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A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenTue, 16 Apr 2024 15:16:21 +0000Cellular texture generation
https://resolver.caltech.edu/CaltechAUTHORS:20161019-165009720
Authors: {'items': [{'id': 'Fleischer-K-W', 'name': {'family': 'Fleischer', 'given': 'Kurt W.'}}, {'id': 'Laidlaw-D-H', 'name': {'family': 'Laidlaw', 'given': 'David H.'}}, {'id': 'Currin-B-L', 'name': {'family': 'Currin', 'given': 'Bena L.'}}, {'id': 'Barr-A-H', 'name': {'family': 'Barr', 'given': 'Alan H.'}}]}
Year: 1995
DOI: 10.1145/218380.218447
We propose an approach for modeling surface details such as scales, feathers, or thorns. These types of cellular textures require a representation with more detail than texture-mapping but are inconvenient to model with hand-crafted geometry.
We generate patterns of geometric elements using a biologically-motivated cellular development simulation together with a constraint to keep the cells on a surface. The surface may be defined by an implicit function, a volume dataset, or a polygonal mesh. Our simulation combines and extends previous work in developmental
models and constrained particle systems.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/b5kc8-27h25Pure phase-encoded MRI and classification of solids
https://resolver.caltech.edu/CaltechAUTHORS:GHOieeetmi95
Authors: {'items': [{'id': 'Ghosh-P', 'name': {'family': 'Ghosh', 'given': 'Pratik'}}, {'id': 'Laidlaw-D-H', 'name': {'family': 'Laidlaw', 'given': 'David H.'}}, {'id': 'Fleischer-K-W', 'name': {'family': 'Fleischer', 'given': 'Kurt W.'}}, {'id': 'Barr-A-H', 'name': {'family': 'Barr', 'given': 'Alan H.'}}, {'id': 'Jacobs-R-E', 'name': {'family': 'Jacobs', 'given': 'Russell E.'}, 'orcid': '0000-0002-1382-8486'}]}
Year: 1995
DOI: 10.1109/42.414627
Here, the authors combine a pure phase-encoded magnetic resonance imaging (MRI) method with a new tissue-classification technique to make geometric models of a human tooth. They demonstrate the feasibility of three-dimensional imaging of solids using a conventional 11.7-T NMR spectrometer. In solid-state imaging, confounding line-broadening effects are typically eliminated using coherent averaging methods. Instead, the authors circumvent them by detecting the proton signal at a fixed phase-encode time following the radio-frequency excitation. By a judicious choice of the phase-encode time in the MRI protocol, the authors differentiate enamel and dentine sufficiently to successfully apply a new classification algorithm. This tissue-classification algorithm identifies the distribution of different material types, such as enamel and dentine, in volumetric data. In this algorithm, the authors treat a voxel as a volume, not as a single point, and assume that each voxel may contain more than one material. They use the distribution of MR image intensities within each voxel-sized volume to estimate the relative proportion of each material using a probabilistic approach. This combined approach, involving MRI and data classification, is directly applicable to bone imaging and hard-tissue contrast-based modeling of biological solids.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/khz19-3vv29Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms
https://resolver.caltech.edu/CaltechAUTHORS:LAIieeetmi98
Authors: {'items': [{'id': 'Laidlaw-D-H', 'name': {'family': 'Laidlaw', 'given': 'David H.'}}, {'id': 'Fleischer-K-W', 'name': {'family': 'Fleischer', 'given': 'Kurt W.'}}, {'id': 'Barr-A-H', 'name': {'family': 'Barr', 'given': 'Alan H.'}}]}
Year: 1998
DOI: 10.1109/42.668696
The authors present a new algorithm for identifying the distribution of different material types in volumetric datasets such as those produced with magnetic resonance imaging (MRI) or computed tomography (CT). Because the authors allow for mixtures of materials and treat voxels as regions, their technique reduces errors that other classification techniques can create along boundaries between materials and is particularly useful for creating accurate geometric models and renderings from volume data. It also has the potential to make volume measurements more accurately and classifies noisy, low-resolution data well. There are two unusual aspects to the authors' approach. First, they assume that, due to partial-volume effects, or blurring, voxels can contain more than one material, e.g., both muscle and fat; the authors compute the relative proportion of each material in the voxels. Second, they incorporate information from neighboring voxels into the classification process by reconstructing a continuous function, ρ(x), from the samples and then looking at the distribution of values that ρ(x) takes on within the region of a voxel. This distribution of values is represented by a histogram taken over the region of the voxel; the mixture of materials that those values measure is identified within the voxel using a probabilistic Bayesian approach that matches the histogram by finding the mixture of materials within each voxel most likely to have created the histogram. The size of regions that the authors classify is chosen to match the sparing of the samples because the spacing is intrinsically related to the minimum feature size that the reconstructed continuous function can represent.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/41nmf-n8k69Partial-Volume Bayesian Classification of Material Mixtures in MR Volume Data using Voxel Histograms
https://resolver.caltech.edu/CaltechCSTR:1997.cs-tr-97-12
Authors: {'items': [{'id': 'Laidlaw-D-H', 'name': {'family': 'Laidlaw', 'given': 'David H.'}}, {'id': 'Fleischer-K-W', 'name': {'family': 'Fleischer', 'given': 'Kurt W.'}}, {'id': 'Barr-A-H', 'name': {'family': 'Barr', 'given': 'Alan H.'}}]}
Year: 2001
DOI: 10.7907/Z9S75DB3
