Farmers, worldwide, are increasingly leveraging data derived from Unmanned Aerial Systems (UAS) imagery to enhance decision-making. This trend reflects a significant rise in both academic and commercial sectors. Furthermore, advancements in UAS and battery technology now enable longer flights and greater coverage, while higher-resolution optical and multispectral cameras provide finer ground sampling distances (GSD). From a regulatory standpoint, the Federal Aviation Administration (FAA) is beginning to accommodate multi-drone operations and Beyond Visual Line of Sight (BVLOS) operations, which can ultimately reduce the cost to collect UAS imagery.

Chaitanya Kulkarni, Jany Chan, Eric Fosler-Lussier, Raghu Machiraju. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

BACKGROUND: Assigning chromatin states genome-wide (e.g. promoters, enhancers, etc.) is commonly performed to improve functional interpretation of these states. However, computational methods to assign chromatin state suffer from the following drawbacks: they typically require data from multiple assays, which may not be practically feasible to obtain, and they depend on peak calling algorithms, which require careful parameterization and often exclude the majority of the genome. To address these drawbacks, we propose a novel learning technique built upon the Self-Organizing Map (SOM), Self-Organizing Map with Variable Neighborhoods (SOM-VN), to learn a set of representative shapes from a single, genome-wide, chromatin accessibility dataset to associate with a chromatin state assignment in which a particular RE is prevalent. These shapes can then be used to assign chromatin state using our workflow. RESULTS: We validate the performance of the SOM-VN workflow on 14 different samples of varying quality, namely one assay each of A549 and GM12878 cell lines and two each of H1 and HeLa cell lines, primary B-cells, and brain, heart, and stomach tissue. We show that SOM-VN learns shapes that are (1) non-random, (2) associated with known chromatin states, (3) generalizable across sets of chromosomes, and (4) associated with magnitude and multimodality. We compare the accuracy of SOM-VN chromatin states against the Clustering Aggregation Tool (CAGT), an unsupervised method that learns chromatin accessibility signal shapes but does not associate these shapes with REs, and we show that overall precision and recall is increased when learning shapes using SOM-VN as compared to CAGT. We further compare enhancer state assignments from SOM-VN in signals above a set threshold to enhancer state assignments from Predicting Enhancers from ATAC-seq Data (PEAS), a deep learning method that assigns enhancer chromatin states to peaks. We show that the precision-recall area under the curve for the assignment of enhancer states is comparable to PEAS. CONCLUSIONS: Our work shows that the SOM-VN workflow can learn relationships between REs and chromatin accessibility signal shape, which is an important step toward the goal of assigning and comparing enhancer state across multiple experiments and phenotypic states.

Data-parallelism has become an established paradigm to train DNNs that fit inside GPU memory on large-scale HPC systems. However, model-parallelism is required to train out-of-core DNNs. In this paper, we deal with emerging requirements brought forward by very large DNNs being trained using high-resolution images common in digital pathology. To address these, we propose, design, and implement GEMS; a GPU-Enabled Memory-Aware Model-Parallelism System. We present several design schemes like GEMS-MAST, GEMS-MASTER, and GEMS-Hybrid that offer excellent speedups over state-of-the-art systems like Mesh-TensorFlow and FlexFlow. Furthermore, we combine model-parallelism and data-parallelism to train a 1000-1ayer ResNet-lk model using 1,024 Volta V100 GPUs with 97.32% scaling-efficiency. For the real-world histopathology whole-slide-image (WSI) of 100,000 x 100,000 pixels, we train custom ResNet-110-v2 on image tiles of size 1024 x 1024 and reduce the training time from seven hours to 28 minutes.

MOTIVATION: Technologies that generate high-throughput omics data are flourishing, creating enormous, publicly available repositories of multi-omics data. As many data repositories continue to grow, there is an urgent need for computational methods that can leverage these data to create comprehensive clusters of patients with a given disease. RESULTS: Our proposed approach creates a patient-to-patient similarity graph for each data type as an intermediate representation of each omics data type and merges the graphs through subspace analysis on a Grassmann manifold. We hypothesize that this approach generates more informative clusters by preserving the complementary information from each level of omics data. We applied our approach to The Cancer Genome Atlas (TCGA) breast cancer dataset and show that by integrating gene expression, microRNA and DNA methylation data, our proposed method can produce clinically useful subtypes of breast cancer. We then investigate the molecular characteristics underlying these subtypes. We discover a highly expressed cluster of genes on chromosome 19p13 that strongly correlates with survival in TCGA breast cancer patients and validate these results in three additional breast cancer datasets. We also compare our approach with previous integrative clustering approaches and obtain comparable or superior results. AVAILABILITY AND IMPLEMENTATION: https://github.com/michaelsharpnack/GrassmannCluster. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Proteomic measurements, which closely reflect phenotypes, provide insights into gene expression regulations and mechanisms underlying altered phenotypes. Further, integration of data on proteome and transcriptome levels can validate gene signatures associated with a phenotype. However, proteomic data is not as abundant as genomic data, and it is thus beneficial to use genomic features to predict protein abundances when matching proteomic samples or measurements within samples are lacking. RESULTS: We evaluate and compare four data-driven models for prediction of proteomic data from mRNA measured in breast and ovarian cancers using the 2017 DREAM Proteogenomics Challenge data. Our results show that Bayesian network, random forests, LASSO, and fuzzy logic approaches can predict protein abundance levels with median ground truth-predicted correlation values between 0.2 and 0.5. However, the most accurately predicted proteins differ considerably between approaches. CONCLUSIONS: In addition to benchmarking aforementioned machine learning approaches for predicting protein levels from transcript levels, we discuss challenges and potential solutions in state-of-the-art proteogenomic analyses.

