@article{mcclelland2006,
  author={Jamie R. McClelland and Jane M. Blackall and S\'gol\`ne Tarte and Adam C. Chandler and Simon Hughes and Shahreen Ahmad and David B. Landau and David J. Hawkes},
  title="{A continuous 4D motion model from multiple respiratory cycles for use in lung radiotherapy}",
  journal={Medical Physics},
  volume={33},
  number={9},
  pages={3348-3358},
  month={September},
  year={2006},
}

@article{frey2007,
  author =   {Brenden J. Frey and Delbert Dueck},
  title =    "{Clustering by Passing Messages Between Data Points}",
  journal =    {Science},
  year =   2007,
  volume =   315,
  pages =  {972--976}
}

@inproceedings{macqueen1965,
  author =   {James MacQueen},
  title =    "{Some methods for classification and analysis of multivariate observations}",
  booktitle = {Fifth Berkeley Symposium on Mathematical Statistics and Probability},
  year =   {1965},
}

@inproceedings{duchenne2009,
  title = "{A Tensor-Based Algorithm for High-Order Graph Matching}",
  author = {Olivier Duchenne and Francis Bach and Inso Kweon and Jean Ponce},
  booktitle = CVPR,
  month = {June},
  year = {2009}
}

A method of measuring distances over a triangular mesh instead of along the edges of the mesh. This might be extendible to high dimensions and changing dimensionality and can then be applied to Riemannian Manifold Learning (lin2006).

@inproceedings{cooper2009,
  title = "{Learning Signs from Subtitles: A Weakly Supervised Approach to Sign Language Recognition}",
  author = {Helen Cooper and Richard Bowden},
  booktitle = CVPR,
  month = {June},
  year = {2009}
}

A very small feature descriptor. This uses discrete histograms encoded as a binary tree (like huffman coding). The number of trees is small so they are encoded explicitly with an index. This is geared towards cell phone applications. Communicating the descriptors over a low bandwidth link is thus dramatically sped up.

@inproceedings{chandrasekhar2009,
  title = "{CHoG: Compressed Histogram of Gradients: A Low Bit-Rate Feature Descriptor}",
  author = {Vijay Chandrasekhar and Gabriel Takacs and David Chen and Sam Tsai and Radek Grzeszczuk and Bernd Girod},
  booktitle = CVPR,
  month = {June},
  year = {2009}
}

This is a paper which provides a fast algorithm for nearest neighbor search in general metric spaces. It does not make use of any coordinate system. This is similar in approach to the divide and conquer approach in branch and bound algorithms, and is related to kd-trees in euclidean spaces.

@inproceedings{komodakis2009,
  title = "{Beyond Pairwise Energies: Efficient Optimization for Higher-order MRFs}",
  author = {Nikos Komodakis and Nikos Paragios},
  booktitle = CVPR,
  month = {June},
  year = {2009}
}

Looks like the same material as luxburg2007, which is really good and should be read by anyone interested in spectral clustering or laplacian eigenmaps.

A survey paper on spectral clustering and lagrangian eigenmaps. This is a very well written, and clear survey paper, I highly recommend it as a reference.

@article{luxburg2007,
  title = "{A Tutorial on Spectral Clustering}",
  author = {Ulrike von Luxburg},
  journal = {Statistics and Computing},
  month = {December},
  year = 2007,
  volume = 17,
  number = 4
}

A set of slides on spectral clustering.

A good survey paper on manifold learning techniques.

@misc{maaten2007,
  author = {L. J. P. van der Maaten and E. O. Postma and H. J. van den Herik},
  howpublished = {Published online.},
  title = {Dimensionality Reduction: A Comparative Review},
  url = {http://www.cse.wustl.edu/~mgeorg/readPapers/manifold/maaten2007_survey.pdf},
  year = {2007},
  note = {submitted to JMLR 2009}
}

This method is named LOGMAP and based on the Riemannian Normal Coordinates around a single central point. This creates a method of manifold learning where a single central point is chosen and all the data points are projected into the tangent space of that point.
The immediate neighborhood of the central point is computed by PCA. The distance to further points are computed as geodesics over the neighborhood graph as in ISOMAP. The direction from the central point is computed by interpolating the derivative of the distance over the neighborhood of the central point.

