TY - JOUR
T1 - A computational statistics approach for estimating the spatial range of morphogen gradients
JF - Development
Y1 - 2011
A1 - Kanodia, Jitendra S.
A1 - Kim, Yoosik
A1 - Tomer, Raju
A1 - Zia Khan
A1 - Chung, Kwanghun
A1 - Storey, John D.
A1 - Lu, Hang
A1 - Keller, Philipp J.
A1 - Shvartsman, Stanislav Y.
KW - Computational Biology
KW - Confidence interval
KW - Dorsal gradient
KW - Drosophila embryo
KW - Morphogen gradient
KW - Statistics
AB - A crucial issue in studies of morphogen gradients relates to their range: the distance over which they can act as direct regulators of cell signaling, gene expression and cell differentiation. To address this, we present a straightforward statistical framework that can be used in multiple developmental systems. We illustrate the developed approach by providing a point estimate and confidence interval for the spatial range of the graded distribution of nuclear Dorsal, a transcription factor that controls the dorsoventral pattern of the Drosophila embryo.
VL - 138
SN - 0950-1991, 1477-9129
UR - http://dev.biologists.org/content/138/22/4867
CP - 22
J1 - Development
ER -
TY - CONF
T1 - Privacy settings from contextual attributes: A case study using Google Buzz
Y1 - 2011
A1 - Mashima, D.
A1 - Elaine Shi
A1 - Chow, R.
A1 - Sarkar, P.
A1 - Li,C.
A1 - Song,D.
KW - contextual attributes
KW - data privacy
KW - Google Buzz
KW - privacy settings
KW - social networking (online)
KW - social networks
KW - Statistics
AB - Social networks provide users with privacy settings to control what information is shared with connections and other users. In this paper, we analyze factors influencing changes in privacy-related settings in the Google Buzz social network. Specifically, we show statistics on contextual data related to privacy settings that are derived from crawled datasets and analyze the characteristics of users who changed their privacy settings. We also investigate potential neighboring effects among such users.
ER -
TY - JOUR
T1 - Online Empirical Evaluation of Tracking Algorithms
JF - IEEE Transactions on Pattern Analysis and Machine Intelligence
Y1 - 2010
A1 - Wu,Hao
A1 - Sankaranarayanan,A. C
A1 - Chellapa, Rama
KW - Back
KW - Biomedical imaging
KW - Computer vision
KW - Filtering
KW - formal model validation techniques
KW - formal verification
KW - ground truth
KW - Kanade Lucas Tomasi feature tracker
KW - Karhunen-Loeve transforms
KW - lighting
KW - Markov processes
KW - mean shift tracker
KW - model validation.
KW - online empirical evaluation
KW - particle filtering (numerical methods)
KW - Particle filters
KW - Particle tracking
KW - performance evaluation
KW - receiver operating characteristic curves
KW - Robustness
KW - SNR
KW - Statistics
KW - Surveillance
KW - time reversed Markov chain
KW - tracking
KW - tracking algorithms
KW - visual tracking
AB - Evaluation of tracking algorithms in the absence of ground truth is a challenging problem. There exist a variety of approaches for this problem, ranging from formal model validation techniques to heuristics that look for mismatches between track properties and the observed data. However, few of these methods scale up to the task of visual tracking, where the models are usually nonlinear and complex and typically lie in a high-dimensional space. Further, scenarios that cause track failures and/or poor tracking performance are also quite diverse for the visual tracking problem. In this paper, we propose an online performance evaluation strategy for tracking systems based on particle filters using a time-reversed Markov chain. The key intuition of our proposed methodology relies on the time-reversible nature of physical motion exhibited by most objects, which in turn should be possessed by a good tracker. In the presence of tracking failures due to occlusion, low SNR, or modeling errors, this reversible nature of the tracker is violated. We use this property for detection of track failures. To evaluate the performance of the tracker at time instant t, we use the posterior of the tracking algorithm to initialize a time-reversed Markov chain. We compute the posterior density of track parameters at the starting time t = 0 by filtering back in time to the initial time instant. The distance between the posterior density of the time-reversed chain (at t = 0) and the prior density used to initialize the tracking algorithm forms the decision statistic for evaluation. It is observed that when the data are generated by the underlying models, the decision statistic takes a low value. We provide a thorough experimental analysis of the evaluation methodology. Specifically, we demonstrate the effectiveness of our approach for tackling common challenges such as occlusion, pose, and illumination changes and provide the Receiver Operating Characteristic (ROC) curves. Finally, we also s how the applicability of the core ideas of the paper to other tracking algorithms such as the Kanade-Lucas-Tomasi (KLT) feature tracker and the mean-shift tracker.
