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 paper we have presented a data model, which allows the users to compose a multimedia presentation, involving different data types (i.e., video, audio, text, graphics and images) in an easy and efficient fashion. This model provides an efficient mechanism for organizing and manipulating multimedia data by assigning logical representations to the underlying data streams and their contents. This scheme also specifies operations for associative access of the multimedia information. On the basis of this data model we have developed a system for browsing and authoring multimedia presentations. Implementation has been done in Visual C++ under Windows-95 environment. © 1999 Taylor & Francis Group, LLC.

World University root node

IETE Journal of Research

A multimedia data model

This paper describes a system for recognition of dynamic hand gestures. Appearance based and motion related features have been used for characterizing the gestures. Gesture sequences have been interpreted using HMM based recognition engine. Interpreted gesture sequences have been translated into commands for controlling a remote robot.

IEEE Region 10 Annual International Conference, Proceedings/TENCON

Gesture based interface for remote robot control

Tropical Doctor

Experience with window formation technique for hydrocele

Identification of the script in an image of a document page is of primary importance for a system processing multi-lingual documents. In this paper three trainable classification schemes have been proposed for identification of Indian scripts. The first scheme is based upon a frequency domain representation of the horizontal profile of the textual blocks. The other two schemes use connected components extracted from the textual region. We have proposed a novel Gabor filter-based feature extraction scheme for the connected components. We have also found that frequency distribution of the width-to-height ratio of the connected components can also be used for script recognition. It has been experimentally found that the Gabor filter-based scheme provides the most reliable performance. However, the other two techniques are computationally more efficient. © 1999 IEEE.

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

Trainable script identification strategies for Indian languages

The use of a self-organising neural network as a feedforward compensator for robot tracking control applications is proposed. The topology of the input space is adaptively mapped onto a set of neurons where each neuron represents a discrete cell in the input domain. Within each cell, a linear mapping is established between the input and output space. The training of such a network involves training of a weight vector that represents the topology of the input space and weight vectors (action space weights) that linearly code an input pattern to action space. In the first phase of network training, a 'neural-gas' algorithm is employed for learning the topology of the input space while weight vectors representing control action space is learned by backpropagating feedback control action. During this phase of learning, the weights associated with neurons in the neighbourhood of winning neurons are also updated. In the second stage, a recursive least squares based estimation scheme is applied to fine tune the action space weights associated with winning neurons only, without disturbing the input topology map learned in the first phase. The proposed scheme has been compared with multilayered network (MLN) and radial basis function network (RBFN) based inverse dynamics learning schemes. Simulation results show that the proposed scheme has better generalisation capability than both MLN and RBFN. © lEE, 1998.

IEE Proceedings: Control Theory and Applications

Application of self-organising neural networks in robot tracking control

This paper is concerned with signature verification. Three different types of global features have been used for the classification of signatures. Feed-forward neural net based classifiers have been used. The features used for the classification are projection moments and upper and lower envelope based characteristics. Output of the three classifiers is combined using a connectionist scheme. Combination of these feature based classifiers for signature verification is the unique feature of this work. Experimental results show that combination of the classifiers increases reliability of the recognition results. Copyright © 1996 Pattern Recognition Society. Published by Elsevier Science Ltd.

Pattern Recognition

Signature verification using multiple neural classifiers

This paper presents a genetic algorithm for solving the problem of structural shape matching. Both sequential and parallel versions of the algorithm have been presented. The genetic operators - reproduction, crossover and mutation - have been constructed for this specific problem. A new variation of the crossover operator, called the color crossover, is presented. This operator has resulted in significant improvement in runtime and algorithm efficiency. Parallelization has been achieved using an "island" model, with several subpopulations and occasional migration. A complete framework for an object recognition system using this genetic algorithm has been presented. Encouraging experimental results have been obtained. © 1997 Pattern Recognition Society. Published by Elsevier Science Ltd.

Matching structural shape descriptions using genetic algorithms

This paper describes an abductive reasoning based inferencing engine for image interpretation. The inferencing strategy finds an acceptable and consistent explanation of the features detected in the image in terms of the objects known a priori. The inferencing scheme assumes representation of the domain knowledge about the objects in terms of local and/or relational features. The inferencing system can be applied for different types of image interpretation problems like 2-D and 3-D object recognition, aerial image interpretation, etc. In this paper, we illustrate functioning of the system with the help of a 2-D object recognition problem.

International Journal of Pattern Recognition and Artificial Intelligence

An abductive reasoning based image interpretation system

This paper is concerned with the design of a neuroadaptive trajectory tracking controller. The paper presents a new control scheme based on inversion of a feedforward neural model of a robot arm. The proposed control scheme requires two modules. The first module consists of an appropriate feedforward neural model of forward dynamics of the robot arm that continuously accounts for the changes in the robot dynamics. The second module implements an efficient network inversion algorithm that computes the control action by inverting the neural model. In this paper, a new extended Kalman filter (EKF) based network inversion scheme is proposed. The scheme is evaluated through comparison with two other schemes of network inversion: gradient search in input space and Lyapunov function approach. Using these three inversion schemes the proposed controller was implemented for trajectory tracking control of a two-link manipulator. Simulation results in all cases confirm the efficacy of control input prediction using network inversion. Comparison of the inversion algorithms in terms of tracking accuracy showed the superior performance of the EKF based inversion scheme over others. © 1996 IEEE.

Fulltext

IEEE Transactions on Neural Networks

On adaptive trajectory tracking of a robot manipulator using inversion of its neural emulator

The paper is concerned with the design of a hybrid controller structure, consisting of the adaptive control law and a neural-network-based learning scheme for adaptation of time-varying controller parameters. The target error vector for weight adaptation of the neural networks is derived using the Lyapunov-function approach. The global stability of the closed-loop feedback system is guaranteed, provided the structure of the robot-manipulator dynamics model is exact. Generalisation of the controller over the desired trajectory space has been established using an online weight-learning scheme. Model learning, using a priori knowledge of a robot arm model, has been shown to improve tracking accuracy. The proposed control scheme has been implemented using both MLN and RBF networks. Faster convergence, better generalisation and superior tracking accuracy have been achieved in the case of the RBF network. © IEE, 1996.