@INPROCEEDINGS{brun2005,
  author = {A. Brun and C.-F. Westin and M. Herberthson and H. Knutsson},
  title = "{Fast Manifold Learning Based on Riemannian Normal Coordinates}",
  booktitle = {Proceedings of the 14th Scandinavian Conference on
               Image Analysis (SCIA'05)},
  year = {2005},
  address = {Joensuu, Finland},
  month = {June},
  liu_class = {B1},
  mi_research_area = {CCALearning,cmiv}
}

A method of creating a simplical mesh with changing dimensionality over a set of high dimensional data. This is manifold learning in the true sense of the words, as a classification of the surface structure of a manifold in a high dimensional space.

@article{freedman2002,
  title =    "{Efficient Simplicial Reconstructions of Manifolds
              from Their Samples}",
  author =   {Daniel Freedman},
  journal = PAMI,
  year =   {2002},
  month = {October},
  volume =   24,
  number =   10,
  pages =  {1349-1357}
}

This is the same as LOGMAP (brun2005) except that distances to far points are computed as the angle preserving positions in the simplical reconstruction of the data as given by freedman2002.

@inproceedings{lin2006,
  title="{Riemannian Manifold Learning for Nonlinear Dimensionality Reduction}",
  author={Tony Lin and Hongbin Zha and Sang Uk Lee},
  booktitle=ECCV,
  year=2006
}

This is a graphics paper which presents a method to map a texture onto a three dimensional object. It shares many things with the Logmap approach (here thought of in the other direction, called exponential maps and cited from [Do Carmo 1976]). There is an assumption that the manifold lives in three dimensions in how the distances from the center point are computed (compute from neighbor and rotate the tangent space into the tangent space of the center point) This rotation as given is only defined for two dimensional surfaces in three dimensions. They acheive very fast real time results (hundreds of frames per second). There is an additional interface to fix points in the texturing, however, this is acheived with a simple thin plate spline deformation. The mapping is aborted and a hole is formed if the curvature of the manifold is too great and too much distortion is introduced. Some references are given for computing distances more accurately over well defined surfaces (better than shortest path graph distance).

@inproceedings{brand2003,
  title = "{Charting a Manifold}",
  author = {Matthew Brand},
  booktitle = NIPS,
  year = {2003},
  pages = {961--968},
  publisher = {MIT Press}
}

@inproceedings{brand2004,
  title = "{Minimax Embeddings}",
  author = {Matthew Brand},
  booktitle = NIPS,
  year = {2004},
  publisher = {MIT Press}
}

@inproceedings{roweis2001,
  author =   {Sam T Roweis and Lawrence K Saul and Geoffrey E Hinton},
  title =    "{Global Coordination of Local Linear Models}",
  booktitle = NIPS,
  year =   {2001},
}

This paper parameterizes manifolds which have cyclic structure. The basic method is to take the manifold and represent it as a graph as in Isomap. Then the graph is cut using method (TODO cite the paper) to cut it either far from a point or along a minimum spanning tree boundary. A duplicate graph is then attached along the edges which were cut. This is accomplished by taking each cut edge and having one end in copy A and the other end in copy B. This graph is then cut again to produce a larger graph with correct tesselation along the cut boundaries. This graph is then embedded using Isomap and can be further analysed to determine where the cyclic structure was placed. A correction term can be incorporated for parameterizing a frustum in a natural way (instead of isometric way).

@inproceedings{dixon2006,
  title="{How to project ‘circular’ manifolds using geodesic distances?}",
  author={Michael Dixon and Nathan Jacobs and Robert Pless},
  booktitle=CVPRW,
  year={2006}
}

@inproceedings{lee2004,
  title="{How to project ‘circular’ manifolds using geodesic distances?}",
  author={John Aldo Lee and Michel Verleysen},
  booktitle={European Symposium on Artificial Neural Networks},
  year={2004}
}

This paper analyses circular structures within data sets from a traditional topological perspective. A point cloud is augmented into a simplicial mesh by forming triangular pieces. This simplicial structure is analysed for cocycles to determine the topological structures in the manifold. A smoothest cocycle is then found for each equivalent set of cocycles. These are then used to parameterize the manifold by its cyclic structures. This method is able to deal with such diverse structures as a two holed torus by parameterizing each loop using two parameters. This paper also includes a method for finding the sampling scale of a manifold by analysing the properties of the triangles in the mesh in a total ordering by scale of the triangles.