VL - 32
SN - 0162-8828
CP - 8
M3 - 10.1109/TPAMI.2009.135
ER -
TY - CONF
T1 - Combining powerful local and global statistics for texture description
T2 - IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009
Y1 - 2009
A1 - Yong Xu
A1 - Si-Bin Huang
A1 - Hui Ji
A1 - Fermüller, Cornelia
KW - Computer science
KW - discretized measurements
KW - fractal geometry
KW - Fractals
KW - geometric transformations
KW - global statistics
KW - Histograms
KW - illumination transformations
KW - image classification
KW - image resolution
KW - Image texture
KW - lighting
KW - local measurements SIFT features
KW - local statistics
KW - MATHEMATICS
KW - multifractal spectrum
KW - multiscale representation
KW - Power engineering and energy
KW - Power engineering computing
KW - Robustness
KW - Solids
KW - Statistics
KW - texture description
KW - UMD high-resolution dataset
KW - wavelet frame system
KW - Wavelet transforms
AB - A texture descriptor is proposed, which combines local highly discriminative features with the global statistics of fractal geometry to achieve high descriptive power, but also invariance to geometric and illumination transformations. As local measurements SIFT features are estimated densely at multiple window sizes and discretized. On each of the discretized measurements the fractal dimension is computed to obtain the so-called multifractal spectrum, which is invariant to geometric transformations and illumination changes. Finally to achieve robustness to scale changes, a multi-scale representation of the multifractal spectrum is developed using a framelet system, that is, a redundant tight wavelet frame system. Experiments on classification demonstrate that the descriptor outperforms existing methods on the UIUC as well as the UMD high-resolution dataset.
JA - IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009
PB - IEEE
SN - 978-1-4244-3992-8
M3 - 10.1109/CVPR.2009.5206741
ER -
TY - JOUR
T1 - Integrating Statistics and Visualization for Exploratory Power: From Long-Term Case Studies to Design Guidelines
JF - IEEE Computer Graphics and Applications
Y1 - 2009
A1 - Perer,A.
A1 - Shneiderman, Ben
KW - case studies
KW - Control systems
KW - Data analysis
KW - data mining
KW - data visualisation
KW - Data visualization
KW - data-mining
KW - design guidelines
KW - Employment
KW - exploration
KW - Filters
KW - Guidelines
KW - Information Visualization
KW - insights
KW - laboratory-based controlled experiments
KW - Performance analysis
KW - social network analysis
KW - Social network services
KW - social networking (online)
KW - social networks
KW - SocialAction
KW - statistical analysis
KW - Statistics
KW - visual analytics
KW - visual-analytics systems
KW - Visualization
AB - Evaluating visual-analytics systems is challenging because laboratory-based controlled experiments might not effectively represent analytical tasks. One such system, Social Action, integrates statistics and visualization in an interactive exploratory tool for social network analysis. This article describes results from long-term case studies with domain experts and extends established design goals for information visualization.
