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

World University root node

Design of face detection CAPTCHA for implementing robust security

A Cellular Automata (CA) is a computing model of complex System using simple rule. In this paper the problem space is divided into number of cell and each cell can be one or several final state. Cells are affected by neighbor's with the application of simple rule. This paper deals with the Cellular Automata in cryptography for a class of Block Ciphers through a new block encryption algorithm based on programmable cellular automata.The proposed algorithm belongs to the class of symmetric key systems. © 2010 Springer-Verlag Berlin Heidelberg.

Communications in Computer and Information Science

Encryption algorithm for block ciphers based on programmable cellular automata

In this paper we address the problem of matching two 3D shapes by representing them using the eigenvalues and eigenvectors of the discrete diffusion operator. This provides representation framework useful for scale-space local shape descriptors and shape comparisons. We formally introduce diffusion embedding and we propose unit hypersphere normalizations of this embedding. We also propose a method to find compatible time scale while matching two different shapes with varying size and sampling. We propose a practical algorithm that seeks the largest set of mutually consistent point-to-point matches between two shapes based on isometric consistency between the two embedded shapes. We illustrate our method with several examples of matching shapes at various scales. © 2010 IEEE.

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010

Shape matching based on diffusion embedding and on mutual isometric consistency

Parametric coding is a technique in which data is processed to extract meaningful information and then representing it compactly using appropriate parameters. Parametric Coding exploits redundancy in information to provide a very compact representation and thus achieves very high compression ratios. However, this is achieved at the cost of higher computation complexity. This disadvantage is now being offset by the availability of high speed processors, thus making it possible to exploit the high compression ratios of the parametric video coding techniques. In this paper a novel idea for efficient parametric representation of video is proposed. We perform Oct-Tree Decomposition on a video stack, followed by parameter extraction using Radial Basis Function Networks (RBFN) to achieve exceptionally high compression ratios, even higher than the state of art H.264 codec. The proposed technique exploits spatial-temporal redundancy and therefore inherently achieves multi-frame prediction. © 2010 ACM.

ACM International Conference Proceeding Series

RBF based spatio-temporal representation technique for video compression

In this paper we propose a novel algorithm for object tracking from Video images based on segmentation and Kernel based procedure. Many Kernel based object tracking algorithms have been developed during last few years. The computational complexity becomes very high in those kernel based techniques. In our proposed method the target localization problem is minimized using segmentation technique, instead of using mean shift tracking algorithm. Following segmentation technique the localization problem of target candidate gets minimized, and then comparing the target candidate with the target model by using Bhattacharya coefficient the object can easily be detected. So, the object can be tracked with less computational burden and more efficiently. The proposed algorithm is validated with an existing video sequence and another with a real time video sequence. © 2010 IEEE.

Proceedings of the 2010 Annual IEEE India Conference: Green Energy, Computing and Communication, INDICON 2010

Real time object tracking based on segmentation and distance minimization

In this paper we propose a novel algorithm for object tracking from Video images based on segmentation and Kernel based procedure. Many Kernel based object tracking algorithms have been developed during last few years. The computational complexity becomes very high in those kernel based techniques. In our proposed method the target localization problem is minimized using segmentation technique, instead of using mean shift tracking algorithm. Following segmentation technique the localization problem of target candidate gets minimized, and then comparing the target candidate with the target model by using Bhattacharya coefficient the object can easily be detected. So, the object can be tracked with less computational burden and more efficiently. The proposed algorithm is validated with an existing video sequence and another with a real time video sequence. ©2010 IEEE.

2010 5th International Conference on Industrial and Information Systems, ICIIS 2010

Real time object tracking based on segmentation and kernel based method

Cultural heritage is encoded in a variety of forms. The task of preserving heritage involves preserving the tangible and intangible resources that broadly define that heritage. A significant aspect of intangible heritage resources are performing arts which include classical dance and music. Digital heritage resources include heritage artefacts in digitized form as well as the background knowledge that puts them in perspective. We present an ontology based approach to capture and preserve the knowledge with digital heritage artefacts. Since the artefacts are generally preserved in multimedia format, we propose the use of Multimedia Web Ontology (MOWL) that supports probabilistic reasoning with media properties of domain concepts, to encode the domain knowledge. We propose an architectural framework that includes a method to construct the ontology with a labelled set of training data and use of the ontology to automatically annotate new instances of digital heritage artefacts. The annotations enable creation of a semantic navigation environment in a cultural heritage repository. We have realized a proof of concept in the domain of Indian Classical Dance and present some results.

eHeritage'10 - Proceedings of the 2nd ACM Workshop on eHeritage and Digital Art Preservation, Co-located with ACM Multimedia 2010

Preservation of intangible heritage: A case-study of Indian classical dance

The paper presents a novel framework for learning the hash functions for indexing through Multiple Kernel Learning. The Distance Based Hashing function is applied which does the object projection to hash space by preserving inter object distances. In recent works, the kernel matrix has been proved to be more accurate representation of similarity in various recognition problems. Our framework learns the optimal kernel for hashing by parametrized linear combination of base kernels. A novel application of Genetic Algorithm for the optimization of kernel combination parameters is presented. We also define new texture based feature representation for images. Our proposed framework can also be applied for optimal combination of multiple sources for indexing. The evaluation of the proposed framework is presented for CIFAR-10 dataset 1 by applying individual and combination of different features. Additionally, the primary experimental results with MNIST dataset2 is also presented. © 2010 ACM.

Multiple kernel learning for image indexing

We propose a novel particle filter-based motion compensation strategy for video coding. We use a higher order linear model in place of the traditional translational model used in standards such as H.264. The measurement/observation process in the particle filter is a computationally efficient mechanism as opposed to traditional search methods. We use a multi-resolution framework for efficient parameter estimation. Results of our experimentation show reduced residual energy and better PSNR as compared to traditional video coding methods, especially in regions of complex motion such as zooming and rotation. © 2010 ACM.

Restricted affine motion compensation in video coding using particle filtering

The paper presents application of multiple features for word based document image indexing and retrieval. A novel framework to perform Multiple Kernel Learning for indexing using the Kernel based Distance Based Hashing is proposed. The Genetic Algorithm based framework is used for optimization. Two different features representing the structural organization of word shape are defined. The optimal combination of both the features for indexing is learned by performing MKL. The retrieval results for document collection belonging to Devanagari script are presented. © 2010 IEEE.