Neural embeddings are widely used in language modeling and feature generation with superior computational power. Particularly, neural document embedding - converting texts of variable-length to semantic vector representations - has shown to benefit widespread downstream applications, e.g., information retrieval (IR). However, the black-box nature makes it difficult to understand how the semantics are encoded and employed. We propose visual exploration of neural document embedding to gain insights into the underlying embedding space, and promote the utilization in prevalent IR applications. In this study, we take an IR application-driven view, which is further motivated by biomedical IR in healthcare decision-making, and collaborate with domain experts to design and develop a visual analytics system. This system visualizes neural document embeddings as a configurable document map and enables guidance and reasoning; facilitates to explore the neural embedding space and identify salient neural dimensions (semantic features) per task and domain interest; and supports advisable feature selection (semantic analysis) along with instant visual feedback to promote IR performance. We demonstrate the usefulness and effectiveness of this system and present inspiring findings in use cases. This work will help designers/developers of downstream applications gain insights and confidence in neural document embedding, and exploit that to achieve more favorable performance in application domains.

SUMMARY: We developed annoPeak, a web application to annotate, visualize and compare predicted protein-binding regions derived from ChIP-seq/ChIP-exo-seq experiments using human and mouse cells. Users can upload peak regions from multiple experiments onto the annoPeak server to annotate them with biological context, identify associated target genes and categorize binding sites with respect to gene structure. Users can also compare multiple binding profiles intuitively with the help of visualization tools and tables provided by annoPeak. In general, annoPeak will help users identify patterns of genome wide transcription factor binding profiles, assess binding profiles in different biological contexts and generate new hypotheses. AVAILABILITY AND IMPLEMENTATION: The web service is freely accessible through URL: http://ccc-annopeak.osumc.edu/annoPeak . Source code is available at https://github.com/XingTang2014/annoPeak . CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Identification and analysis of recurrent combinatorial patterns of multiple chromatin modifications provide invaluable information for understanding epigenetic regulations. Furthermore, as more data becomes available, it is computationally expensive and unnecessary to study combinatorial patterns of all modifications. METHODS: A novel framework is proposed to investigate recurrent combinatorial patterns of a subset of quantitatively selected chromatin modifications. The framework is based on heirarchical clustering and selects subsets of chromatin modifications that form distinct recurrent patterns at regulatory regions. The identified recurrent combinatorial patterns can be further utilized to discover novel regulatory regions. Data is in the form of genome wide maps of histone acetylations, methylations, and histone variant of human skeletal muscular and B-lymphocyte cells both derived from the ENCODE project. RESULTS: A case study conducted at promoter regions is presented: four out of twelve chromatin modifications were selected, eight different promoter states were identified and the identified patterns of active promoters were further utilized to discover novel promoter regions. Several previously un-annotated promoters were discovered, further investigations confirm their promoter functions. CONCLUSIONS: This framework is approproiately general and could lead to better understanding of epigenetic regulations by discovering previously unknown regulatory regions.

Co-expression analysis has been employed to predict gene function, identify functional modules, and determine tumor subtypes. Previous co-expression analysis was mainly conducted at bulk tissue level. It is unclear whether co-expression analysis at the single-cell level will provide novel insights into transcriptional regulation. Here we developed a computational approach to compare glioblastoma expression profiles at the single-cell level with those obtained from bulk tumors. We found that the co-expressed genes observed in single cells and bulk tumors have little overlap and show distinct characteristics. The co-expressed genes identified in bulk tumors tend to have similar biological functions, and are enriched for intrachromosomal interactions with synchronized promoter activity. In contrast, single-cell co-expressed genes are enriched for known protein-protein interactions, and are regulated through interchromosomal interactions. Moreover, gene members of some protein complexes are co-expressed only at the bulk level, while those of other complexes are co-expressed at both single-cell and bulk levels. Finally, we identified a set of co-expressed genes that can predict the survival of glioblastoma patients. Our study highlights that comparative analyses of single-cell and bulk gene expression profiles enable us to identify functional modules that are regulated at different levels and hold great translational potential.

Although vortex analysis and detection have been extensively investigated in the past, none of the existing techniques are able to provide fully robust and reliable identification results. Local vortex detection methods are popular as they are efficient and easy to implement, and produce binary outputs based on a user-specified, hard threshold. However, vortices are global features, which present challenges for local detectors. On the other hand, global detectors are computationally intensive and require considerable user input. In this work, we propose a consensus-based uncertainty model and introduce spatial proximity to enhance vortex detection results obtained using point-based methods. We use four existing local vortex detectors and convert their outputs into fuzzy possibility values using a sigmoid-based soft-thresholding approach. We apply a majority voting scheme that enables us to identify candidate vortex regions with a higher degree of confidence. Then, we introduce spatial proximity- based analysis to discern the final vortical regions. Thus, by using spatial proximity coupled with fuzzy inputs, we propose a novel uncertainty analysis approach for vortex detection. We use expert's input to better estimate the system parameters and results from two real-world data sets demonstrate the efficacy of our method.

