2 citations · 10 across the 12 of their papers we have counts for
14 papers
Shape-Graph Matching Network (SGM-net): Registration for Statistical Shape Analysis
Shenyuan Liang, Mauricio Pamplona Segundo, Sathyanarayanan N. Aakur +2
This paper focuses on the statistical analysis of shapes of data objects called shape graphs, a set of nodes connected by articulated curves with arbitrary shapes. A critical need…
Peak-Persistence Diagrams for Estimating Shapes and Functions from Noisy Data
Woo Min Kim, Sutanoy Dasgupta, Anuj Srivastava
Estimating signals underlying noisy data is a significant problem in statistics and engineering. Numerous estimators are available in the literature, depending on the observation m…
Statistical Shape Analysis of Shape Graphs with Applications to Retinal Blood-Vessel Networks
Aditi Basu Bal, Xiaoyang Guo, Tom Needham +1
This paper provides theoretical and computational developments in statistical shape analysis of shape graphs, and demonstrates them using analysis of complex data from retinal bloo…
Elastic Shape Analysis of Tree-like 3D Objects using Extended SRVF Representation
Guan Wang, Hamid Laga, Anuj Srivastava
How can one analyze detailed 3D biological objects, such as neurons and botanical trees, that exhibit complex geometrical and topological variation? In this paper, we develop a nov…
Rethinking Multidimensional Discriminator Output for Generative Adversarial Networks
Mengyu Dai, Haibin Hang, Anuj Srivastava
The study of multidimensional discriminator (critic) output for Generative Adversarial Networks has been underexplored in the literature. In this paper, we generalize the Wasserste…
Dynamic Shape Modeling to Analyze Modes ofMigration During Cell Motility
Ximu Deng, Rituparna Sarkar, Elisabeth Labruyere +2
This paper develops a generative statistical model for representing, modeling, and comparing the morphological evolution of biological cells undergoing motility. It uses the elasti…
Elastic Shape Analysis of Brain Structures for Predictive Modeling of PTSD
Yuexuan Wu, Suprateek Kundu, Jennifer S. Stevens +2
There is increasing evidence on the importance of brain morphology in predicting and classifying mental disorders. However, the vast majority of current shape approaches rely heavi…
Shape Analysis of Functional Data with Elastic Partial Matching
Darshan Bryner, Anuj Srivastava
Elastic Riemannian metrics have been used successfully in the past for statistical treatments of functional and curve shape data. However, this usage has suffered from an important…
4D Atlas: Statistical Analysis of the Spatiotemporal Variability in Longitudinal 3D Shape Data
Hamid Laga, Marcel Padilla, Ian H. Jermyn +3
We propose a novel framework to learn the spatiotemporal variability in longitudinal 3D shape data sets, which contain observations of objects that evolve and deform over time. Thi…
Data Science for Motion and Time Analysis with Modern Motion Sensor Data
Chiwoo Park, Sang Do Noh, Anuj Srivastava
The motion-and-time analysis has been a popular research topic in operations research, especially for analyzing work performances in manufacturing and service operations. It is reg…
Statistical Shape Analysis of Brain Arterial Networks (BAN)
Xiaoyang Guo, Aditi Basu Bal, Tom Needham +1
Structures of brain arterial networks (BANs) - that are complex arrangements of individual arteries, their branching patterns, and inter-connectivities - play an important role in…
Representations, Metrics and Statistics For Shape Analysis of Elastic Graphs
Xiaoyang Guo, Anuj Srivastava
Past approaches for statistical shape analysis of objects have focused mainly on objects within the same topological classes, e.g., scalar functions, Euclidean curves, or surfaces,…
Intensity Estimation for Poisson Process with Compositional Noise
Glenna Schluck, Wei Wu, Anuj Srivastava
Intensity estimation for Poisson processes is a classical problem and has been extensively studied over the past few decades. Practical observations, however, often contain composi…
A Quotient Space Formulation for Generative Statistical Analysis of Graphical Data
Xiaoyang Guo, Anuj Srivastava, Sudeep Sarkar
Complex analyses involving multiple, dependent random quantities often lead to graphical models - a set of nodes denoting variables of interest, and corresponding edges denoting st…