Department of Computer Science & Engineering

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Flexibility in curriculum, in all its academic programs, encompassing elements of teaching, research, creativity, innovation and entrepreneurship is the most unique feature of IIT Jodhpur. The Department of Science and Technology (DST), under the National Mission on Cyber Physical Systems, has identified IIT Jodhpur as a Technology &amp; Innovation Hub in the area of Augmented Reality and Virtual Reality. The institute has also been nominated as the coordinator for the Jodhpur City and Knowledge Cluster by the Office of Principal Scientific Adviser, Government of India.

The sprawling 852-acre IIT Jodhpur campus is a majestic tribute to the deep-rooted diverse cultural heritage of the region. The campus is an architectural masterpiece, which is slated to become an international exemplar of sustainability with net-zero energy, and advanced water and waste management strategies.&rdquo;

IIT Jodhpur

We present a novel predictive statistical framework to improve the performance of an EigenTracker. In addition, we use fast and efficient eigenspace updates to learn new views of the object being tracked on the fly. We also incorporate a new Importance Sampling mechanism which increases the robustness of the EigenTracker, and enables it to track non-convex objects better. Our EigenTracker is flexible - it is possible to use it symbiotically with other trackers. We show its successful application in hand gesture analysis; and face and person tracking. ©2004 IEEE.

World University root node

Proceedings - International Conference on Image Processing, ICIP

On line predictive appearance-based tracking

This paper presents a framework for a distributed knowledge based system by integrating case based reasoning (CBR) and Fuzzy Logic. Fuzzy Logic gives CBR the power to deal with impreciseness and uncertainty. The framework for handling distributed case bases enables our system to construct solution based on collective experience distributed by discipline, time, and geography. In the proposed system the cases can be expressed in terms of attributes that can be crisp as well as fuzzy and appropriately similarity scores are computed. The cases can have attributes from a vocabulary, which can be defined with the constraint of global commitments so that the attributes can be shared and interpreted in a distributed setting. We have implemented a knowledge sharing protocol with common ontology as the repository of exchange vocabulary for knowledge sources with different Universe of Discourses (UOD). We have developed a shell for tailored application development in different domains. We have used RDBMS as the back end repository for cases, DAML+OIL for Ontology design, SAX and DOM for ontology access and RMI for remote procedure call. We have illustrated effectiveness of our approach by developing a travel planning and a help desk application. © 2004 Elsevier B.V. All rights reserved.

Applied Soft Computing Journal

Distributed fuzzy case based reasoning

3-D object recognition involves using image-computable features to identify 3-D object. A single view of a 3-D object may not contain sufficient features to recognize it unambiguously. One needs to plan different views around the given object in order to recognize it. Such a task involves an active sensor-one whose parameters (external and/or internal) can be changed in a purposive manner. In this paper, we review two important applications of an active sensor. We first survey important approaches to active 3-D object recognition. Next, we review existing approaches towards another important application of an active sensor namely, that of scene analysis and interpretation. © 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Fulltext

Pattern Recognition

Active recognition through next view planning: A survey

In this paper, we have studied various well known algorithms for iris recognition. Four algorithms due to Avila [5], Li Ma [9], Tisse [13] and Daugman [15] are implemented and compared on the CASIA iris image database. The results show that the Daugman's algorithm gave the highest accuracy of 99.9%.

Proceedings of International Conference on Intelligent Sensing and Information Processing, ICISIP 2004

Comparison of Iris Recognition Algorithms

Proceedings of the Workshop on Biometric Challenges from Theory to Practice-ICPR Workshop

Comparing robustness of watermarking algorithms on biometrics data

In this paper we have presented a fusion of three face recognizers, LFA, LBA and KDDA, -which combines the three Confidence Measure Factors (CMFs) using a RBF neural netwrok. This strategy is used to increase the accuracy of the face recognition system. The recognizer is tested on AT&amp;T-ORL, AR and IITK Face database and the results are found to be more than 95%. © 2004 IEEE.

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Face recognition using multiple recognizers

This paper presents a multimodal biometrics system using watermarking algorithms with two levels of security for simultaneously verifying an individual and protecting the biometric template. Iris template is watermarked in face, such that the face is visible for verification and the watermarked iris is used to cross authenticate the individual and secure the biometrics data as well. The accuracy of the multimodal biometrics system is around 96.8%. This system is also resistant to common attacks on biometric templates. © 2004 IEEE.

Digital watermarking based secure multimodal biometric system

Tropical Doctor

Live ascariasis of gall bladder and bile duct: Clinical and radiological features

ICVGIP

Multi Biometric System for Verification with Minimum Training Data

In this paper three algorithms for iris verification have been presented. Iris detection algorithms include the normalization and iris extraction steps. Three algorithms for verification process are (a) Algorithm using radial and circular features, (b) Algorithm using Fourier transforms and (c) Algorithm using Circular-Mellin transforms. Proposed algorithms have been tested on CASIA database and some non-infrared Iris images. The experimental results show that the algorithm based on Circular - Mellin Transform gives the best result with an accuracy of 95.45%. Some initial experiments on non-infrared iris images shows that this algorithm can work on such images but it still requires some more attention and this is our future work. © Springer-Verlag Berlin Heidelberg 2004.