BACKGROUND: Histology images comprise one of the important sources of knowledge for phenotyping studies in systems biology. However, the annotation and analyses of histological data have remained a manual, subjective and relatively low-throughput process. RESULTS: We introduce Graph based Histology Image Explorer (GRAPHIE)-a visual analytics tool to explore, annotate and discover potential relationships in histology image collections within a biologically relevant context. The design of GRAPHIE is guided by domain experts' requirements and well-known InfoVis mantras. By representing each image with informative features and then subsequently visualizing the image collection with a graph, GRAPHIE allows users to effectively explore the image collection. The features were designed to capture localized morphological properties in the given tissue specimen. More importantly, users can perform feature selection in an interactive way to improve the visualization of the image collection and the overall annotation process. Finally, the annotation allows for a better prospective examination of datasets as demonstrated in the users study. Thus, our design of GRAPHIE allows for the users to navigate and explore large collections of histology image datasets. CONCLUSIONS: We demonstrated the usefulness of our visual analytics approach through two case studies. Both of the cases showed efficient annotation and analysis of histology image collection.

Histone modification is an important epigenetic event which plays essential roles in cell differentiation and tissue development. Recent studies show that a unique dimethylation of lysine 4 residue on histone 3 (H3K4me2) distribution pattern around transcription starting sites (TSS) of genes marks tissue specific genes in human CD4 þ T cells and mouse nervous tissue cells. However, existence of this pattern has not been widely tested and its implication remains unclear. In this paper, we study the H3K4me2 distribution patterns across six different cell lines from five major tissue types (including muscular tissue, nervous tissue, non-blood connective tissue, blood, and epithelial tissue) as well as embryonic stem cells. We define a metric ‘tail length’ to quantitatively describe H3K4me2 distribution patterns around the TSS. While confirming the previous observations, we also identified a group of 217 genes with ubiquitous long-tail H3K4me2 patterns in all the tested tissues and the embryonic stem cells (ESC). Further analyses confirmed that these genes are critical for development, and highly interactive with other tissue specific genes as evinced by protein-protein interaction networks, suggesting their critical regulatory functions. Our results suggest that rich information on gene functions and epigenetic events can be revealed using pattern recognition methods.

BACKGROUND: Cancers are highly heterogeneous with different subtypes. These subtypes often possess different genetic variants, present different pathological phenotypes, and most importantly, show various clinical outcomes such as varied prognosis and response to treatment and likelihood for recurrence and metastasis. Recently, integrative genomics (or panomics) approaches are often adopted with the goal of combining multiple types of omics data to identify integrative biomarkers for stratification of patients into groups with different clinical outcomes. RESULTS: In this paper we present a visual analytic system called Interactive Genomics Patient Stratification explorer (iGPSe) which significantly reduces the computing burden for biomedical researchers in the process of exploring complicated integrative genomics data. Our system integrates unsupervised clustering with graph and parallel sets visualization and allows direct comparison of clinical outcomes via survival analysis. Using a breast cancer dataset obtained from the The Cancer Genome Atlas (TCGA) project, we are able to quickly explore different combinations of gene expression (mRNA) and microRNA features and identify potential combined markers for survival prediction. CONCLUSIONS: Visualization plays an important role in the process of stratifying given population patients. Visual tools allowed for the selection of possibly features across various datasets for the given patient population. We essentially made a case for visualization for a very important problem in translational informatics.

Abstract Scatterplot matrices or SPLOMs provide a feasible method of visualizing and representing multi‐dimensional data especially for a small number of dimensions. For very high dimensional data, we introduce a novel technique to summarize a SPLOM, as a clustered matrix of glyphs, or a Glyph SPLOM. Each glyph visually encodes a general measure of dependency strength, distance correlation, and a logical dependency class based on the occupancy of the scatterplot quadrants. We present the Glyph SPLOM as a general alternative to the traditional correlation based heatmap and the scatterplot matrix in two examples: demography data from the World Health Organization (WHO), and gene expression data from developmental biology. By using both, dependency class and strength, the Glyph SPLOM illustrates high dimensional data in more detail than a heatmap but with more summarization than a SPLOM. More importantly, the summarization capabilities of Glyph SPLOM allow for the assertion of “necessity” causal relationships in the data and the reconstruction of interaction networks in various dynamic systems.

Abstract Robust automated vortex detection algorithms are needed to facilitate the exploration of large‐scale turbulent fluid flow simulations. Unfortunately, robust non‐local vortex detection algorithms are computationally intractable for large data sets and local algorithms, while computationally tractable, lack robustness. We argue that the deficiencies inherent to the local definitions occur because of two fundamental issues: the lack of a rigorous definition of a vortex and the fact that a vortex is an intrinsically non‐local phenomenon. As a first step towards addressing this problem, we demonstrate the use of machine learning techniques to enhance the robustness of local vortex detection algorithms. We motivate the presence of an expert‐in‐the‐loop using empirical results based on machine learning techniques. We employ adaptive boosting to combine a suite of widely used, local vortex detection algorithms, which we term weak classifiers, into a robust compound classifier. Fundamentally, the training phase of the algorithm, in which an expert manually labels small, spatially contiguous regions of the data, incorporates non‐local information into the resulting compound classifier. We demonstrate the efficacy of our approach by applying the compound classifier to two data sets obtained from computational fluid dynamical simulations. Our results demonstrate that the compound classifier has a reduced misclassification rate relative to the component classifiers.

