selectedpapers.bib
@INPROCEEDINGS{Wang+Makedon-Rstar-Hi:2004,
ADDRESS = {New York, New York, USA},
AUTHOR = {Wang, Yuhang and Makedon, Fillia and Chakrabarti, Amit},
BOOKTITLE = {To appear in Proceedings of the 12th Annual ACM International Conference on Multimedia},
TITLE = {R*-Histograms: Efficient Representation of Spatial Relations between Objects of Arbitrary Topology},
YEAR = 2004,
ABSTRACT = {Representation of relative spatial relations between objects is
often required in many multimedia database applications because
spatial relations between objects in an image convey important
information about the image. Quantitative representation of
spatial relations taking into account shape, size, orientation and
distance is often required. The \mbox{R-Histogram} is such a
quantitative representation of spatial relations between two
objects. However, this method only considers pixels on the object
boundary, assuming that the objects are homeomorphic to a 2-ball.
For objects with more complicated topology, we propose in this
paper the \mbox{R*-Histogram}, a new extension to the
\mbox{R-Histogram}. The \mbox{R*-Histogram} generalizes the
\mbox{R-Histogram} by taking into account all the pixels in the
objects. We also introduce an efficient $O(kN\/log N)$ time
algorithm to compute the \mbox{R*-Histogram}, which is
asymptotically faster than the original $O(N^{2})$ time algorithm
for the \mbox{R-Histogram} even when $k=O(n)$. Here, $N=n^{2}$
denotes the number of pixels in the processed $n\times n$ image
and $k$ is the number of different directions considered. The
effectiveness of the \mbox{R*-Histogram} is evaluated empirically
with a Query By Example (QBE) system on a database of 2000
synthetic images containing objects with complicated shape and
topology. Experiments have shown that the similarly search results
match human intuition very well.}
}
@INPROCEEDINGS{Wang+MakedonETAL-MiniMarkGenePhen:04,
AUTHOR = {Yuhang Wang and Fillia Makedon and James Ford},
TITLE = {Mining Marker Genes for Phenotype Classification using Microarray Gene Expression Data},
BOOKTITLE = {In submission},
YEAR = 2004
}
@ARTICLE{Wang+MakedonETAL-FastAlgoCompForc:04,
AUTHOR = {Yuhang Wang and Fillia Makedon and Robert L. (Scot) Drysdale},
TITLE = {Fast Algorithms to Compute the Force Histogram},
JOURNAL = {In submission},
YEAR = 2004
}
@INPROCEEDINGS{Wang+MakedonETAL-BipaGrapMatcFram:04,
AUTHOR = {Yuhang Wang and Fillia Makedon and James Ford},
TITLE = {A Bipartite Graph Matching Framework for Finding Correspondences between Structural Elements in Two Proteins},
BOOKTITLE = {To appear in Proceedings of the 26th Annual International Conference of
the IEEE Engineering in Medicine and Biology Society},
DATE = {September 1--5},
YEAR = 2004,
ABSTRACT = {A protein molecule consists one or more chains
of amino acid sequences that fold into a complex
three-dimensional structure. A protein's functions are often
determined by its 3D structure, and so comparing the similarity
of 3D structures between proteins is an important problem. To
accomplish such comparison, one must align two proteins properly
with rotation and translation in 3D space. Finding the
correspondences between structural elements in the two proteins
is the key step in many protein structure alignment algorithms.
In this paper, we introduce a new graph theoretic framework
based on bipartite graph matching for finding sufficiently good
correspondences. It is capable of providing both
sequence-dependent and sequence-independent correspondences. It
is a general framework for pair-wise matching of atoms, amino
acids residues or secondary structure elements.