VL - 29
SN - 0272-1716
CP - 3
M3 - 10.1109/MCG.2009.44
ER -
TY - CONF
T1 - Integrating statistics and visualization: case studies of gaining clarity during exploratory data analysis
T2 - Proceedings of the twenty-sixth annual SIGCHI conference on Human factors in computing systems
Y1 - 2008
A1 - Perer,Adam
A1 - Shneiderman, Ben
KW - case studies
KW - Evaluation
KW - exploratory data analysis
KW - Information Visualization
KW - social networks
KW - Statistics
AB - Although both statistical methods and visualizations have been used by network analysts, exploratory data analysis remains a challenge. We propose that a tight integration of these technologies in an interactive exploratory tool could dramatically speed insight development. To test the power of this integrated approach, we created a novel social network analysis tool, SocialAction, and conducted four long-term case studies with domain experts, each working on unique data sets with unique problems. The structured replicated case studies show that the integrated approach in SocialAction led to significant discoveries by a political analyst, a bibliometrician, a healthcare consultant, and a counter-terrorism researcher. Our contributions demonstrate that the tight integration of statistics and visualizations improves exploratory data analysis, and that our evaluation methodology for long-term case studies captures the research strategies of data analysts.
JA - Proceedings of the twenty-sixth annual SIGCHI conference on Human factors in computing systems
T3 - CHI '08
PB - ACM
CY - New York, NY, USA
SN - 978-1-60558-011-1
UR - http://doi.acm.org/10.1145/1357054.1357101
M3 - 10.1145/1357054.1357101
ER -
TY - CHAP
T1 - Computer Vision, Statistics in
T2 - Encyclopedia of Statistical SciencesEncyclopedia of Statistical Sciences
Y1 - 2006
A1 - Chellapa, Rama
A1 - Chowdhury,Amit K. Roy
KW - Computer vision
KW - sampling
KW - Statistics
KW - structure from motion
KW - tracking
AB - Computer Vision (CV) broadly refers to the discipline where extraction of useful 2D and/or 3D information from one or more images is of interest. Useful information could consist of features such as edges, lines, curves and textures, or information about depth, motion, object descriptions, etc. CV problems are usually ill-posed inverse problems. Since the image data is usually obtained from sensors such as video cameras, infrared, radar, etc., the information extraction processes often have to deal with data that is corrupted by noise from the sensors and the environment. Statistics can help in obtaining robust and accurate solutions to these inverse problems by modeling the noise processes. We present here a broad overview of the applications of statistics to different computer vision problems and explain in detail two particular applications, tracking and motion analysis, where statistical approaches have been used very successfully.
JA - Encyclopedia of Statistical SciencesEncyclopedia of Statistical Sciences
PB - John Wiley & Sons, Inc.
SN - 9780471667193
UR - http://onlinelibrary.wiley.com/doi/10.1002/0471667196.ess3129/abstract
ER -
TY - JOUR
T1 - Random doping-induced fluctuations of subthreshold characteristics in MOSFET devices
JF - Solid-State Electronics
Y1 - 2003
A1 - Andrei,Petru
A1 - Mayergoyz, Issak D
KW - Fluctuations
KW - Mismatch
KW - MOSFET
KW - Sensitivity analysis
KW - Statistics
KW - Submicron devices
AB - The random doping-induced fluctuations of subthreshold characteristics in MOSFET devices are analyzed. A technique for the computations of sensitivity coefficients and variances of subthreshold parameters is presented and applied to the computation of fluctuations of subthreshold current and gate-voltage swing. This technique is based on the linearization of transport equations with respect to the fluctuating quantities. It is computationally much more efficient than purely “statistical” methods (Monte-Carlo methods) that are based on the simulations of a large number of devices with different doping realizations. The numerical implementation of this technique is discussed and numerous computational results are presented.
VL - 47
SN - 0038-1101
UR - http://www.sciencedirect.com/science/article/pii/S0038110103002363
CP - 11
M3 - 10.1016/S0038-1101(03)00236-3
ER -
TY - CONF
T1 - Extending understanding of federal statistics in tables
T2 - Proceedings of the 2000 annual national conference on Digital government research
Y1 - 2000
A1 - Marchionini,Gary
A1 - Hert,Carol
A1 - Liddy,Liz
A1 - Shneiderman, Ben
KW - data exploration
KW - dynamic queries
KW - Statistics
KW - tablular data
KW - User interfaces
AB - This paper describes progress toward improving user interfaces for US Federal government statistics that are presented in tables. Based on studies of user behaviors and needs related to statistical tables, we describe interfaces to assist diverse users with a range of statistical literacy to explore, find, understand, and use US Federal government statistics.