This paper presents a novel algorithm to enhance the robustness of vortex core detection that automatically learns to build a strong compound classifier based on a locally weighted combination of weak detectors and the training samples. We use semi-supervised learning with domain expert input to develop strategies for guiding the selective refinement process. This compound detector combines the advantages of each individual local detector. Our main application area is vortex detection in turbulent flows. We demonstrate the efficacy of our approach by applying the compound detector to a variety of fluid data examples.

BACKGROUND AND OBJECTIVE: Biomarkers for subtyping triple negative breast cancer (TNBC) are needed given the absence of responsive therapy and relatively poor prediction of survival. Morphology of cancer tissues is widely used in clinical practice for stratifying cancer patients, while genomic data are highly effective to classify cancer patients into subgroups. Thus integration of both morphological and genomic data is a promising approach in discovering new biomarkers for cancer outcome prediction. Here we propose a workflow for analyzing histopathological images and integrate them with genomic data for discovering biomarkers for TNBC. MATERIALS AND METHODS: We developed an image analysis workflow for extracting a large collection of morphological features and deployed the same on histological images from The Cancer Genome Atlas (TCGA) TNBC samples during the discovery phase (n=44). Strong correlations between salient morphological features and gene expression profiles from the same patients were identified. We then evaluated the same morphological features in predicting survival using a local TNBC cohort (n=143). We further tested the predictive power on patient prognosis of correlated gene clusters using two other public gene expression datasets. RESULTS AND CONCLUSION: Using TCGA data, we identified 48 pairs of significantly correlated morphological features and gene clusters; four morphological features were able to separate the local cohort with significantly different survival outcomes. Gene clusters correlated with these four morphological features further proved to be effective in predicting patient survival using multiple public gene expression datasets. These results suggest the efficacy of our workflow and demonstrate that integrative analysis holds promise for discovering biomarkers of complex diseases.

BACKGROUND: RNA polymerase II (PolII) is essential in gene transcription and ChIP-seq experiments have been used to study PolII binding patterns over the entire genome. However, since PolII enriched regions in the genome can be very long, existing peak finding algorithms for ChIP-seq data are not adequate for identifying such long regions. METHODS: Here we propose an enriched region detection method for ChIP-seq data to identify long enriched regions by combining a signal denoising algorithm with a false discovery rate (FDR) approach. The binned ChIP-seq data for PolII are first processed using a non-local means (NL-means) algorithm for purposes of denoising. Then, a FDR approach is developed to determine the threshold for marking enriched regions in the binned histogram. RESULTS: We first test our method using a public PolII ChIP-seq dataset and compare our results with published results obtained using the published algorithm HPeak. Our results show a high consistency with the published results (80-100%). Then, we apply our proposed method on PolII ChIP-seq data generated in our own study on the effects of hormone on the breast cancer cell line MCF7. The results demonstrate that our method can effectively identify long enriched regions in ChIP-seq datasets. Specifically, pertaining to MCF7 control samples we identified 5,911 segments with length of at least 4 Kbp (maximum 233,000 bp); and in MCF7 treated with E2 samples, we identified 6,200 such segments (maximum 325,000 bp). CONCLUSIONS: We demonstrated the effectiveness of this method in studying binding patterns of PolII in cancer cells which enables further deep analysis in transcription regulation and epigenetics. Our method complements existing peak detection algorithms for ChIP-seq experiments.

These are the proceedings of the IEEE Visualization Conference 2010 (Vis 2010) and the IEEE Information Visualization Conference 2010 (InfoVis 2010) held during October 24 to 29, 2010 in Salt Lake City, Utah, USA.

The six papers in this special issue are extended versions of papers presented at the IEEE/EG International Symposium om Volume Graphics, held in September 2007 in Prague, and the joint event of the IEEE/EG International Symposia on Volume Graphics (VG '08) and Point-Based Graphics (PBG '08), held in August 2008 in Los Angeles.

Abstract Classically, analysis of the time‐varying data acquired during fMRI experiments is done using static activation maps obtained by testing voxels for the presence of significant activity using statistical methods. The models used in these analysis methods have a number of parameters, which profoundly impact the detection of active brain areas. Also, it is hard to study the temporal dependencies and cascading effects of brain activation from these static maps. In this paper, we propose a methodology to visually analyze the time dimension of brain function with a minimum amount of processing, allowing neurologists to verify the correctness of the analysis results, and develop a better understanding of temporal characteristics of the functional behaviour. The system allows studying time‐series data through specific volumes‐of‐interest in the brain‐cortex, the selection of which is guided by a hierarchical clustering algorithm performed in the wavelet domain. We also demonstrate the utility of this tool by presenting results on a real data‐set.