},
URL = {http://www.cs.dartmouth.edu/~wyh/papers/embs04_alignment.pdf}
}
@INPROCEEDINGS{Wang+Makedon-ApplReliFeatFilt:04,
ADDRESS = {Stanford, California},
AUTHOR = {Yuhang Wang and Fillia Makedon},
BOOKTITLE = {To appear in Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference},
TITLE = {Application of Relief-F Feature Filtering Algorithm to Selecting Informative Genes
for Cancer Classification using Microarray Data (poster paper)},
DATE = {August 16--19},
YEAR = 2004,
ABSTRACT = {Numerous recent studies have shown that microarray gene expression
data is useful for cancer classification. Classification based on
microarray data is very different from previous classification
problems in that the number of features (genes) greatly exceeds
the number of instances (tissue samples). It has been shown that
selecting a small set of informative genes can lead to improved
classification accuracy. It is thus important to first apply
feature selection methods prior to classification. In the machine
learning field, one of the most successful feature filtering
algorithms is the Relief-F algorithm. In this work, we
empirically evaluate its performance on three published cancer
classification data sets. We use the linear SVM and the k-NN
as classifiers in the experiments, and compare the performance of
Relief-F with other feature filtering methods, including
Information Gain, Gain Ratio, and $\chi^{2}$-statistic. Using the
leave-one-out cross validation, experimental results show that the
performance of Relief-F is comparable with other methods.
},
URL = {http://www.cs.dartmouth.edu/~wyh/papers/csb04_geneselection.pdf}
}
@INPROCEEDINGS{Wang+MakedonETAL-GeneFuzzSemaMeta:04,
ADDRESS = {Tucson, Arizona},
AUTHOR = {Yuhang Wang and Fillia Makedon and James Ford and Li Shen and Dina Goldin},
BOOKTITLE = {Proceedings of the Fourth ACM/IEEE-CS Joint Conference on Digital Libraries},
TITLE = {Generating Fuzzy Semantic Metadata describing Spatial Relations from Images using the {R}-Histogram},
PAGES = {202--211},
YEAR = 2004,
ABSTRACT = {Automatic generation of semantic metadata
describing spatial relations is highly desirable for image
digital libraries. Relative spatial relations between objects
in an image convey important information about the image.
Because the perception of spatial relations is subjective, we
propose a novel framework for automatic metadata generation
based on fuzzy k-nn classification that generates fuzzy
semantic metadata describing spatial relations between objects
in an image. For each pair of objects of interest, the
corresponding R-histogram is computed and used as input for a
set of fuzzy k-nn classifiers. The R-histogram is a
quantitative representation of spatial relations between two
objects. The outputs of the classifiers are soft class labels
for each of the following eight spatial relations: 1) LEFT OF,
2) RIGHT OF, 3) ABOVE, 4) BELOW, 5) NEAR, 6) FAR, 7) INSIDE,
8) OUTSIDE. Because the classifier-training stage
involves annotating the training images manually, it is
desirable to use as few training images as possible. To
address this issue, we applied existing prototype selection
techniques and also devised two new extensions. We evaluated
the performance of different fuzzy k-nn algorithms and
prototype selection algorithms empirically on both synthetic
and real images. Preliminary experimental results show that
our system is able to obtain good annotation accuracy (92\%-98\%
on synthetic images and 82\%-93\% on real images) using only a
small training set (4-5 images).},
URL = {http://www.cs.dartmouth.edu/~wyh/papers/jcdl04_metadata.pdf}
}
@INPROCEEDINGS{Wang+Makedon-R-Hi:2003,
ADDRESS = {Berkeley, California, USA},
AUTHOR = {Wang, Yuhang and Makedon, Fillia},
BOOKTITLE = {The 11th Annual ACM International Conference on Multimedia},
TITLE = {R-Histogram: Quantitative Representation of SpatialRelations for
Similarity-Based Image Retrieval},
PAGES = {323--326},
YEAR = 2003,
ABSTRACT = {Representation of relative spatial relations between objects is
required in many multimedia database applications. Quantitative
representation of spatial relations taking into account shape,
size, orientation and distance is often required. This cannot be
accomplished by assimilating an object to elementary entities
such as the centroid or the minimum bounding rectangle. Thus many
authors have proposed numerous representations based on the
notion of histograms of angles. However, they can only represent
directional relations, but not the topological spatial relations
''inside'' and ''overlap.'' Moreover, distance information is not
explicitly taken into account. To address these issues, we
propose in this paper a new histogram representation called
R-Histogram that extends the histogram of angles by incorporating
both angles and labeled distances. Dissimilarity between images
is then defined by the distance between corresponding
R-Histograms. A prototype Query By Example (QBE) system using the
R-Histogram has been implemented. The effectiveness of our
algorithm is demonstrated with experiments on two databases of
2000 synthetic images.},
URL = {http://www.cs.dartmouth.edu/~wyh/papers/acmmm03_rhist.pdf}
}
@INPROCEEDINGS{Wang+SteinbergETAL-QuanEvol:2003,
ADDRESS = {Montr\'eal, Qu\'ebec, Canada},
AUTHOR = {Wang, Yuhang and Steinberg, Tilmann and Makedon, Fillia and
Wishart, Heather and Saykin, Andrew},
BOOKTITLE = {The Sixth Annual International Conference on Medical Image
Computing and Computer-Assisted Intervention (MICCAI 2003)},
TITLE = {Quantifying Evolving Processes in Multimodal {3D} Medical Images},
YEAR = 2003,
PAGES = {101--108},
ABSTRACT = {Quantitative measurements of changes in evolving brain
pathology, such as multiple sclerosis lesions and brain tumors,
are important for clinicians to perform pertinent diagnoses and
to help in patient follow-up. Lesions or tumors can vary over
time in size, shape, location and composition because of natural
pathological processes or the effect of a drug treatment or
therapy. In the past, people have used as a quantitative
measurement the change in total or regional lesion/tumor volume.
