encouraging result led us to design a harder benchmark to better assess the
classification capability of our approach. We constructed a large scale set of
300 protein domains (a subset of ASTRAL database) that we have called Proteus
300. Using the relative gap of any of the 44850 couples as a similarity
measure, we obtained a classification in very good agreement with SCOP. Our
algorithm provides thus a powerful classification tool for large structure
databases
Pigment Melanin: Pattern for Iris Recognition
Recognition of iris based on Visible Light (VL) imaging is a difficult
problem because of the light reflection from the cornea. Nonetheless, pigment
melanin provides a rich feature source in VL, unavailable in Near-Infrared
(NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical
not stimulated in NIR. In this case, a plausible solution to observe such
patterns may be provided by an adaptive procedure using a variational technique
on the image histogram. To describe the patterns, a shape analysis method is
used to derive feature-code for each subject. An important question is how much
the melanin patterns, extracted from VL, are independent of iris texture in
NIR. With this question in mind, the present investigation proposes fusion of
features extracted from NIR and VL to boost the recognition performance. We
have collected our own database (UTIRIS) consisting of both NIR and VL images
of 158 eyes of 79 individuals. This investigation demonstrates that the
proposed algorithm is highly sensitive to the patterns of cromophores and
improves the iris recognition rate.Comment: To be Published on Special Issue on Biometrics, IEEE Transaction on
Instruments and Measurements, Volume 59, Issue number 4, April 201
Soliton Dynamics in Computational Anatomy
Computational anatomy (CA) has introduced the idea of anatomical structures
being transformed by geodesic deformations on groups of diffeomorphisms. Among
these geometric structures, landmarks and image outlines in CA are shown to be
singular solutions of a partial differential equation that is called the
geodesic EPDiff equation. A recently discovered momentum map for singular
solutions of EPDiff yields their canonical Hamiltonian formulation, which in
turn provides a complete parameterization of the landmarks by their canonical
positions and momenta. The momentum map provides an isomorphism between
landmarks (and outlines) for images and singular soliton solutions of the
EPDiff equation. This isomorphism suggests a new dynamical paradigm for CA, as
well as new data representation.Comment: published in NeuroImag
Connectivity-Enforcing Hough Transform for the Robust Extraction of Line Segments
Global voting schemes based on the Hough transform (HT) have been widely used
to robustly detect lines in images. However, since the votes do not take line
connectivity into account, these methods do not deal well with cluttered
images. In opposition, the so-called local methods enforce connectivity but
lack robustness to deal with challenging situations that occur in many
realistic scenarios, e.g., when line segments cross or when long segments are
corrupted. In this paper, we address the critical limitations of the HT as a
line segment extractor by incorporating connectivity in the voting process.
This is done by only accounting for the contributions of edge points lying in
increasingly larger neighborhoods and whose position and directional content
agree with potential line segments. As a result, our method, which we call
STRAIGHT (Segment exTRAction by connectivity-enforcInG HT), extracts the
longest connected segments in each location of the image, thus also integrating
into the HT voting process the usually separate step of individual segment
extraction. The usage of the Hough space mapping and a corresponding
hierarchical implementation make our approach computationally feasible. We
present experiments that illustrate, with synthetic and real images, how
STRAIGHT succeeds in extracting complete segments in several situations where
current methods fail.Comment: Submitted for publicatio
Part-to-whole Registration of Histology and MRI using Shape Elements
Image registration between histology and magnetic resonance imaging (MRI) is
a challenging task due to differences in structural content and contrast. Too
thick and wide specimens cannot be processed all at once and must be cut into
smaller pieces. This dramatically increases the complexity of the problem,
since each piece should be individually and manually pre-aligned. To the best
of our knowledge, no automatic method can reliably locate such piece of tissue
within its respective whole in the MRI slice, and align it without any prior
information. We propose here a novel automatic approach to the joint problem of
multimodal registration between histology and MRI, when only a fraction of
tissue is available from histology. The approach relies on the representation
of images using their level lines so as to reach contrast invariance. Shape
elements obtained via the extraction of bitangents are encoded in a
projective-invariant manner, which permits the identification of common pieces
of curves between two images. We evaluated the approach on human brain
histology and compared resulting alignments against manually annotated ground
truths. Considering the complexity of the brain folding patterns, preliminary
results are promising and suggest the use of characteristic and meaningful
shape elements for improved robustness and efficiency.Comment: Paper accepted at ICCV Workshop (Bio-Image Computing
A Unified Framework for Integer Programming Formulation of Graph Matching Problems
Graph theory has been a powerful tool in solving difficult and complex problems arising in all disciplines. In particular, graph matching is a classical problem in pattern analysis with enormous applications. Many graph problems have been formulated as a mathematical program then solved using exact, heuristic and/or approximated-guaranteed procedures. On the other hand, graph theory has been a powerful tool in visualizing and understanding of complex mathematical programming problems, especially integer programs. Formulating a graph problem as a natural integer program (IP) is often a challenging task. However, an IP formulation of the problem has many advantages. Several researchers have noted the need for natural IP formulation of graph theoretic problems. The aim of the present study is to provide a unified framework for IP formulation of graph matching problems. Although there are many surveys on graph matching problems, however, none is concerned with IP formulation. This paper is the first to provide a comprehensive IP formulation for such problems. The framework includes variety of graph optimization problems in the literature. While these problems have been studied by different research communities, however, the framework presented here helps to bring efforts from different disciplines to tackle such diverse and complex problems. We hope the present study can significantly help to simplify some of difficult problems arising in practice, especially in pattern analysis
What Makes the Arc-Preserving Subsequence Problem Hard?
International audienceGiven two arc-annotated sequences (S, P ) and (T, Q) representing RNA structures, the Arc-Preserving Subsequence (APS) problem asks whether (T, Q) can be obtained from (S, P ) by deleting some of its bases (together with their incident arcs, if any). In previous studies [3, 6], this problem has been naturally divided into subproblems reflecting intrinsic complexity of arc structures. We show that APS(Crossing, Plain) is NP-complete, thereby answering an open problem [6]. Furthermore, to get more insight into where actual border of APS hardness is, we refine APS classical subproblems in much the same way as in [11] and give a complete categorization among various restrictions of APS problem complexity
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