Manifold learning is an effective methodology for extracting nonlinear structures from high-dimensional data with many applications in image analysis, computer vision, text data a...
Most connectionist research has focused on learning mappings from one space to another (eg. classification and regression). This paper introduces the more general task of learnin...
d Abstract) Aravindhan K Krishnan Madhava Krishna Supreeth Achar This paper presents an approach to explore an unknown indoor environment using vision as the sensing modality, t...
Aravindhan K. Krishnan, K. Madhava Krishna, Supree...
In this paper we present a new practical camera characterization technique to improve color accuracy in high dynamic range (HDR) imaging. Camera characterization refers to the pro...
Higher-order tensor decompositions are analogous to the familiar Singular Value Decomposition (SVD), but they transcend the limitations of matrices (second-order tensors). SVD is ...