I am interested in intersection of machine learning and computer vision especially object detection and graphical models.
In this project, we demonstrate that the evaluation protocol for the evaluation of object proposals is biased. We conduct a thorough evaluation of various proposal methods on three datasets. We also conduct experiments to introduce diagnostic tools to alleviate this bias.
A classifier and a proposal generator specific to text in urban scene images were developed as a part of this project. [Report]
As part of graphical models toolkit, Bethe-ADMM was implemented for Graphlab. A special type of factor, called budget factor was introduced as a feature to ADMM graph. [Code]
Symmetric and Projected Dual Decomposition, Alternating Direction Dual Decomposition were implemented on Graphlab. [Code]
Algorithm was developed to classify transparent objects from their surroundings using a special property of specular reflection. [Publication]