In this paper we propose a new model to quantify changes in
evolving processes in multimodal 3D medical images. We believe
this model reflects changes in pathology more accurately because
it simultaneously takes into account information in multiple
imaging modalities and the location of lesion/tumor voxels. We
demonstrate the effectiveness of this model with experiments on
synthetic lesion data.},
URL = {http://www.cs.dartmouth.edu/~wyh/papers/miccai03_quantify.pdf}
}
@INPROCEEDINGS{Makedon+WangETAL-SystFram:2003,
ADDRESS = {Capri Island, Italy},
AUTHOR = {Makedon, Fillia and Wang, Yuhang and Steinberg, Tilmann and
Wishart, Heather and Saykin, Andrew and Ford, James and Ye, Song
and Shen, Li},
BOOKTITLE = {Proceedings of First International IEEE EMBS Conference on
Neural Engineering},
PAGES = {495--498},
TITLE = {A System Framework for the Integration and Analysis of Multi-modal
Spatiotemporal Data Streams: A Case Study in {MS} Lesion Analysis},
YEAR = 2003,
ABSTRACT = {This paper describes the development of MS-Analyze, a system
framework to analyze and detect patterns in brain pathology of
multiple sclerosis (MS) as the disease progresses over time. We
are building the system to collect, analyze and integrate
disparate data extracted from observing the behavior of MS
lesions and surrounding pathology on magnetic resonance imaging
(MRI) over time. Multiple sclerosis (MS) is a brain disease that
affects over 250,000 people in the USA alone. Various MRI
sequences are used to monitor brain changes during the natural
progression of the disease, and as different drug treatments are
explored to slow the disease. The outcome is a set of disparate
data streams that need to be correlated efficiently to discover
patterns of MS pathology and plan treatment. However, MS data
analysis faces the same computational problems as many other
scientific domains with heterogeneous data streams: the need for
integration of and access to large amounts of data, beyond what
is normally available to any one given laboratory. MS-Analyze
addresses both of these challenges by combining data collection,
data fusion, data analysis, and secure data sharing. MS is a good
application to demonstrate the system because it offers rich data
that challenge the system development.},
URL = {http://www.cs.dartmouth.edu/~wyh/papers/embs03_framework.pdf}
}
@INPROCEEDINGS{Steinberg+WangETAL-SpatMult:2003,
ADDRESS = {Cambridge, Massachusetts, USA},
AUTHOR = {Steinberg, Tilmann and Wang, Yuhang and Makedon, Fillia and Shen,
Li and Saykin, Andrew and Wishart, Heather},
BOOKTITLE = {Proceedings of 15th International Conference on Scientific and
Statistical Database Management (SSDBM '03)},
PAGES = {245--246},
TITLE = {A Spatio-temporal Multi-modal Data Management and Analysis
Environment for Tracking {MS} Lesions},
YEAR = 2003
}
@INPROCEEDINGS{Butler+FitchETAL-Distgoal:2002,
AUTHOR = {Butler, Z. and Fitch, R. and Rus, D. and Wang, Yuhang},
BOOKTITLE = {Proceedings of 2002 IEEE International Conference on Robotics
and Automation (ICRA '02)},
PAGES = {110--116},
TITLE = {Distributed Goal Recognition Algorithms for Modular Robots},
VOLUME = 1,
YEAR = 2002
}
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