@inproceedings{ek2008,
  title="{GP-LVM for Data Consolidation}",
  author={Carl Henrik Ek and Philip H.S. Torr and Neil D. Lawrence},
  booktitle={Learning from Multiple Sources (NIPS workshop)},
  year={2008}
}

@inproceedings{geiger2009,
  title = "{Rank Priors for Continuous Non-Linear Dimensionality Reduction}",
  author = {Andreas Geiger and Raquel Urtasun and Trevor Darrell},
  booktitle = CVPR,
  month = {June},
  year = {2009}
}

A method similar to GP-LVM except that both a forward and reverse mapping are learned. The forward mapping is a probabilistic mapping like in GP-LVM and the reverse mapping is a maximum likelihood map. When both are combined a projection function is created, which is used to form an optimization problem. An optimization is run to minimize the reconstruction error (as given by the projection function). This algorithm is proven to find the principal surface given enough sample data.
This method has problems with local minima and must be initialized well to converge to anything useful (like a GP-LVM). In this paper Isomap is used to initialize the algorithm.
The thing I like the most about this paper is the ability to quantitatively asses the quality of an embedding using the reconstruction error.

@inproceedings{gerber2009,
  title="{Dimensionality Reduction and Principal Surfaces via Kernel Map Manifolds}",
  author={Samuel Gerber and Tolga Tasdizen and Ross Whitaker},
  booktitle=ICCV,
  year={2009}
}

@inproceedings{lawrence2003,
  title="{Gaussian Process Latent Variable Models for
          Visualisation of High Dimensional Data}",
  author={Neil D. Lawrence},
  booktitle={NIPS},
  year={2003}
}

A non-linear dimensionality reduction technique which is based off of the kernel trick. This paper includes an analysis of GP-LVM in the framework of PCA from a probabilistic perspective. Kernel PCA is presented as the reverse of GP-LVM, meaning that the kernel trick is applied at the other end of the embedding. In GP-LVM a kernel is used to measure distances in the embedding space to match distances in the original data space. This method has effectively supplanted GTM and much of Self-Organizing Maps.

@article{lawrence2005,
  title="{Probabilistic Non-linear Principal Component Analysis with
           Gaussian Process Latent Variable Models}",
  author={Neil D. Lawrence},
  journal={Journal of Machine Learning Research},
  year={2005}
}

@inproceedings{leen2006,
  author =   {Gayle Leen and Colin Fyfe},
  title =    "{A Gaussian Process Latent Variable Model formulation of Canonical Correlation Analysis}",
  booktitle = ESANN,
  year =   {2006},
}

This paper uses the Kernel Trick to perform PCA on a larger space than the input space. This allows for non-linear structures to be extracted, without actually needing to compute the points in the expanded space. As an example imaging expanding the input space by including the square of each dimension as a new dimension. This allows the modeling of quadratic structures with linear functions. Instead of actually performing this computation to find the expanded feature points, a kernel is used which directly computes the similarity between points in the feature space. Using a Gaussian kernel it is even possible to perform this computation when the corresponding feature space is infinitely dimensional. This is one of the most simple and effective things that can be done with kernels.
From the paper: "The general question that function k does correspond to a dot product in some space F has been discussed by Boser et al. (1992) and Vapnik (1995): Mercer’s theorem of functional analysis implies that if k is a continuous kernel of a positive integral operator, there exists a mapping into a space where k acts as a dot product"

@article{scholkopf1998,
  title="{Nonlinear Component Analysis as a Kernel Eigenvalue Problem}",
  author={Bernhard Scholk\"{o}pf and Alexander Smola and
          Klaus-Robert M\"{u}ller},
  journal={Neural Computation},
  volume=10,
  pages={1299-1319},
  year={1998}
}

A manifold learning technique where a grid of points are laid down in the manifold space. We assume that the distribution of the data in the manifold space is distributed by gaussians around the laid out points. A mapping is then used to project each gaussian into the high dimensional space, which can then describe the data as a mixture of gaussians.
This technique has been largely superseded by Gaussian Process Latent Variable Models (GP-LVM).

@article{bishop1998,
  title = "{GTM: The Generative Topographic Mapping}",
  author = {Christopher M. Bishop and Christopher K. I. Williams},
  journal = {Neural Computation},
  year = {1998}
  volume = {10},
  pages = {215--234}
}

This is a paper which looks at under what circumstances ISOMAP does a good job of finding the correct parameterization.

