Department of Electrical and Computer Engineering,
University of California, San Diego
9500 Gilman Drive, Mail code 0409
La Jolla, CA 92093-0409

EBU 1, Room 5512

Email: krmurali at u c s d . e d u
Phone: (858) 534-4538


I am currently a PhD student at the Statistical Visual Computing Lab, Department of Electrical and Computer Engineering at University of California, San Diego (UCSD). I received a Bachelor of Technology in ECE from The Indian Institute of Technology Guwahati, India in May 2008. I have been a Research Assistant at SVCL since 2009.



My research interests are computer vision, machine learning and signal processing. I am currently working on biologically inspired solutions to problems in computer vision.





Connections between SIFT and Biological Vision


The determination of dominant orientation at a given image location is formulated as a decision-theoretic question. This leads to a novel measure for the dominance of a given orientation, which is similar to that used by SIFT. We then show that the new measure can be computed with a network that implements the sequence of operations of the standard neurophysiological model of V1. We verify that the network units exhibit trademark properties of V1 neurons, such as cross-orientation suppression, sparseness, independence, off-looking strategy etc. In this work, we establish a connection between SIFT (and similar histogram based features that are popular in computer vision) and biological vision. This connection provides a justification for the success of SIFT-like features and tries to establish the sequence of operations that are statistically optimal for low-level image representation. It also shows that this optimal sequence of operations is performed by the visual cortex.




Fully Automated Design of Fast Object Detectors


We consider the problem of learning a real-time object detector cascade with no manual supervision and propose a two-step solution. A discriminant saliency based procedure is used to automatically collect a high quality training set by cleaning and cropping a dataset obtained from a web search. The second step involved a new optimization procedure for automated cascade design that achieves an optimal trade-off between classification accuracy and detection speed. Demos include an interface that enables the user to train a real-time object detector for any desired object, and a smartphone application that runs real-time detectors designed using our system for 10 real world objects.




How Language and Visual Saliency Jointly Guide Eye Movements in the Perception of Complex Scenes


Integrating visual and linguistic information e.g. combining the visual properties with a spoken description of a scene, is an extremely important skill that humans use almost constantly. Despite this, how linguistic information influences the processing of complex visual input is still unclear. In this collaboratory project, we use methods from psychology and linguistics as well as computer vision in order to understand (1) How linguistic descriptions of scenes drive eye movements (2) how the visual aspects of scenes interact with language processing and (3) how we can derive computational models that predict the interaction between eye movements and language.



Shape Coding in Area V4


The ventral visual pathway in primates is often modeled as a feedforward hierarchy that is selective to increasingly complex patterns (from simple oriented edges to objects) and is increasingly invariant. Specifically, it has been shown from neural recordings of macaque monkeys that Area V4 is selective to complex properties of boundaries such as orientation and curvature in addition to being invariant to spatial translation. This project involves the derivation of a computational model of area V4 as a natural extension to our biologically plausible model of V1. We also investigate applications to computer vision tasks.  





A biologically plausible network for the Computation of Orientation Dominance

Kritika Muralidharan and Nuno Vasconcelos

Proc. Neural Information Processing Systems(NIPS), 2010 (spotlight presentation)


On the connections between SIFT and Biological Vision

Kritika Muralidharan and Nuno Vasconcelos

Frontiers in Systems Neuroscience, March 2010.

Last update: 07/28/2009


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