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

To enable seamless integration of video information on the semantic web, we require that the knowledge of a video domain be formally specified in an ontology. We present a novel approach for defining video domain concepts in an ontology using properties that can be observed from the media. We use the ontology specified knowledge for recognizing concepts relevant to a video scene by making observations for the media properties of concepts as well as making inferences from other ontological concept definitions and relations. For this purpose we introduce new language constructs to OWL (Web Ontology Language), which are used to specify the inherently uncertain nature of media observations. The new constructs also allow additional semantics concerned with the association of media properties with concepts. We propose the use of Bayesian network as the reasoning mechanism for doing inferencing tasks in the presence of uncertainty. The video is annotated with the relevant concepts defined in the ontology. These conceptual annotations are used to create hyperlinks in the video collection. © 2006 IEEE.

World University root node

Proceedings - 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings), WI'06

Using multimedia ontology for generating conceptual annotations and hyperlinks in video collections

Effective self-organization schemes lead to the creation of autonomous and reliable robot teams that can outperform a single, sophisticated robot on several tasks. We present here a novel, vision-based microscopic framework for active and distributed object-recognition and pose-estimation using a team of robots of simple construction. The team performs the task of locating a given object(s) in an unknown territory, recognizing it with sufficient confidence and estimating its pose. The larger goal is to experiment with probabilistic frameworks and graph-theoretic methods in the design of robot teams to achieve autonomous self-organization independent of the task at hand. We have chosen 3D object recognition as a first problem area to evaluate the effectiveness of our system design. The system comprises a probabilistic framework for the successful detection of the object in a coordinated manner and adaptive measures in case of machinery failures or presence of obstacles. A pose estimation method for the detected object and graph theoretic solutions for optimal field coverage by the robots are also presented. Each robot is provided with a part-based, spatial model of the object. The object to be recognized is taken to be much bigger than the robots and need not fit completely into the field of view of the robot cameras. We assume no knowledge of the internal parameters of the robot cameras and perform no camera calibration procedures. Initial simulation results corroborate our system design and field coverage methods. © 2006 IEEE.

9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06

A microscopic framework for distributed object-recognition &amp; pose-estimation

This paper presents a Dempster Shafer theory based classi-fier fusion algorithm to improve the performance of fingerprint verifica-tion. The proposed fusion algorithm combines decision induced match scores of minutiae, ridge, fingercode and pore based fingerprint verifica-tion algorithms and provides an improvement of at least 8.1% in the ver-ification accuracy compared to the individual algorithms. Further, pro-posed fusion algorithm outperforms by at least 2.52% when compared with existing fusion algorithms. We also found that the use of Demp-ster's rule of conditioning reduces the training time by approximately 191 seconds.

Computer vision, graphics and image processing

Dempster-Shafer Theory Based Classifier Fusion for Improved Fingerprint Verification Performance

In this paper we have proposed a scheme for parsing News video sequences into their semantic components using integrated aural and visual features. We have explored use of the Token Passing Algorithm with HMM for simultaneous segmentation and characterization of the components. Experimentation with about 100 sequences have shown impressive results. © Springer-Verlag Berlin Heidelberg 2005.

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

Parsing news video using integrated audio-video features

A novel face recognition algorithm using single training face image is proposed. The algorithm is based on textural features extracted using the 2D log Gabor wavelet. These features are encoded into a binary pattern to form a face template which is used for matching. Experimental results show that on the colour FERET database the accuracy of the proposed algorithm is higher than the local feature analysis (LFA) and correlation filter (CF) based face recognition algorithms even when the number of training images is reduced to one. In comparison with recent single training image based face recognition algorithms, the proposed 2D log Gabor wavelet based algorithm shows an improvement of more than 3% in accuracy. © IEE 2005.

Electronics Letters

Textural feature based face recognition for single training images

In this paper, we discuss a new representation scheme for word images which exploits the structural features. The word image features are represented in the form of a graph called as the Geometric Feature Graph (GFG). The GFG is encoded in the form of a string which serves as a compressed representation of the word image skeleton. We demonstrate reconstruction, and retrieval of word images for 3 different scripts using the GFG string. © 2005 IEEE.

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

Improved geometric feature graph: A script independent representation of word images for compression, and retrieval

Unconstrained Face Recognition for Law Enforcement Applications

This paper present a novel framework for formal specification of spatio-temporal relations between media objects using fuzzy membership. We have illustrated its use in multimedia ontologies and have described a reasoning framework for creating media based descriptions of concepts. © Springer-Verlag Berlin Heidelberg 2005.

Specifying spatio temporal relations for multimedia ontologies

This paper addresses the problem of synthesizing novel views of a scene using images taken by an uncalibrated translating camera. We propose a method for synthesis of views corresponding to translational motion of the camera. Our scheme can handle occlusions and changes in visibility in the synthesized views. We give a characterisation of the viewpoints corresponding to which views can be synthesized. Experimental results have established the validity and effectiveness of the method. Our synthesis scheme can also be used to detect translational pan motion of the camera in a given video sequence. We have also presented experimental results to illustrate this feature of our scheme. © 2004 Published by Elsevier B.V.

Pattern Recognition Letters

Novel view synthesis using a translating camera

Most model-based three-dimensional (3-D) object recognition systems use information from a single view of an object. However, a single view may not contain sufficient features to recognize it unambiguously. Further, two objects may have all views in common with respect to a given feature set, and may be distinguished only through a sequence of views. A further complication arises when in an image, we do not have a complete view of an object. This paper presents a new online scheme for the recognition and pose estimation of a large isolated 3-D object, which may not entirely fit in a camera's field of view. We consider an uncalibrated projective camera, and consider the case when the internal parameters of the camera may be varied either unintentionally, or on purpose. The scheme uses a probabilistic reasoning framework for recognition and next-view planning. We show results of successful recognition and pose estimation even in cases of a high degree of interpretation ambiguity associated with the initial view. © 2005 IEEE.