@inproceedings{donoho2003,
  author = {David L. Donoho and Carrie Grimes},
  title = "{Hessian Eigenmaps: Locally Linear Embedding Techniques for High-dimensional Data}",
  booktitle = {Proc Natl Acad Sci USA},
  year = {2003},
  month = {May},
}

I only skimmed this. This adds a constant to all distances in ISOMAP to create a positive semi-definite kernel (the additive constant problem in MDS). The correct constant to add is found through an eigenvalue problem. This method is particularly good when the data is sampled noisily from the manifold and therefore the distances are significantly purturbed from the manifold distances (which would naturally lead to a positive semi-definite similarity kernel matrix).

@article{choi2007,
  title = "{Robust kernel Isomap}",
  author = {Choi, Heeyoul and Choi, Seungjin},
  journal = {Pattern Recognition},
  volume = {40},
  number = {3},
  year = {2007},
  pages = {853--862},
  doi = {http://dx.doi.org/10.1016/j.patcog.2006.04.025}
}

This is a paper which looks at under what circumstances ISOMAP does a good job of finding the correct parameterization.

@inproceedings{donoho2003,
  author = {David L. Donoho and Carrie Grimes},
  title = "{Hessian Eigenmaps: Locally Linear Embedding Techniques for High-dimensional Data}",
  booktitle = {Proc Natl Acad Sci USA},
  year = {2003},
  month = {May},
}

The original ISOMAP paper in Science.

@Article{tenenbaum2000,
  author =   {Joshua B Tenenbaum and Vin de Silva and John C Langford},
  title =    "{A Global Geometric Framework for Nonlinear Dimensionality Reduction}",
  journal =    {Science},
  year =   2000,
  volume =   290,
  number =   5500,
  pages =  {2319-2323}
}

@inproceedings{gerber2007,
  title="{Robust Non-linear Dimensionality Reduction using Successive
         1-Dimensional Laplacian Eigenmaps}",
  author={Samuel Gerber and Tolga Tasdizen and Ross Whitaker},
  booktitle=ICML,
  year={2007}
}

This method starts with a graph representation. Many manifold methods immediately simplify to a neighborhood graph, so this isn't a big deal. The idea is to embed this graph into a small number of dimensions while preserving the structure of the graph. This means that the embedding should be reversible, so that the original graph can be reconstructed using only the positions in the embedding space (and the algorithm to be used to construct the graph). As with MVU this is formulated as a positive semi-definite optimization. The objective function is slightly different than in MVU in that it includes both the kernel matrix and the adjacency matrix, and the constraints are completely different in that they try to make the embedding reversible by preserving the graph structure. One thing to note is that distances are not preserved in the embedding space but just the relative distance between neighbors and non-neighbors. I think this is a better approach than MVU for many applications.

@inproceedings{shaw2009,
  author =   {Blake Shaw and Tony Jebara},
  title =    "{Structure Preserving Embedding}",
  booktitle = ICML,
  year =   {2009},
}

A modification to Maximum Variance Unfolding with side channel data. This attempts to find the MVU which most preserves the ability of the subspace to differentiate labeled data. This is motivated by preserving cluster labels in the low dimensional data, I'm not sure what if anything prevents the extension of this method to continuous side channel data.

@inproceedings{song2007,
  title = "{Colored Maximum Variance Unfolding}",
  author = {Le Song and Alex Smola and Karsten Borgwardt and Arthur Gretton},
  booktitle = NIPS,
  year = {2007}
}

Original paper on Maximum Variance Unfolding (back when it was called Semi-Definite Embedding).

@inproceedings{weinberger2004,
  title="{Unsupervised Learning of Image Manifolds by Semidefinite Programming}",
  author={K. Q. Weinberger and L. K. Saul},
  booktitle=CVPR,
  pages = {988-995},
  year=2004
}

A re-explanation of Maximum Variance Unfolding (also where it was renamed) with different examples.

@inproceedings{weinberger2006,
  title="{An Introduction to Nonlinear Dimensionality Reduction by
         Maximum Variance Unfolding}",
  author={K. Q. Weinberger and L. K. Saul},
  booktitle=AAAI,
  note = {Nectar paper},
  year=2006
}

Hilbert-Schmidt Independence Criterion.
A way of measuring the dependence between two distributions in kernel Hilbert spaces. This is used in colored Maximum Variance Unfolding. This uses the Hilbert-Schmidt Norm (like Frobinius norm in matrices but for kernel spaces).
I have yet to understand how this is functionally different from using the kullback-leibler divergence as is done in charting.

