Department of Computer Science & Engineering

&ldquo;IIT Jodhpur is committed to impart quality education to facilitate the creation of qualified, competent and responsible human resources to meet the emerging technological challenges of the world. It is working towards creating an equitable learning platform that is positive, accessible and effective in the face of the modern societal and scientific advancements.

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 paper we address the issue of enhancement in the quality of scanned images of old manuscripts. Small portions of the text in these manuscripts have degraded with time and are not readable. We propose a segmentation based histogram matching scheme for enhancing these degraded text regions. To automatically identify the degraded text we use a matched wavelet based text extraction algorithm followed by MRF(Markov Random Field) post processing. Additionally we do background clearing to improve the quality of results. This method does not require any a priori information about the font, font size, background texture or geometric transformation. We have tested our method on a variety of manuscript images. The results show proposed method to be a robust, versatile and effective tool for enhancement of manuscript images.

World University root node

Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

Enhancement of old manuscript images

We present a unique representation scheme for events in an area under surveillance, which provides a mechanism to analyze videos from multiple perspectives for unusual activity analysis. We propose clustering in event component spaces and define algebraic operations on these clusters to find co-occurrences of event components. A usualness measure for clusters is proposed that not only gives a measure on how usual or unusual an activity is, but also a basis for analyzing and predicting the possibly usual or unusual activities that can occur in the surveillance region. © Springer-Verlag Berlin Heidelberg 2007.

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

Unusual activity analysis in video sequences

In this paper, we have proposed a novel scheme for the extraction of textual areas of an image using globally matched wavelet filters. A clustering-based technique has been devised for estim ating globally matched wavelet filters using a collection of groundtruth images. We have extended our text extraction scheme for the segmentation of document images into text, background, and picture components (which include graphics and continuous tone images). Multiple, two-class Fisher classifiers have been used for this purpose. We also exploit contextual information by using a Markov random field formulation-based pixel labeling scheme for refinement of the segmentation results. Experimental results have established effectiveness of our approach. © 2007 IEEE.

IEEE Transactions on Image Processing

Text extraction and document image segmentation using matched wavelets and MRF model

Biometrics is the process of verifying or identifying an individual using the person's physiological and behavioral characteristics such as face, fingerprint, iris, signature, and gait. The main advantage of using biometrics for recognition is that it is unique for every individual and cannot be misplaced or forgotten. However, biometric systems that use a single biometric trait have to contend with noisy data, restricted degrees of freedom, failure to-enroll problems, spoof attacks, and unacceptable error rates. Various researchers have suggested that no single biometric modality can provide the protection required for high security applications [1] - [4]. To alleviate this problem and enhance the performance of a biometric system, information from different biometric sources are combined and such systems are known as multimodal biometric systems [2]. © Springer-Verlag Berlin Heidelberg 2007.

Studies in Fuzziness and Soft Computing

Intelligent biometric information fusion using support vector machine

In this paper we present a framework which integrates object-model knowledge with the perceptual organization process. We demonstrate the advantages of the add-on grouping evidences as contributed by the object models for a more robust perceptual organization in the spatiotemporal domain. Our system performs detection of foreground objects along with recognition in video. © 2007 IEEE.

Proceedings - International Conference on Computing: Theory and Applications, ICCTA 2007

Using object models as domain knowledge in perceptual organization: An approach for object category identification in video sequences

This paper proposes a new method for using implicit user feedback from clickthrough data to provide personalized ranking of results in a video retrieval system. The annotation based search is complemented with a feature based ranking in our approach. The ranking algorithm uses belief revision in a Bayesian Network, which is derived from a multimedia ontology that captures the probabilistic association of a concept with expected video features. We have developed a content model for videos using discrete feature states to enable Bayesian reasoning and to alleviate on-line feature processing overheads. We propose a reinforcement learning algorithm for the parameters of the Bayesian Network with the implicit feedback obtained from the clickthrough data. Copyright 2007 ACM.

Proceedings of the ACM International Multimedia Conference and Exhibition

Learning ontology for personalized video retrieval

We present a novel content-based re-ranking scheme for enhancing the precision of video retrieval on the web. We use ontology specified knowledge of the video domain to map user queries to domain-based concepts. The user preferences are learned implicitly from the web logs of users' interaction with a video search engine. A ranking SVM is trained for each concept to learn the ranking function which incorporates user preferences for the concept. The videos are represented by a set of ingeniously derived contentbased features which are based on MPEG-7 descriptors. Our re-ranking scheme thus effectively re-ranks results for new text queries submitted to our video retrieval system, leading to better satisfaction of the users' information need. © 2007 IEEE.

Proceedings - 2007 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2007

Content based re-ranking scheme for video queries on the web

This paper proposes an intelligent 2v-support vector machine based match score fusion algorithm to improve the performance of face and iris recognition by integrating the quality of images. The proposed algorithm applies redundant discrete wavelet transform to evaluate the underlying linear and non-linear features present in the image. A composite quality score is computed to determine the extent of smoothness, sharpness, noise, and other pertinent features present in each subband of the image. The match score and the corresponding quality score of an image are fused using 2v-support vector machine to improve the verification performance. The proposed algorithm is experimentally validated using the FERET face database and the CASIA iris database. The verification performance and statistical evaluation show that the proposed algorithm outperforms existing fusion algorithms. © World Scientific Publishing Company.

International Journal of Neural Systems

Integrating image quality in 2v-ASVM biometric match score fusion

We present a novel approach for applying perceptual grouping principles to the spatio-temporal domain of video. Our perceptual grouping scheme, applied on blobs, makes use of a specified spatio-temporal coherence model. The grouping scheme identifies the blob cliques or perceptual clusters in the scene. We propose a computational model for analyzing a video shot based on a novel principle of perceptual prominence. The principle of perceptual prominence captures the key aspects of mise-en-scène required for characterizing a video scene. Copyright © 2007 IEEE.

IEEE Transactions on Circuits and Systems for Video Technology

Video shot characterization using principles of perceptual prominence and perceptual grouping in spatio-temporal domain

We present an integrated scheme for document image compression which preserves the layout structure, and still allows the display of textual portions to adapt to the user preferences and screen area. We encode the layout structure of the document images in an XML representation. The textual components and picture components are compressed separately into different representations. We derive an SVG (Scalable Vector Graphics) representation of the complete document image. Compression is achieved since the word-images are encoded using specifications for geometric primitives that compose a word. A document rendered from its SVG representation can be adapted for display and interactive access through common browsers on desktop as well as mobile devices. We demonstrate the effectiveness of the proposed scheme for document access. © 2007 IEEE.