Search Results for author: Nagesh Adluru

Found 10 papers, 1 papers with code

Accurate Automatic Segmentation of Amygdala Subnuclei and Modeling of Uncertainty via Bayesian Fully Convolutional Neural Network

no code implementations19 Feb 2019 Yilin Liu, Gengyan Zhao, Brendon M. Nacewicz, Nagesh Adluru, Gregory R. Kirk, Peter A Ferrazzano, Martin Styner, Andrew L. Alexander

However, most of the previous deep learning work does not investigate the specific difficulties that exist in segmenting extremely small but important brain regions such as the amygdala and its subregions.

Efficient Relative Attribute Learning using Graph Neural Networks

1 code implementation ECCV 2018 Zihang Meng, Nagesh Adluru, Hyunwoo J. Kim, Glenn Fung, Vikas Singh

A sizable body of work on relative attributes provides compelling evidence that relating pairs of images along a continuum of strength pertaining to a visual attribute yields significant improvements in a wide variety of tasks in vision.

A Geometric Framework for Statistical Analysis of Trajectories With Distinct Temporal Spans

no code implementations ICCV 2017 Rudrasis Chakraborty, Vikas Singh, Nagesh Adluru, Baba C. Vemuri

Finally, by using existing algorithms for recursive Frechet mean and exact principal geodesic analysis on the hypersphere, we present several experiments on synthetic and real (vision and medical) data sets showing how group testing on such diversely sampled longitudinal data is possible by analyzing the reconstructed data in the subspace spanned by the first few PGs.

Riemannian Nonlinear Mixed Effects Models: Analyzing Longitudinal Deformations in Neuroimaging

no code implementations CVPR 2017 Hyunwoo J. Kim, Nagesh Adluru, Heemanshu Suri, Baba C. Vemuri, Sterling C. Johnson, Vikas Singh

Statistical machine learning models that operate on manifold-valued data are being extensively studied in vision, motivated by applications in activity recognition, feature tracking and medical imaging.

Activity Recognition

Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks

no code implementations CVPR 2016 Seong Jae Hwang, Nagesh Adluru, Maxwell D. Collins, Sathya N. Ravi, Barbara B. Bendlin, Sterling C. Johnson, Vikas Singh

There is a great deal of interest in using large scale brain imaging studies to understand how brain connectivity evolves over time for an individual and how it varies over different levels/quantiles of cognitive function.

Latent Variable Graphical Model Selection Using Harmonic Analysis: Applications to the Human Connectome Project (HCP)

no code implementations CVPR 2016 Won Hwa Kim, Hyunwoo J. Kim, Nagesh Adluru, Vikas Singh

A major goal of imaging studies such as the (ongoing) Human Connectome Project (HCP) is to characterize the structural network map of the human brain and identify its associations with covariates such as genotype, risk factors, and so on that correspond to an individual.

Model Selection

A Projection Free Method for Generalized Eigenvalue Problem With a Nonsmooth Regularizer

no code implementations ICCV 2015 Seong Jae Hwang, Maxwell D. Collins, Sathya N. Ravi, Vamsi K. Ithapu, Nagesh Adluru, Sterling C. Johnson, Vikas Singh

Eigenvalue problems are ubiquitous in computer vision, covering a very broad spectrum of applications ranging from estimation problems in multi-view geometry to image segmentation.

Semantic Segmentation Stochastic Optimization

Interpolation on the Manifold of K Component GMMs

no code implementations ICCV 2015 Hyunwoo J. Kim, Nagesh Adluru, Monami Banerjee, Baba C. Vemuri, Vikas Singh

Probability density functions (PDFs) are fundamental "objects" in mathematics with numerous applications in computer vision, machine learning and medical imaging.

Epitome driven 3-D Diffusion Tensor image segmentation: on extracting specific structures

no code implementations NeurIPS 2010 Kamiya Motwani, Nagesh Adluru, Chris Hinrichs, Andrew Alexander, Vikas Singh

Now, given such a representation, the problem reduces to segmenting new brain image with additional constraints that enforce consistency between the segmented foreground and the pre-specified histogram over features.

Semantic Segmentation

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