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

World University root node

Multimedia Systems and Content-based Image Retrieval, S

Face Recognition Technology: A Biometric Solution to Security Problems

JOURNAL OF CLINICAL PHARMACOLOGY

Audit of pattern of utilization of drugs using anatomical, therapeutic and chemical classification and defined daily dose concept at BP Koirala Institute of Health Sciences.

IEEE International Workshop on Computer Architectures for Machine Perception {\ldots}

A Comprative Study of Various Face Recognition Algorithms

Face detection is an important research area having wide applications in man-machine interface, visual surveillance and face recognition. We have proposed an algorithm for face detection using Gradient Vector Flow in gray level images which overcomes the problem for localization and initialization. The algorithm has been tested on various face databases and the result shows an accuracy of 97% with invariance to pose and orientation.

International Conference on Machine Learning and Cybernetics

Face detection using gradient vector flow

A gesture-based interface involves tracking a moving hand across frames, and extracting the semantic interpretation corresponding to the gesture. This is a difficult task, since there is a change in both the position as well as the appearance of the hand. Further, such a system should be robust to the speed at which the gesture is performed. This paper presents a novel attempt at developing a hand gesture-based interface. We propose an on-line predictive EigenTracker for the moving hand. Our tracker can learn the eigenspace on the fly. We propose a new state-based representation scheme for hand gestures, based on the eigenspace reconstruction error. This makes the system independent of the speed of performing the gesture. We use learning for adapting the gesture recognition system to individual requirements. We show results of successful operation of our system even in cases of background clutter and other moving objects.

Fulltext

IETE Journal of Research

Developing a gesture-based interface

This paper is concerned with recognition of handwritten Devnagari numerals. The basic objective of the present work is to provide an efficient and reliable technique for recognition of handwritten numerals. Three different types of features have been used for classification of numerals. A multi-classifier connectionist architecture has been proposed for increasing reliability of the recognition results. Experimental results show that the technique is effective and reliable.

Sadhana - Academy Proceedings in Engineering Sciences

Devnagari numeral recognition by combining decision of multiple connectionist classifiers

In this paper, we have proposed a fuzzy rule based system for classification of video into semantic categories. The classification scheme uses an evolutionary learning methodology to evolve a fuzzy system for use in the classification process. This evolved fuzzy class tier has the inherent capability to tackle variations and ambiguities invariably present in the video data. A novel fuzzy theoretic sheme has been suggested for extraction of key frames from a given video after shot segmentation. Frame based temporal features and spatial features obtained from key frames have been used in the classification system. We have developed an experimental system for categorization of sports video. The experimental system has yielded reasonably correct recognition results for a large number of samples.

Semantic categorization of video: An evolutionary learning based approach

Local appearance-based approaches for planar object recognition do not support efficient means for indexing of object models. The size of the feature set which needs to be stored and processed is also large. To reduce database size, we propose an optimal feature extraction technique that selects only the salient features of an object. Since in typical local appearance-based systems, the actual feature information is not isolated from the background, scene clutter causes error in recognition. We propose a mechanism whereby this shortcoming can be alleviated. Further, by indexing onto this space, we propose to improve the performance in terms of computation time and suppression of false positives. This indexing mechanism based on the color histogram and the geometry of the feature, exploits the fact that features tend to form clusters in the feature space based on their similarity of appearances. We have implemented the proposed system and verified its validity through extensive testing. © 2002 Published by Elsevier Science B.V.

Pattern Recognition Letters

Indexing for local appearance-based recognition of planar objects

This paper describes a novel appearance based scheme for extraction and representation of video objects. The tracking algorithm used for video object extraction is based upon a new eigen-space update scheme. We propose a scheme for organisation of video objects in an appearance based hierarchy. Appearance based hierarchy is constructed using a new SVD based eigen-space merging algorithm. The hierarchy enables approximate query resolution. Experiments performed on a large number of video sequences have yielded promising results. © 2002 IEEE.

Proceedings - International Conference on Pattern Recognition

An appearance based approach for video object extraction and representation

One of the most successful applications of image analysis and understanding, face recognition has received significant attention. There are at least two reasons for the trend: the first is the wide range of commercial and law enforcement application and the second is the availability of feasible technologies. In general, few methods of face recognition are in practice, Feature based face recognition methods, Eigen Face Based, Line Based, Elastic Bunch Graph method and Neural Network based methods. All have their possibilities and features. In Neural Network approach automatic detection of eyes and mouth is followed by a spatial normalization of the images. The classification of the normalized images is carried out by hybrid Neural Network that combines unsupervised and supervised methods for finding structures and reducing classification errors respectively. The line-based is a type of image-based approach. It does not use any detailed biometric knowledge of the human face. These techniques use either the pixel-based bi-dimensional array representation of the entire face image or a set of transformed images or template sub-images of facial features as the image representation. An image-based metric such as correlation is then used to match the resulting image with the set of model images. In the context of image-based techniques, two approaches are there namely template-based and neural networks. In the template-based approach, the face is represented as a set of templates of the major facial features, which are then matched with the prototypical model face templates. Neural network-based image techniques use an input image representation that is the gray-level pixel-based image or transformed image which is used as an input to one of a variety of neural network architectures, including multilayer, radial basis functions and auto-associative networks.