@inproceedings{sandler2009,
  title = "{Nonnegative Matrix Factorization with Earth Mover's Distance Metric}",
  author = {Roman Sandler and Michael Lindenbaum},
  booktitle = CVPR,
  month = {June},
  year = {2009}
}

@inproceedings{wang2009,
  title = "{Multiplicative Nonnegative Graph Embedding}",
  author = {Changhu Wang and Zheng Song and Shuicheng Yan and
            Lei Zhang and Hong-Jiang Zhang},
  booktitle = CVPR,
  month = {June},
  year = {2009}
}

@inproceedings{zafeiriou2009,
  title = "{Nonlinear Nonnegative Component Analysis}",
  author = {Stefanos Zafeiriou and Maria Petrou},
  booktitle = CVPR,
  month = {June},
  year = {2009}
}

The original Canonical Correlation Analysis paper. I have only skimmed it, I think the wikipedia page probably has a better explanation at this point.

@article{bartlett1941,
  title="{The Statistical Significance of Cononical Correlations}",
  author={M. S. Bartlett},
  journal={Biometrika},
  volume=32,
  number=1,
  pages={29-37},
  year={1941}
}

I feel like there is a simple idea in this paper, but I still don't get it. Perhaps on the next reading. This is an extension of laplacian eigenmaps (or maybe charting) which is able to work with multiple data sources. It uses a probabilistic framework and has as a special case laplacian eigenmaps. It basically works by trying to align local charts in each data set to a global coordinate system (like in charting).

@inproceedings{verbeek2003,
  author = {Jakob J. Verbeek and Sam T. Roweis and Nikos Vlassis},
  title = "{Non-linear CCA and PCA by Alignment of Local Models}",
  booktitle = NIPS,
  volume = {16},
  year = {2003},
  pages = {297--304},
  publisher = {MIT Press}
}

This method is called DrLIM. The idea is to map points into an embedding space using only neighborhood relationships. This method is able to project new points into the embedding space.
Neighborhood relationships are determined between points by analogy between springs. Two kinds of "springs" are created. The first kind repulses points apart if they the distance between them is less than a threshold but do nothing if the distance is above that threshold. The second kind always pulls points together. The neighborhood relationships are encoded into a large spring network which is then solved for its rest state using a general solver.

@inproceedings{hadsell2006,
  title = "{Dimensionality Reduction by Learning an Invariant Mapping}",
  author = {Raia Hadsell and Sumit Chopra and Yann LeCun},
  booktitle = CVPR,
  year = {2006}
}

@inproceedings{ali2007,
 author = {Saad Ali and Mubarak Shah},
 title = "{A lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis}",
 booktitle = CVPR,
 year = {2009}
}

@inproceedings{hu2008,
 author = {Min Hu and Saad Ali and Mubarak Shah},
 title = "{Learning Motion Patterns in Crowded Scenes Using Motion Flow Field}",
 booktitle = ICPR,
 year = {2008}
}

@article{li2008,
  author={Guang Li and Deborah Citrin and Kevin Camphausen and Boris Mueller and Chandra Burman and Borys Mychalczak and Robert W. Miller and Yulin Song},
  title="{Advances in 4D Medical Imaging and 4D Radiation Therapy}",
  journal={TCRT},
  volume={7},
  number={1},
  pages={67-82},
  month={February},
  year={2008},
}

@inproceedings{wang2007,
  author = {Xiaogang Wang and Xiaoxu Ma and Eric Grimson},
  title = "{Unsupervised Activity Perception by Hierarchical Bayesian Models}",
  booktitle=CVPR,
  year = {2007},
}

@techreport{wang2008,
 author = {Xiaogang Wang and Keng Teck Ma and Gee-Wah Ng and Eric Grimson},
 title = "{Trajectory Analysis and Semantic Region Modeling Using a Nonparametric Bayesian Model}",
 number = {MIT-CSAIL-TR-2008-015},
 institution = {Massachusetts Institute of Technology},
 year = {2008}
}

@inproceedings{myronenko2009,
  title = "{Image Registration by Minimization of Residual Complexity}",
  author = {Andriy Myronenko and Xubo Song},
  booktitle = CVPR,
  month = {June},
  year = {2009}
}

Determine optic flow by tracking where features move. This is a slow method.