Abstract Neuronal dendrites and their spines affect the connectivity of neural networks, and play a significant role in many neurological conditions. Neuronal function is observed to be closely correlated with the appearance, disappearance and morphology of the spines. Automatic 3‐D reconstruction of neurons from light microscopy images, followed by the identification, classification and visualization of dendritic spines is therefore essential for studying neuronal physiology and biophysical properties. In this paper, we present a method to reconstruct dendrites using a surface representation of the dendrite. The 1‐D skeleton of the dendritic surface is then extracted by a medial geodesic function that is robust and topologically correct. This is followed by a Bayesian identification and classification of the spines. The dendrite and spines are visualized in a manner that displays the spines' types and the inherent uncertainty in identification and classification. We also describe a user study conducted to validate the accuracy of the classification and the efficacy of the visualization.

Abstract At a microscopic resolution, biological structures are composed of cells, red blood corpuscles (RBCs), cytoplasm and other microstructural components. There is a natural pattern in terms of distribution, arrangement and packing density of these components in biological organization. In this work, we propose to use N‐point correlation functions to guide the analysis and exploration process in microscopic datasets. These functions provide useful feature spaces to aid segmentation and visualization tasks. We show3D visualizations of mouse placenta tissue layers and mouse mammary ducts as well as2D segmentation/tracking of clonal populations. Further confidence in our results stems from validation studies that were performed with manual ground‐truth for segmentation.

Developments in optical microscopy imaging have generated large high-resolution data sets that have spurred medical researchers to conduct investigations into mechanisms of disease, including cancer at cellular and subcellular levels. The work reported here demonstrates that a suitable methodology can be conceived that isolates modality-dependent effects from the larger segmentation task and that 3D reconstructions can be cognizant of shapes as evident in the available 2D planar images. In the current realization, a method based on active geodesic contours is first deployed to counter the ambiguity that exists in separating overlapping cells on the image plane. Later, another segmentation effort based on a variant of Voronoi tessellations improves the delineation of the cell boundaries using a Bayesian formulation. In the next stage, the cells are interpolated across the third dimension thereby mitigating the poor structural correlation that exists in that dimension. We deploy our methods on three separate data sets obtained from light, confocal, and phase-contrast microscopy and validate the results appropriately.

Particle deposition in the small bronchial tubes (generations six through twelve) is strongly influenced by the vortex-dominated secondary flows that are induced by axial curvature of the tubes. In this paper, we employ particle destination maps in conjunction with two-dimensional, finite-time Lyapunov exponent maps to illustrate how the trajectories of finite-mass particles are influenced by the presence of vortices. We consider two three-generation bronchial tube models: a planar, asymmetric geometry and a non-planar, asymmetric geometry. Our visualizations demonstrate that these techniques, coupled with judiciously seeded particle trajectories, are effective tools for studying particle/flow structure interactions.

In this paper, we propose a technique for detecting pockets on a surface-of-interest. A sequence of propagating fronts converging to the target surface is used as the basis for inspection. We compute a correspondence function between the initial and the target surface. This leads to a natural definition of the local feature size measured as the evolution distance between mapped points. Surface pockets are then extracted as salient clusters embedded in the feature space. The level-set initialization also determines the scale-space of the extracted pockets. Results are presented on a case-study in which the focus is to chronicle the phenotyping differences in genetically modified mouse placenta. Our results are validated based on manually verified ground-truth.

In this paper, we present a framework for simulating light transport in three-dimensional tissue with inhomogeneous scattering properties. Our approach employs a computational model to simulate light scattering in tissue through the finite element solution of the diffusion equation. Although our model handles both visible and nonvisible wavelengths, we especially focus on the interaction of near infrared (NIR) light with tissue. Since most human tissue is permeable to NIR light, tools to noninvasively image tumors, blood vasculature, and monitor blood oxygenation levels are being constructed. We apply this model to a numerical phantom to visually reproduce the images generated by these real-world tools. Therefore, in addition to enabling inverse design of detector instruments, our computational tools produce physically-accurate visualizations of subsurface structures.

In this article, we present trajectory representation algorithms for tangible features found in temporally varying scientific datasets. Rather than modeling the features as points, we take attributes like shape and extent of the feature into account. Our contention is that these attributes play an important role in understanding the temporal evolution and interactions among features. The proposed representation scheme is based on motion and shape parameters including linear velocity, angular velocity, etc. We use these parameters to segment the trajectory instead of relying on the geometry of the trajectory. We evaluate our algorithms on real datasets originating from different domains. We show the accuracy of the motion and shape parameter estimation by reconstructing the trajectories with high accuracy. Finally, we present performance and scalability results.

In order to understand complex vortical flows in large data sets, we must be able to detect and visualize vortices in an automated fashion. In this paper, we present a feature-based vortex detection and visualization technique that is appropriate for large computational fluid dynamics data sets computed on unstructured meshes. In particular, we focus on the application of this technique to visualization of the flow over a serrated wing and the flow field around a spinning missile with dithering canards. We have developed a core line extraction technique based on the observation that vortex cores coincide with local extrema in certain scalar fields. We also have developed a novel technique to handle complex vortex topology that is based on k-means clustering. These techniques facilitate visualization of vortices in simulation data that may not be optimally resolved or sampled. Results are included that highlight the strengths and weaknesses of our approach. We conclude by describing how our approach can be improved to enhance robustness and expand its range of applicability.