We present a new algorithm for identifying the distribution of different material types in volumetric datasets such as those produced with Magnetic Resonance Imaging (MRI) or Computed Tomography (CT). Because we allow for mixtures of materials and treat voxels as regions, our technique reduces the classification artifacts that thresholding can create along boundaries between materials and is particularly useful for creating accurate geometric models and renderings from volume data. It also has the potential to make more-accurate volume measurements and classifies noisy, low-resolution data well. There are two unusual aspects to our approach. First, we assume that, due to partial-volume effects, voxels can contain more than one material, e.g., both muscle and fat; we compute the relative proportion of each material in the voxels. Second, we incorporate information from neighboring voxels into the classification process by reconstructing a continuous function, p(x), from the samples and then looking at the distribution of values that p takes on within the region of a voxel. This distribution of values is represented by a histogram taken over the region of the voxel; the mixture of materials that those values measure is identified within the voxel using a probabilistic Bayesian approach that matches the histogram by finding the mixture of materials within each voxel most likely to have created the histogram. The size of regions that we classify is chosen to match the spacing of the samples because the spacing is intrinsically related to the minimum feature size that the reconstructed continuous function can represent.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/tgp23-4zt02Classification of Material Mixtures in Volume Data for Visualization and Modeling
https://resolver.caltech.edu/CaltechCSTR:1994.cs-tr-94-07
Authors: {'items': [{'id': 'Laidlaw-D-H', 'name': {'family': 'Laidlaw', 'given': 'David H.'}}, {'id': 'Fleischer-K-W', 'name': {'family': 'Fleischer', 'given': 'Kurt W.'}}, {'id': 'Barr-A-H', 'name': {'family': 'Barr', 'given': 'Alan H.'}}]}
Year: 2001
DOI: 10.7907/Z97S7KTR
Material classification is a key stop in creating computer graphics models and images from volume data, We present a new algorithm for identifying the distribution of different material types in volumetric datasets such as those produced with Magnetic Resonance Imaging (NMI) or Computed Tomography (CT). The algorithm assumes that voxels can contain more than one material, e.g. both muscle and fat; we wish to compute the relative proportion of each material in the voxels. Other classification methods have utilized Gaussian probability density functions to model the distribution of values within a dataset. These Gaussian basis functions work well for voxels with unmixed materials, but do not work well where the materials are mixed together. We extend this approach by deriving non-Gaussian "mixture" basis functions. We treat a voxel as a volume, not as a single point. We use the distribution of values within each voxel-sized volume to identify materials within the voxel using a probabilistic approach. The technique reduces the classification artifacts that occur along boundaries between materials. The technique is useful for making higher quality geometric models and renderings from volume data, and has the potential to make more accurate volume measurements. It also classifies noisy, low-resolution data well.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/v1vn7-yhz93Polygon Scan Conversion Derivations
https://resolver.caltech.edu/CaltechCSTR:1995.cs-tr-91-12
Authors: {'items': [{'id': 'Fleischer-K-W', 'name': {'family': 'Fleischer', 'given': 'Kurt'}}]}
Year: 2001
DOI: 10.7907/Z9RR1W8B
[No Abstract]https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/nf2xa-ak451A Multiple-Mechanism Developmental Model for Defining Self-Organizing Geometric Structures
https://resolver.caltech.edu/CaltechCSTR:1995.cs-tr-95-14
Authors: {'items': [{'id': 'Fleischer-K-W', 'name': {'family': 'Fleischer', 'given': 'Kurt W.'}}]}
Year: 2001
DOI: 10.7907/Z9513W74
This thesis introduces a model of multicellular development. The model combines elements of the chemical, cell lineage, and mechanical models of morphogenesis pioneered by Turing, Lindenmayer, and Odell, respectively. The internal state of each cell in the model is represented by a time-varying state vector that is updated by a differential equation. The differential equation is formulated as a sum of contributions from different sources, describing gene transcription, kinetics, and cell metabolism. Each term in the differential equation is multiplied by a conditional expression that models regulatory processes specific to the process described by that term. The resulting model has a broader range of fundamental mechanisms than other developmental models. Since gene transcription is included, the model can represent the genetic orchestration of a developmental process involving multiple mechanisms. We show that a computational implementation of the model represents a wide range of biologically relevant phenomena in two and three dimensions. This is illustrated by a diverse collection of simulation experiments exhibiting phenomena such as lateral inhibition, differentiation, segment formation, size regulation, and regeneration of damaged structures. We have explored several application areas with the model: Synthetic biology. We advocate the use of mathematical modeling and simulation for generating intuitions about complex biological systems, in addition to the usual application of mathematical biology to perform analysis on a simplified model. The breadth of our model makes it useful as a tool for exploring biological questions about pattern formation and morphogenesis. We show that simulated experiments to address a particular question can be done quickly and can generate useful biological intuitions. As an example, we document a simulation experiment exploring inhibition via surface chemicals. This experiment suggests that the final pattern depends strongly on the temporal sequence of events. This intuition was obtained quickly using the simulator as an aid to understanding the general behavior of the developmental system. Artificial evolution of neural networks. Neural networks can be represented using a developmental model. We investigate the use of artificial evolution to select equations and parameters that cause the model to create desired structures. We compare our approach to other work in evolutionary neural networks, and discuss the difficulties involved. Computer graphics modeling. We extend the model to allow cells to sense the presence of a 3D surface model, and then use the multicellular simulator to grow cells on the surface. This database amplification technique enables the creation of cellular textures to represent detailed geometry on a surface (e.g., scales, feathers, thorns). In the process of writing many developmental programs, we have gained some experience in the construction of self-organizing cellular structures. We identify some critical issues (size regulation and scalability), and suggest biologically-plausible strategies for addressing them.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/97156-wtf43