33 citations · 93 across the 16 of their papers we have counts for
7 papers · 1 filter
PI-Net: A Deep Learning Approach to Extract Topological Persistence Images
Anirudh Som, Hongjun Choi, Karthikeyan Natesan Ramamurthy +2
Topological features such as persistence diagrams and their functional approximations like persistence images (PIs) have been showing substantial promise for machine learning and c…
Counting and Segmenting Sorghum Heads
Min-hwan Oh, Peder Olsen, Karthikeyan Natesan Ramamurthy
Phenotyping is the process of measuring an organism's observable traits. Manual phenotyping of crops is a labor-intensive, time-consuming, costly, and error prone process. Accurate…
Crowd Counting with Decomposed Uncertainty
Min-hwan Oh, Peder A. Olsen, Karthikeyan Natesan Ramamurthy
Research in neural networks in the field of computer vision has achieved remarkable accuracy for point estimation. However, the uncertainty in the estimation is rarely addressed. U…
Perturbation Robust Representations of Topological Persistence Diagrams
Anirudh Som, Kowshik Thopalli, Karthikeyan Natesan Ramamurthy +3
Topological methods for data analysis present opportunities for enforcing certain invariances of broad interest in computer vision, including view-point in activity analysis, artic…
Distributed Bundle Adjustment
Karthikeyan Natesan Ramamurthy, Chung-Ching Lin, Aleksandr Aravkin +2
Most methods for Bundle Adjustment (BA) in computer vision are either centralized or operate incrementally. This leads to poor scaling and affects the quality of solution as the nu…
Kernel Sparse Models for Automated Tumor Segmentation
Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Deepta Rajan +3
In this paper, we propose sparse coding-based approaches for segmentation of tumor regions from MR images. Sparse coding with data-adapted dictionaries has been successfully employ…