We present a technique to extract motion parameters of a human figure from a single video stream. Our goal is to prototype motion synthesis rapidly for game design and animation applications. For example, our approach is especially useful in situations where motion capture systems are restricted in their usefulness given the various required instrumentation. Similarly, our approach can be used to synthesize motion from archival footage. By extracting the silhouette of the foreground figure and using a model-based approach, the problem is re-formulated as a local, optimized search of the pose space. The pose space consists of 6 rigid body transformation parameters plus the internal joint angles of the figure. The silhouette of the figure from the captured video is compared against the silhouette of a synthetic figure using a pixel-by-pixel, distance-based cost function to evaluate goodness-of-fit. For a single video stream, this is not without problems. Occlusion and ambiguities arising from the use of a single view often cause spurious reconstruction of the captured motion. By using temporal coherence, physical constraints, and knowledge of the anatomy, a viable pose sequence can be reconstructed for many live-action sequences.

We present a technique to generate an illumination subspace for arbitrary 3D faces based on the statistics of measured illuminations under variable lighting conditions from many subjects. A bilinear model based on the higher-order singular value decomposition is used to create a compact illumination subspace given arbitrary shape parameters from a parametric 3D face model. Using a fitting procedure based on minimizing the distance of the input image to the dynamically changing illumination subspace, we reconstruct a shape-specific illumination subspace from a single photograph. We use the reconstructed illumination subspace in various face recognition experiments with variable lighting conditions and obtain accuracies which are very competitive with previous methods that require specific training sessions or multiple images of the subject.

In this application paper we explore techniques to classify anomalous structures (defects) in data generated from Molecular Dynamics (MD) simulations of Silicon (Si) atom systems. These systems are studied to understand the processes behind the formation of various defects as they have a profound impact on the electrical and mechanical properties of Silicon. In our prior work [12, 13, 14] we presented techniques for defect detection. Here, we present a two-step dynamic classifier to classify the defects. The first step uses up to third-order shape moments to provide a smaller set of candidate defect classes. The second step assigns the correct class to the defect structure by considering the actual spatial positions of the individual atoms. The dynamic classifier is robust and scalable in the size of the atom systems. Each phase is immune to noise, which is characterized after a study of the simulation data. We also validate the proposed solutions by using a physical model and properties of lattices. We demonstrate the efficacy and correctness of our approach on several large datasets. Our approach is able to recognize previously seen defects and also identify new defects in real time.

Domain decomposition is a popular technique for solving large computational problems that require data to be divided into smaller sub-domains. The exact manner of decomposition depends on the computational needs of the algorithm and often introduces irregular boundaries. Each subdomain forms a block of a larger grid and can be solved or rendered separately by different processing nodes. Rendering of each sub-domain can result in images which are then composited in a back-to-front or front-to-back manner. This scenario is useful when visualization is used concurrently with the simulation. However, the irregularity of boundaries may prohibit the correct image composition due to a visibility anomaly between the sub-domains. In this paper, we present an algorithm based on object-space partitioning to resolve this problem. To accelerate the partitioning process, two techniques are introduced. First, an image-space partition representation is employed for fast assignment of data points to correct partitions. Secondly, a k-d tree is used to subdivide the view-space adaptively according to the complexity of the surface. This view-space partition provides a trade-off between performance and accuracy of the rendered image. Large gains in performance can be achieved with only small losses of accuracy. Two examples of curvilinear grids of different complexity are used to demonstrate the effectiveness of this scheme. Copyright © 2005 John Wiley & Sons, Ltd.

High-fidelity wavelet transforms can facilitate visualization and analysis of large scientific data sets. However, it is important that salient characteristics of the original features be preserved under the transformation. We present a set of filter design axioms in the spatial domain which ensure that certain feature characteristics are preserved from scale to scale and that the resulting filters correspond to wavelet transforms admitting in-place implementation. We demonstrate how the axioms can be used to design linear feature-preserving filters that are optimal in the sense that they are closest in L2 to the ideal low pass filter. We are particularly interested in linear wavelet transforms for large data sets generated by computational fluid dynamics simulations. Our effort is different from classical filter design approaches which focus solely on performance in the frequency domain. Results are included that demonstrate the feature-preservation characteristics of our filters.

We present a new level set method for reconstructing interfaces from point aggregations. Although level-set-based methods are advantageous because they can handle complicated topologies and noisy data, most tend to smooth the inherent roughness of the original data. Our objective is to enhance the quality of a reconstructed surface by preserving certain roughness-related characteristics of the original dataset. Our formulation employs the total variation of the surface as a roughness measure. The algorithm consists of two steps: a roughness-capturing flow and a roughness-preserving flow. The roughness capturing step attempts to construct a surface for which the original roughness is captured - distance flow is well suited for roughness capturing. Surface reconstruction is enhanced by using a total variation preserving (TVP) scheme for the roughness-preserving flow. The shock filter formulation of Osher and Rudin is exploited to achieve this goal. In practice, we have found that better results arc obtained by balancing the TVP term with a smoothing term based on curvature. The algorithm is applied to both fractal surface growth simulations and scanned data sets to demonstrate the efficacy of our approach.