JA - Proceedings of the 2000 annual national conference on Digital government research
T3 - dg.o '00
PB - Digital Government Society of North America
UR - http://dl.acm.org/citation.cfm?id=1123075.1123079
ER -
TY - JOUR
T1 - The Ouchi illusion as an artifact of biased flow estimation
JF - Vision Research
Y1 - 2000
A1 - Fermüller, Cornelia
A1 - Pless,Robert
A1 - Aloimonos, J.
KW - Bias
KW - MOTION
KW - optical flow
KW - Plaid
KW - Statistics
AB - A pattern by Ouchi has the surprising property that small motions can cause illusory relative motion between the inset and background regions. The effect can be attained with small retinal motions or a slight jiggling of the paper and is robust over large changes in the patterns, frequencies and boundary shapes. In this paper, we explain that the cause of the illusion lies in the statistical difficulty of integrating local one-dimensional motion signals into two-dimensional image velocity measurements. The estimation of image velocity generally is biased, and for the particular spatial gradient distributions of the Ouchi pattern the bias is highly pronounced, giving rise to a large difference in the velocity estimates in the two regions. The computational model introduced to describe the statistical estimation of image velocity also accounts for the findings of psychophysical studies with variations of the Ouchi pattern and for various findings on the perception of moving plaids. The insight gained from this computational study challenges the current models used to explain biological vision systems and to construct robotic vision systems. Considering the statistical difficulties in image velocity estimation in conjunction with the problem of discontinuity detection in motion fields suggests that theoretically the process of optical flow computations should not be carried out in isolation but in conjunction with the higher level processes of 3D motion estimation, segmentation and shape computation.
VL - 40
SN - 0042-6989
UR - http://www.sciencedirect.com/science/article/pii/S0042698999001625
CP - 1
M3 - 10.1016/S0042-6989(99)00162-5
ER -
TY - CONF
T1 - The statistics of optical flow: implications for the process of correspondence in vision
T2 - 15th International Conference on Pattern Recognition, 2000. Proceedings
Y1 - 2000
A1 - Fermüller, Cornelia
A1 - Aloimonos, J.
KW - Bias
KW - Computer vision
KW - correlation
KW - correlation methods
KW - energy-based method
KW - flow estimation
KW - Frequency estimation
KW - gradient method
KW - gradient methods
KW - Image analysis
KW - Image motion analysis
KW - Image sequences
KW - least squares
KW - least squares approximations
KW - Motion estimation
KW - Nonlinear optics
KW - Optical feedback
KW - optical flow
KW - Optical harmonic generation
KW - Optical noise
KW - Statistics
KW - Visual perception
AB - This paper studies the three major categories of flow estimation methods: gradient-based, energy-based, and correlation methods; it analyzes different ways of compounding 1D motion estimates (image gradients, spatio-temporal frequency triplets, local correlation estimates) into 2D velocity estimates, including linear and nonlinear methods. Correcting for the bias would require knowledge of the noise parameters. In many situations, however, these are difficult to estimate accurately, as they change with the dynamic imagery in unpredictable and complex ways. Thus, the bias really is a problem inherent to optical flow estimation. We argue that the bias is also integral to the human visual system. It is the cause of the illusory perception of motion in the Ouchi pattern and also explains various psychophysical studies of the perception of moving plaids. Finally, the implication of the analysis is that flow or correspondence can be estimated very accurately only when feedback is utilized
JA - 15th International Conference on Pattern Recognition, 2000. Proceedings
PB - IEEE
VL - 1
SN - 0-7695-0750-6
M3 - 10.1109/ICPR.2000.905288
ER -