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 address the problem of automatically generating human-like descriptions for unseen images, given a collection of images and their corresponding human-generated descriptions. Previous attempts for this task mostly rely on visual clues and corpus statistics, but do not take much advantage of the semantic information inherent in the available image descriptions. Here, we present a generic method which benefits from all these three sources (i.e. visual clues, corpus statistics and available descriptions) simultaneously, and is capable of constructing novel descriptions. Our approach works on syntactically and linguistically motivated phrases extracted from the human descriptions. Experimental evaluations demonstrate that our formulation mostly generates lucid and semantically correct descriptions, and significantly outperforms the previous methods on automatic evaluation metrics. One of the significant advantages of our approach is that we can generate multiple interesting descriptions for an image. Unlike any previous work, we also test the applicability of our method on a large dataset containing complex images with rich descriptions. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

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Proceedings of the National Conference on Artificial Intelligence

Choosing linguistics over vision to describe images

Text line segmentation is a basic step in any OCR system. Its failure deteriorates the performance of OCR engines. This is especially true for the Indian languages due to the nature of scripts. Many segmentation algorithms are proposed in literature. Often these algorithms fail to adapt dynamically to a given page and thus tend to yield poor segmentation for some specific regions or some specific pages. In this work we design a text line segmentation post processor which automatically localizes and corrects the segmentation errors. The proposed segmentation post processor, which works in a "learning by examples" framework, is not only independent to segmentation algorithms but also robust to the diversity of scanned pages. We show over 5% improvement in text line segmentation on a large dataset of scanned pages for multiple Indian languages. © 2012 ACM.

ACM International Conference Proceeding Series

Automatic localization and correction of line segmentation errors

In this paper we present Shiksha - an integrated architecture which incorporates handwritten illustrations captured and rendered in a temporal fashion synchronized with audio and video data. The architecture of Shiksha permits non-linear growth in the form of multiple hierarchically organized play streams. We have developed an asynchronous multimedia conferencing application in which the users are provided with an authoring and rendering environment to record and view lectures. It also allows the users to ask and reply to doubts in the previously stored lectures making it a fully interactive but asynchronous system. © 2012 Springer-Verlag.

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

Shiksha: A novel architecture for tele-teaching using handwriting as a perceptually significant temporal media

Iris recognition has been used mainly to recognize cooperative subjects in controlled environments. With the continuing improvements in iris matching performance and reduction in the cost of iris scanners, the technology will witness broader applications and may be confronted with newer challenges. In this research, we have investigated one such challenge, namely matching iris images captured before and after alcohol consumption. Due to alcohol consumption, the pupil dilates/constricts which causes deformation in iris pattern, possibly affecting iris recognition performance. The experiments performed on the IIITD Iris Under Alcohol Influence database show that in matching pre and post alcohol consumption images, the overlap between genuine and impostor match score distributions increases by approximately 20%. These results on a relatively small database suggest that about one in five subjects under alcohol influence may be able to evade identification by iris recognition. © 2012 IEEE.

Proceedings - 2012 5th IAPR International Conference on Biometrics, ICB 2012

Iris recognition under alcohol influence: A preliminary study

Face recognition systems, trained in controlled environment, often fail to efficiently match low resolution images with high resolution images. In this research, a co-transfer learning framework is proposed in which knowledge learnt in controlled high resolution environment is transferred for matching low resolution probe images with high resolution gallery. The proposed framework seamlessly combines transfer learning and co-training to perform knowledge transfer by updating classifier's decision boundary with low resolution probe instances. Experiments are performed on the CMU-Multi-PIE and SCface database with gallery images of size 72 × 72 and size of probe images varying from 48 × 48 to 16 × 16. The results show that, in terms of rank-1 identification accuracy, the proposed algorithm outperforms existing approaches by at least 5%. © 2012 IEEE.

Proceedings - International Conference on Image Processing, ICIP

Matching cross-resolution face images using co-transfer learning

Establishing kinship using images can be utilized as context information in different applications including face recognition. However, the process of automatically detecting kinship in facial images is a challenging and relatively less explored task. The reason for this includes limited availability of datasets as well as the inherent variations amongst kins. This paper presents a kinship classification algorithm that uses the local description of the pre-processed Weber face image. A kinship database is also prepared that contains images pertaining to 272 kin pairs. The database includes images of celebrities (and their kins) and has four ethnicity groups and seven kinship groups. The proposed algorithm outperforms an existing algorithm and yields a classification accuracy of 75.2%. © 2012 IEEE.

2012 IEEE 5th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2012

Self-similarity representation of Weber faces for kinship classification

\ldots} should use English language during lectures although 55% says that both languages (English and Hindi {\ldots} up to-date and ready to put in their best in their job of teaching {\ldots} relationship is important dimension and class interaction can help to develop proper perception of each {\ldots

International Journal of Pharmacy Teaching & Practice

A Study on Student ' s Perception of Ideal Teacher : A Survey on Students of Pharmacy Colleges in Punjab

In this paper we present an algorithm to detect text on video frames consisting of lecture slides. We begin by performing a multi-channel wavelet transform and then merge the channel components for the high frequency sub bands to obtain a composite energy map. Thresholding the energy map results in an edge map consisting of candidate text pixels - some of these correspond to actual text and others correspond to graphics, logo, tables, etc. The connected components in the edge map are then filtered to reject some of the false positives using a trained classifier. Rectangular text blocks compactly surrounding the text regions are then identified using a process of selective dilation and recursive splitting. False positive text blocks still remaining are then rejected using heuristics. Experiments conducted on 890 images show that our scheme has lower false positive rate and misdetection rate when compared with two existing scene text detection methods. © 2012 ICPR Org Committee.

Proceedings - International Conference on Pattern Recognition

Text detection on camera acquired document images using supervised classification of connected components in wavelet domain

This paper presents a new approach to detect tabular structures present in document images and in low resolution video images. The algorithm for table detection is based on identifying the unique table start pattern and table trailer pattern. We have formulated perceptual attributes to characterize the patterns. The performance of our table detection system is tested on a set of document images picked from UW-III (University of Washington) dataset, UNLV dataset, video images of NPTEL videos, and our own dataset. Our approach demonstrates improved detection for different types of table layouts, with or without ruling lines. We have obtained correct table localization on pages with multiple tables aligned side-by-side. © 2012 ACM.

Table detection in document images using header and trailer patterns

MRF models have shown state-of-the-art performance for many computer vision tasks. In this work, we propose a non-local MRF model for image completion problem. The goal of image completion is to fill user specified "target" region with patches of "source" regions in a way that is visually plausible to an observer. We represent the patches in the target region of the image as random variables in an MRF, and introduce a novel energy function on these variables. Each variable takes a label from a label set which is a collection of patches of the source region. The quality of the image completion is determined by the value of the energy function. The non-locality in the MRF is achieved through long range pairwise potentials. These long range pairwise potentials are defined to capture the inherent repeating patterns present in heritage architectural images. We minimize this energy function using Belief Propagation to obtain globally optimal image completion. We have tested our method on a wide variety of images and shown superior performance over previously published results for this task. © 2012 ACM.