We explore techniques to detect and visualize features in data from molecular dynamics (MD) simulations. Although the techniques proposed are general, we focus on silicon (Si) atomic systems. The first set of methods use 3D location of atoms. Defects are detected and categorized using local operators and statistical modeling. Our second set of exploratory techniques employ electron density data. This data is visualized to glean the defects. We describe techniques to automatically detect the salient isovalues for isosurface extraction and designing transfer functions. We compare and contrast the results obtained from both sources of data. Essentially, we find that the methods of defect (feature) detection are at least as robust as those based on the exploration of electron density for Si systems.

Numerical simulation is replacing experimentation as a means to gain insight into complex physical phenomena. Analyzing the data produced by such simulations is extremely challenging, given the enormous sizes of the datasets involved. In order to make efficient progress, analyzing such data must advance from current techniques that only visualize static images of the data, to novel techniques that can mine, track, and visualize the important features in the data. In this paper, we present our research on a unified framework that addresses this critical challenge in two science domains: computational fluid dynamics and molecular dynamics. We offer a systematic approach to detect the significant features in both domains, characterize and track them, and formulate hypotheses with regard to their complex evolution. Our framework includes two paradigms for feature mining, and the choice of one over the other, for a given application, can be determined based on local or global influence of relevant features in the data.

In this paper, we present a novel modeling method for synthesizing rough surfaces using discrete surface growth models. We employ a two-pass method. Initial point cluster data is generated using discrete simulation models derived from physical surface growth and evolution. Then, a final surface is reconstructed from the point clusters by a level set method. Since many rough surfaces in nature are formed by deposition and diffusion processes, discrete models simulating these processes can reasonably reproduce natural rough surfaces. These discrete models are easy to implement and efficient enough for interactive control. After generating initial data set from the application of a discrete model, a level set method is used to obtain implicit surface representations. This approach allows us to easily handle complex surface topologies. We demonstrate that our approach allows a flexible and general way to create various rough surfaces.

In this paper, we present a verification algorithm for swirling features in flow fields, based on the geometry of streamlines. The features of interest in this case are vortices. Without a formal definition, existing detection algorithms lack the ability to accurately identify these features, and the current method for verifying the accuracy of their results is by human visual inspection. Our verification algorithm addresses this issue by automating the visual inspection process. It is based on identifying the swirling streamlines that surround the candidate vortex cores. We apply our algorithm to both numerically simulated and procedurally generated datasets to illustrate the efficacy of our approach.

Presents a method to estimate illumination dependent properties in image synthesis prior to rendering. A preprocessing step is described in which a linear image basis is developed and a lighting-independent formulation defined. A reflection function, similar to hemispherical reflectance, approximates normal Lambertian shading. Intensity errors resulting from this approximation are reduced by use of a polynomial gamma correction function and scaling to a normalized display range. This produces images that are similar to normal Lambertian shading without employing the maximum (max) function. For a single object view, images can then be expressed in a linear form so that lighting direction can be factored out. During normal rendering, image quantities for arbitrary light directions can be found without rendering. This method is demonstrated for estimating image intensity and level-of-detail error prior to rendering an object.

Volume graphics has not been accepted for widespread use. One of the inhibiting reasons is the lack of general methods for data-analysis and simple interfaces for data exploration. An error-and-trial iterative procedure is often used to select a desirable transfer function or mine the dataset for salient iso-values. New semi-automatic methods that are also data-centric have shown much promise. However, general and robust methods are still needed for data-exploration and analysis. In this paper, we propose general model-independent statistical methods based on central moments of data. Using these techniques we show how salient iso-surfaces at material boundaries can be determined. We provide examples from the medical and computational domain to demonstrate the effectiveness of our methods.

The work presents a method to enable matching of level-of-detail (LOD) models to image-plane resolution over large variations in viewing distances often present in exterior images. A relationship is developed between image sampling rate, viewing distance, object projection, and expected image error due to LOD approximations. This is employed in an error metric to compute error profiles for LOD models. Multirate filtering in the frequency space of a reference object image is utilized to approximate multiple distant views over a range of orientations. An importance sampling method is described to better characterize perspective projection over view distance. A contrast sensitivity function (CSF) is employed to approximate the response of the vision system. Examples are presented for multiresolution spheres and a terrain height field feature. Future directions for extending this method are described.

Often, images or datasets have to be compared to facilitate choices of visualization and simulation parameters respectively. Common comparison techniques include side-by-side viewing and juxtaposition, in order to facilitate visual verification of verisimilitude. We propose quantitative techniques which accentuate differences in images and datasets. The comparison is enabled through a collection of partial metrics which, essentially, measure the lack of correlation between the datasets or images being compared. That is, they attempt to expose and measure the extent of the inherent structures in the difference between images or datasets. Besides yielding numerical attributes, the metrics also produce images which can visually highlight differences. Our metrics are simple to compute and operate in the spatial domain. We demonstrate the effectiveness of our metrics through examples for comparing images and datasets.

