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

In this work, an accurate method for real time assessment of tool wear in face milling based on the cutting force signals using support vector machine learning is presented. New combinations of signal processing techniques are used to extract relevant signal features, which are robust against random process variations, external chance disturbances and presence of outliers, from the acquired data. Relationship between the extracted features and the observed values of tool wear is built using the support vector regression technique. This machine learning algorithm is accurate and robust for estimation of complex and non-linear relationship among the process variables. Grid search method is adopted to select the optimum model parameters. The developed models are validated on unseen testing data for their predicting capabilities. Significant results are obtained using the proposed method for both laboratory and industrial data.

World University root node

Proceedings of the IEEE International Conference on Industrial Technology

Support vector regression based tool wear assessment in face milling

We propose a novel biometrics cryptosystem where one can send and receive secure information using just the fingerprints. This cryptosystem is a judicious blend of the asymmetric cryptosystem like RSA and the symmetric Fuzzy Vault Scheme having the advantages of both the aforementioned cryptosystems. We have proposed a modification of the Fuzzy Vault Scheme to make it more robust against variations in the values of biometric features. Finally we propose the use of invariant features as a key to producing a hierarchical security system where the same key (fingerprint) can be used to generate encrypted messages at different levels of security.

Proceedings - International Conference on Pattern Recognition

Biometrics based asymmetric cryptosystem design using modified fuzzy vault scheme

In this paper, we design a content-based image retrieval system where multiple query examples can be used to indicate the need to retrieve not only images similar to the individual examples, but also those images which actually represent a combination of the content of query images. We propose a scheme for representing content of an image as a combination of features from multiple examples. This scheme is exploited for developing a multiple example-based retrieval engine. We have explored the use of machine learning techniques for generating the most appropriate feature combination scheme for a given class of images. The combination scheme can be used for developing purposive query engines for specialized image databases. Here, we have considered facial image databases. The effectiveness of the image retrieval system is experimentally demonstrated on different databases. © 2006 IEEE.

IEEE Transactions on Image Processing

Multiple exemplar-based facial image retrieval using independent component analysis

Conceptual organization of semi-structured documents can help in effective retrieval from collections of emails, product complaints, video descriptions etc. In this paper, we propose a conceptual organization scheme for grouping and categorizing semi-structured text data using natural language processing techniques. We propose a knowledge-discovery mechanism that extracts noun phrases from documents and arranges them into concept maps based on their co-occurrence. The emerging concept maps can be used for automatic grouping and conceptual categorization of documents. Further, Phrase structure Grammar is employed to extract relationships among these entities from documents and index the document collection with these relations. © 2006 IEEE.

Proceedings - 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2006 Workshops Proceedings)

Knowledge discovery from semi-structured data for conceptual organization

This paper presents a novel fingerprint classifier fusion algorithm using Dempster-Shafer theory concomitant with update rule. The proposed algorithm accurately matches fingerprint evidences arid also efficiently adapts to dynamically evolving database size without compromising accuracy or speed. We experimentally validate our approach using three fingerprint recognition algorithms based on minutiae, ridges, and image pattern features. The performance of our proposed algorithm is compared with these individual fingerprint algorithms and commonly used fusion algorithms. In all cases, the proposed Dempster Shafer theory with update rule outperforms existing algorithms even with partial fingerprint image. We also show that as the database size increases, the proposed algorithm is designed to operate on only the augmented data instead of the entire database, thereby reducing the training time without compromising the verification accuracy. © IEICE 2006.

IEICE Electronics Express

DS theory based fingerprint classifier fusion with update rule to minimize training time

This paper presents a combined DWT and LSB based biometric watermarking algorithm that securely embeds a face template in a fingerprint image. The proposed algorithm is robust to geometric and frequency attacks and protects the integrity of both the face template and the fingerprint image. Experimental results performed on a database of 750 face and 750 fingerprint images show that the algorithm has the advantages of both the existing DWT and LSB based algorithms. A multimodal biometric algorithm is used as a metric to evaluate the combined performance of both face and fingerprint recognition. © IEICE 2006.

Robust biometric image watermarking for fingerprint and face template protection

Unsupervised clustering is an important tool to analyze video data. Selection of an appropriate clustering scheme is governed by the suitability of the clusters it produces. It is difficult to formulate cluster suitability criteria for a domain where different feature attributes have different meanings. We propose a novel clustering strategy, tailored towards the specific requirements of clustering in video data. Our clustering methodology decouples clustering along different feature components. Our scheme chooses the clustering model so as to meet the requirements of clustering in video data. The clusters obtained from our scheme reasonably model the homogeneous color regions in a video scene in both space and time. The space-time clusters obtained by our clustering methodology can be subsequently grouped together to compose meaningful objects. Experimental comparison of our results with existing clustering techniques clearly show that our scheme takes care of many of the problems with traditional clustering schemes applied to the heterogeneous feature space of video. © 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Pattern Recognition

Clustering in video data: Dealing with heterogeneous semantics of features

Many attempts have been made to analyze gene expression data. Typical goals of such analysis include discovery of subclasses, designing predictors/classifiers for diseases, identifying marker genes, and trying to get a deeper understanding of underlying biological process. Success of each of these tasks strongly depends on the features used to solve the problem. The high dimensional nature of expression profiles makes the task very difficult. Consequently, many researchers have used some feature selection criteria to reduce the dimensionality of the problem. These approaches are off-line in nature, as feature selection is done in a separate phase from the system design phase. These approaches ignore the fact that utility of features depends on both the problem that is solved and the tool that is used to solve the problem. We here propose to use a novel neural scheme that picks up the necessary features on-line when the system learns the classification task. Because it considers all the features at one go, it does not miss any subtle combination of these features. We demonstrate the effectiveness of our on-line feature selection (OFS) scheme to distinguish between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) cancer expression data set. Our scheme could identify only five genes that can produce results as good as or even better than what is reported in the literature on this data set. It identifies an important marker gene that alone has a very good discriminating power. This analysis method is quite general in nature and can be effectively used in other areas of bioinformatics. © 2006 Wiley Periodicals, Inc.

International Journal of Intelligent Systems

On identifying marker genes from gene expression data in a neural framework through online feature analysis

We propose a technique for view synthesis of scenes with static objects as well as objects that translate independent of the camera motion. Assuming the availability of three vanishing points in general position in the given views, we set up an affine coordinate system in which the static and moving points are reconstructed and the translations of the dynamic objects are recovered. We then describe how to synthesize new views corresponding to a completely new camera specified in the affine space with new translations for the dynamic objects. As the extent of the synthesized scene is restricted by the availability of corresponding points, we use a voxel-based volumetric scene reconstruction scheme to obtain a scene model and synthesize views of the entire scene. We present experimental results to validate our technique. © Springer-Verlag Berlin Heidelberg 2006.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

View synthesis of scenes with multiple independently translating objects from uncalibrated views

We propose an empirical computational model for generating an interpretation of a video shot based on our proposed principle of perceptual prominence. The principle of perceptual prominence captures the key aspects of mise-en-scène required for interpreting a video scene. We present a novel approach for applying perceptual grouping principles to the spatio-temporal domain of video. Our spatio-temporal perceptual grouping scheme, applied on blob tracks, makes use of a specified spatio-temporal coherence model. A high level semantic interpretation of scenes is done using the mise-en-scène features and the perceptual prominence computed for the perceptual clusters. © Springer-Verlag Berlin Heidelberg 2006.