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 framework for classification of text document images based on their script. We deal with the domain of Indian scripts which has high inter script similarities. Indian scripts have characteristic curvature distributions which help in visual discrimination of scripts. We use edge direction based features to capture the distribution of curvature. We also use a recently proposed feature selection algorithm to obtain the most discriminating curvature features. We form hierarchy (automatically) based on statistical distances between the script models. Hierarchy allows us to group similar scripts at one level and then focus on the classification between the similar scripts at the next level leading to improvement in accuracy. We show experiments and results on a large set of about 3400 images.

World University root node

ACM International Conference Proceeding Series

Curvature feature distribution based classification of Indian scripts from document images

In this paper, we propose level-2 and level-3 feature based fingerprint indexing algorithm to improve the speed and accuracy of identification. Indexing parameters are computed using the minutiae and pore features. The identification performance is further improved by incorporating Dempster Shafer theory based match score fusion algorithm. Experimental results on a high resolution fingerprint database show that the proposed algorithm improves the identification performance by at least 10% compared to existing fingerprint identification algorithms.

Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009

Fingerprint indexing using minutiae and pore features

Recent cases such as Commonwealth v Patterson show that there is a lack of research in how to process and recognize simultaneous fingerprint impressions, especially when none of the latent prints in the cluster could be individually matched. SWGFAST released the first version of the standard on simultaneous impression examination that can help fingerprint examiners to systematically compare latent simultaneous impressions to a known ten-print card. However, when the individual is not known, the simultaneous fingerprint impressions have to be compared using a large database of reference ten-prints, making the process very challenging. This paper introduces the research problem of identifying simultaneous latent fingerprint impressions to the community and presents a semi-automatic approach to process the impressions of any individual. The approach generates a list of top matches and latent fingerprint examiners can then examine them for individualization. Using a fingerprint database that contains simultaneous latent impressions, we analyze the performance of the proposed approach obtained by matching simultaneous impressions with the gallery database. ©2009 IEEE.

IEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, BTAS 2009

Simultaneous latent fingerprint recognition: A preliminary study

This paper presents a framework for multi-biometric match score fusion when non-ideal conditions cause conflict in the results of different classifiers. The proposed frame work uses belief function theory to effectively fuse the match scores and density estimation technique to compute the belief assignments. Fusion is performed using belief models such as Transferable Belief Model (TBM) and Proportional Conflict Redistribution (PCR) Rule followed by the likelihood ratio based decision making. Experimental results on multi-instance and multi-unit iris verification show that the proposed fusion framework with PCR rule yields the best verification accuracy even when individual biometric classifiersprovide highly conflicting match scores. © 2009 IEEE.

Proceedings of the 7th International Conference on Advances in Pattern Recognition, ICAPR 2009

Belief function theory based biometric match score fusion: Case studies in multi-instance and multi-unit iris verification

INDIAN JOURNAL OF PHARMACEUTICAL SCIENCES

A Need to Reorient the Pharmaceutical Education: A Survey on Students of Pharmacy Colleges in Punjab

In this paper we describe our document image analysis system which performs segmentation, content characterization as well as semantic labeling of components. Segmentation is done using white spaces and gives the segmented components arranged in a hierarchy. Semantic labeling is done using domain knowledge which is specified where possible in the form of a document model applicable to a class of documents. The novelty of the system lies in the suite of methods it employs which are capable of handling documents in Indian scripts. We have obtained promising results for semantic segmentation of over 30 categories of documents in Indian scripts. © 2009 IEEE.

Syntactic and Semantic labeling of hierarchically organized document image components of Indian scripts

This paper presents an XML-based scheme for managing a large multilingual OCR project. In particular we describe how a new XML based tagging scheme has been exploited to achieve the objectives of the project. Managing a large multi-lingual OCR project involving multiple research groups, developing script specific and script independent technologies in a collaborative fashion is a challenging problem. In this paper, we present some of the software and data management strategies designed for the project aimed at developing OCR for 11 scripts of Indian origin for which mature OCR technology was not available. Copyright © 2009 ACM.

Managing multilingual OCR project using XML

In this paper we propose an inexact spectral matching algorithm that embeds large graphs on a low-dimensional isometric space spanned by a set of eigenvectors of the graph Laplacian. Given two sets of eigenvectors that correspond to the smallest non-null eigenvalues of the Laplacian matrices of two graphs, we project each graph onto its eigenenvectors. We estimate the histograms of these one-dimensional graph projections (eigenvector histograms) and we show that these histograms are well suited for selecting a subset of significant eigenvectors, for ordering them, for solving the sign-ambiguity of eigenvector computation, and for aligning two embeddings. This results in an inexact graph matching solution that can be improved using a rigid point registration algorithm. We apply the proposed methodology to match surfaces represented by meshes. © 2009 Springer Berlin Heidelberg.

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

Inexact matching of large and sparse graphs using laplacian eigenvectors

We present a video summarization technique based on supervised learning. Within a class of videos of similar nature, user provides the desired summaries for a subset of videos. Based on this supervised information, the summaries for other videos in the same class are generated. We derive frame-transitional features and subsequently represent each frame transition as a state. We then formulate a loss functional to quantify the discrepency between state tansitional probabilities in the original video and that in the intended summary video, and optimize this functional. We experimentally validate the performance of the technique using cross-validation scores on two different class of videos, and demonstrate that the proposed technique is able to produce high quality summarization capturing the user perception. © 2008 IEEE.

Proceedings - International Conference on Pattern Recognition

Video summarization with supervised learning

The performance of score-level fusion algorithms is often affected by conflicting decisions generated by the constituent matchers/classifiers. This paper describes a fusion algorithm that incorporates the likelihood ratio test statistic in a support vector machine (SVM) framework in order to classify match scores originating from multiple matchers. The proposed approach also takes into account the precision and uncertainties of individual matchers. The resulting fusion algorithm is used to mitigate the effect of covariate factors in face recognition by combining the match scores of linear appearance-based face recognition algorithms with their non-linear counterparts. Experimental results on a heterogeneous face database of 910 subjects suggest that the proposed fusion algorithm can significantly improve the verification performance of a face recognition system. Thus, the contribution of this work is two-fold: (a) the design of a novel fusion technique that incorporates the likelihood ratio test-statistic in a SVM fusion framework; and(b) the application of the technique to face recognition in order to mitigate the effect of covariate factors.