Numerical simulation of physical phenomena is an accepted way of scientific inquiry. However, the field is still evolving, with a profusion of new solution and grid generation techniques being continuously proposed. Concurrent and retrospective visualization are being used to validate the results. There is a need for representation schemes which allow access of structures in an increasing order of smoothness. We describe our methods on datasets obtained from curvilinear grids. Our target application required visualization of a computational simulation performed on a very remote supercomputer. Since no grid adaptation was performed, it was not deemed necessary to simplify or compress the grid. Inherent to the identification of significant structures is determining the location of the scale coherent structures and assigning saliency values to them. Scale coherent structures are obtained as a result of combining the coefficients of a wavelet transform across scales. The result of this operation is a correlation mask that delineates regions containing significant structures. A spatial subdivision is used to delineate regions of interest. The mask values in these subdivided regions are used as a measure of information content. Later, another wavelet transform is conducted within each subdivided region and the coefficients are sorted based on a perceptual function with bandpass characteristics. This allows for ranking of structures based on the order of significance, giving rise to an adaptive and embedded representation scheme. We demonstrate our methods on two datasets from computational field simulations. We show how our methods allow the ranked access of significant structures. We also compare our adaptive representation scheme with a fixed blocksize scheme.

The task of reconstructing the derivative of a discrete function is essential for its shading and rendering as well as being widely used in image processing and analysis. We survey the possible methods for normal estimation in volume rendering and divide them into two classes based on the delivered numerical accuracy. The three members of the first class determine the normal in two steps by employing both interpolation and derivative filters. Among these is a new method which has never been realized. The members of the first class are all equally accurate. The second class has only one member and employs a continuous derivative filter obtained through the analytic derivation of an interpolation filter. We use the new method to analytically compare the accuracy of the first class with that of the second. As a result of our analysis we show that even inexpensive schemes can in fact be more accurate than high order methods. We describe the theoretical computational cost of applying the schemes in a volume rendering application and provide guidelines for helping one choose a scheme for estimating derivatives. In particular we find that the new method can be very inexpensive and can compete with the normal estimations which pre-shade and pre-classify the volume (M. Levoy, 1988).

The paper addresses multiresolutional representation of datasets arising from a computational field simulation. The approach determines the regions of interest, breaks the volume into variable size blocks to localize the information, and then codes each block using a wavelet transform. The blocks are then ranked by visual information content so that the most informative wavelet coefficients can be embedded in a bit stream for progressive transmission or access. The technique is demonstrated on a widely-used computational field simulation dataset.

It is often the case that images generated by image synthesis algorithms are judged by visual examination. The user resorts to an iterative refinement process of inspection and rendering until a satisfactory image is obtained. In this paper we propose quantitative metrics to compare images that arise from an image synthesis algorithm. The intent is to be able to guide the refinement process inherent in image synthesis. The Mean‐Square‐Error (MSE) has been traditionally employed to guide this process. However, it is not a viable metric for image synthesis control. We propose the use of a wavelet based perceptual metric which incorporates the frequency response of the Human Visual System. A useful aspect of the wavelet based metric is its ability to selectively measure the changes to structures of different sizes and scales in specific locations. Also, by resorting to the use of wavelets of various degrees of regularity, one can seek different levels of smoothness in an image. It is rare that such level of control can be obtained from a metric other than a wavelet based metric. We show the usefulness of our metric by examining its effectiveness in providing insights for common operations of an image synthesis algorithm (e.g., blurring). We also provide some examples of its use in rendering algorithms frequently used in graphics.

We describe a new method for analyzing, classifying, and evaluating filters that can be applied to interpolation filters as well as to arbitrary derivative filters of any order. Our analysis is based on the Taylor series expansion of the convolution sum. Our analysis shows the need and derives the method for the normalization of derivative filter weights. Under certain minimal restrictions of the underlying function, we are able to compute tight absolute error bounds of the reconstruction process. We demonstrate the utilization of our methods to the analysis of the class of cubic BC-spline filters. As our technique is not restricted to interpolation filters, we are able to show that the Catmull-Rom spline filter and its derivative are the most accurate reconstruction and derivative filters, respectively, among the class of BC-spline filters. We also present a new derivative filter which features better spatial accuracy than any derivative BC-spline filter, and is optimal within our framework. We conclude by demonstrating the use of these optimal filters for accurate interpolation and gradient estimation in volume rendering.

Reconstruction is prerequisite whenever a discrete signal needs to be resampled as a result of transformations such as texture mapping, image manipulation, volume slicing, and rendering. We present a new method for the characterization and measurement of reconstruction error in the spatial domain. Our method uses the Classical Shannon's Sampling Theorem as a basis to develop error bounds. We use this formulation to provide, for the first time, an efficient way to guarantee an error bound at every point by varying the size of the reconstruction filter. We go further to support position-adaptive reconstruction and data-adaptive reconstruction which adjusts the filter size to the location of the reconstruction point and to the data values in its vicinity. We demonstrate the effectiveness of our methods with 1D signals, 2D signals (images), and 3D signals (volumes).

Rendering volumes represented as a 3D grid of voxels requires an overwhelming amount of processing power In this paper we investigate eficient techniques for rendering semi-transparent volumes on vector and parallel processors.Parallelism inherent in a regular grid is obtained by decomposing the volume into geometric primitives called beams, slices and slabs of voxels.By using the adjacency properties of voxels in beams and slices, eficient incremental transformation schemes are developed.The slab decomposition of the volume allows the implementation of an eficient parallel feed-forward renderer which includes the splatting technique for image reconstruction and a back-to-front method for creating images.We report the implementation of this feed-forward volume renderer on a hierarchical shared memory machine with individual pipelined processors.