@inproceedings{brox2009,
  title = "{Large Displacement Optical Flow}",
  author = {Thomas Brox and Christoph Bregler and Jitendra Malik},
  booktitle = CVPR,
  month = {June},
  year = {2009}
}

@inproceedings{reddy2009,
  title = "{Enforcing Integrability by Error Correction using l1-minimization}",
  author = {Dikpal Reddy and Amit Agrawal and Rama Chellappa},
  booktitle = CVPR,
  month = {June},
  year = {2009}
}

@article{choy2008,
  author={Jenny Susana Choy and Ghassan S. Kassab},
  title={Scaling of myocardial mass to flow and morphometry of coronary arteries},
  journal={Applied Physiology},
  month ={May},
  year ={2008},
  volume =104,
  pages={1281--1286}
}

@article{gabrys2006,
  author={El\.{z}bieta Gabry\'{s} and Marek Rybaczuk and Alicja K\c{e}dzia},
  title={Blood Flow Simulation Through Fractal Models of Circulatory System},
  journal={Chaos Solitons and Fractals},
  year ={2006},
  volume =27,
  number = 1,
  pages={1-7}
}

@article{kamiya2007,
  author={Akira Kamiya and Tatsuhisa Takahashi},
  title={Quantitative Assesments of Morphological and Functional Propertios of Biological Trees Based on their Fractal Nature},
  journal={Applied Physiology},
  month ={March},
  year ={2007},
  volume =102,
  pages={2315--2323}
}

@article{kassab2005,
  author={Ghassan S. Kassab},
  title={Scaling Laws of Vascular Trees: of Form and Function},
  journal={Applied Physiology},
  month ={Am J Physiol Heart Circ Physiol},
  year ={2005},
  volume =290,
  pages={894--903}
}

@article{khanin2003,
  author={M. A. Khanin and I. B. Bukharov},
  title={Optimal Structure of the Circulatory System: Influence of the Energetics of Vascular Smooth Muscles},
  journal={Optimization Theory and Applications},
  volume={116},
  number={2},
  pages={347--357},
  year=2003
}

@article{migliavacca2005,
  author={Francesco Migliavacca and Gabriele Dubini},
  title={Computational Modeling of Vascular Anastomoses},
  journal={Biomechan Model Mechanobiol},
  year ={2005},
  volume =3,
  pages={235--250}
}

@article{tawhai2000,
 author={M. H. Tawhai and A. J. Pullan and P. J. Hunter},
 title={Generation of an Anatomically Based Three-Dimensional Model of the Conducting Airways},
 journal={Ann. Biomed. Eng.}, volume=28, pages={793--802}, year=2000
}

@article{turcotte1998,
 author={D. L. Turcotte and J. D. Pelletier and W. I. Newman},
 title={Networks with Side Branching in Biology},
 journal={J. Theor. Biol.}, volume=193, number={577--592}, year=1998
}

@article{schreiner1993,
 author={Wolfgang Schreiner},
 title={Computer Generation of Complex Arterial Tree Models},
 journal={Journal Biomedical Engineering},
 volume=15,
 number=2,
 pages={148--150},
 year=1993
}

@ARTICLE{schreiner1996,
  author={Wolfgang Schreiner and Friederike Neumann and Martin Neumann and Adelheid End and Michael R. M\"uller},
  title={Structural Quantification and Bifurcation Symmetry in Arterial Tree Models Generated by Constrained Constructive Optimization},
  journal={J. Theor. Biol.},
  year={1996},
  pages={161--174},
  volume={180},
}

@ARTICLE{schreiner1999,
  author={Wolfgang Schreiner and Friederike Neumann and Rudolf Karch and Martin Neumann and Susanne M. Roedler and Adelheid End},
  title={Shear Stress Distribution in Arterial Tree Models, Generated by Constrained Constructive Optimization},
  journal={J. Theor. Biol.},
  year={1999},
  pages={27--45},
  volume={198},
}

@ARTICLE{schreiner2003,
  author={Wolfgang Schreiner and Rudolf Karch and Martin Neumann and Friederike Neumann and Susanne M. Roedler and Georg Heinze},
  title={Heterogeneous Perfusion is a Consequence of Uniform Shear Stress in Optimized Arterial Tree Models},
  journal={J. Theor. Biol.},
  year={2003},
  pages={285--301},
  volume={220},
}

@article{schreiner2006,
  author={Wolfgang Schreiner and Rudolf Karch and Martin Neumann and Friederike Neumann and Paul Szawlowski and Susanne Roedler},
  title={Optimized Arterial Trees Supplying Hollow Organs},
  journal={Medical Engineering and Physics},
  year ={2006},
  volume =28,
  pages={416--429}
}