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

Ieee Transactions on Image Processing

Improving Cross-Resolution Face Matching Using

This paper investigates the problem of cross document image retrieval, i.e. use of query images from one style (say font) to perform retrieval from a collection which is in a different style (say a different set of books). We present two approaches to tackle this problem. We propose an effective style independent retrieval scheme using a nonlinear style-content separation model. We also propose a semi-supervised style transfer strategy to expand the query into multiple styles. We validate both these approaches on a collection of word images which vary in fonts/styles. © 2014 IEEE.

Proceedings - International Conference on Pattern Recognition

Enhancing word image retrieval in presence of font variations

The paper presents a novel learning-based framework to identify tables from scanned document images. The approach is designed as a structured labeling problem, which learns the layout of the document and labels its various entities as table header, table trailer, table cell and non-table region. We develop features which encode the foreground block characteristics and the contextual information. These features are provided to a fixed point model which learns the inter-relationship between the blocks. The fixed point model attains a contraction mapping and provides a unique label to each block. We compare the results with Condition Random Fields(CRFs). Unlike CRFs, the fixed point model captures the context information in terms of the neighbourhood layout more efficiently. Experiments on the images picked from UW-III (University of Washington) dataset, UNLV dataset and our dataset consisting of document images with multi-column page layout, show the applicability of our algorithm in layout analysis and table detection. Copyright 2014 ACM.

ACM International Conference Proceeding Series

Table extraction from document images using fixed point model

Wireless sensor networks have grown significant attentions among researchers for providing a flexible and low-cost framework to design an architecture for Intelligent Transport Systems. The inherent challenges in distribution and management of sensor networks along the road require an application-specific protocol support for the network connectivity, the sensing coverage, the reliable data forwarding, and the network lifetime improvement. This paper introduces the concept of k-strip length coverage along the road, which ensures a better sensing coverage for the detection of moving vehicles compared with the conventional barrier coverage and full area coverage, in terms of the availability of sufficient information for statistical processing and the number of sensors required to be active. To extend the network lifetime, every sensor follows a sleep-wakeup schedule maintaining the network connectivity and the k-strip length coverage. This scheduling problem is modeled as a graph optimization, the NP-hardness of which motivates to design a centralized heuristic, providing an approximate solution. As a sensor network is inherently distributed in nature, properties of the centralized heuristic are explored to design a per-node solution based on local information. Performance of the proposed scheme is analyzed through simulation results. © 2000-2011 IEEE.

IEEE Transactions on Intelligent Transportation Systems

ADCROSS: Adaptive data collection from road surveilling sensors

Sorting has been a topic of immense research value since the inception of computer science. Hybrid computing on multicore architectures involves computing simultaneously on a tightly coupled heterogeneous collection of devices. In this work, we consider a multicore CPU along with a manycore GPU as our experimental hybrid platform.In this work, we present a hybrid comparison based sorting algorithm which utilizes a many-core GPU and a multi-core CPU to perform sorting. The algorithm is broadly based on splitting the input list according to a large number of splitters followed by creating independent sublists. Sorting the independent sublists results in sorting the entire original list.On a CPU + GPU platform consisting of an Intel i7-980X and an NVidia GTX 580, our algorithm achieves a 20% gain over the current best known comparison sort result that was published by (Davidson et al., 2012). On the above experimental platform, our results are better by 40% on average over a similar GPU-alone algorithm proposed by (Leischner et al., 2010). We also extend our sorting algorithm for fixed length keys to variable length keys. We use a look-ahead based approach to sort strings and obtain around a 24% benefit compared to the current best known solution. Our results also show that our algorithm and its implementation scale with the size of the input. We also show that such performance gains can be obtained on other hybrid CPU + GPU platforms. © The Author(s) 2014.

International Journal of High Performance Computing Applications

Comparison sorting on hybrid multicore architectures for fixed and variable length keys

In this paper, we re-evaluate the security-bound of full round AES against biclique attack. Under some reasonable restrictions, we exhaustively analyze the most promising class of biclique cryptanalysis as applied to AES through a computer-assisted search and find optimal attacks towards lowest computational and data complexities: - Among the attacks with the minimal data complexity of the unicity distance, the ones with computational complexity 2126.67 (for AES- 128), 2190.9 (for AES-192) and 2255 (for AES-256) are the fastest. Each attack just requires 2 (for AES-128 and AES-192) or 3 (for AES-256) known plaintexts for success probability 1. We obtain these results using the improved biclique attack proposed in Crypto’13. – Among the attacks with data complexity less than the full codebook, for AES-128, the ones of computational complexity 2126.16 are fastest. Within these, the one with data complexity 264 requires the smallest amount of data. Thus, the original attack (with data complexity 288) did not have the optimal data complexity for AES-128. Similar findings are observed for AES-192 as well (data complexity 248 as against 280 in the original attack). For AES-256, we find an attack that has a lower computational complexity of 2254.31 as compared to the original attack complexity of 2254.42. – Among all the attacks covered, the ones of computational complexity 2125.56 (for AES-128), 2189.51 (for AES-192) and 2253.87 (for AES-256) are fastest, though requiring the full codebook. This can be considered as an indication of the limitations of the independent biclique attack approach as applied to AES. © Springer International Publishing Switzerland 2015.

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

Bicliques with minimal data and time complexity for AES

First, we describe how 360°-rotational symmetry may be used for three dimensional reconstruction of repeated cylinders from a single perspective image. In our experiments, we consider translational and affine repetition of cylinders with vertical and random orientations. Later, we create a virtual camera configuration for retrieving pose and location of repeated cylinders. The combination of 360°-rotational symmetry and camera center is used to identify two orthogonal planes called axis plane and orthogonal axis plane. These two planes are the basis for the proposed reconstruction framework and virtual camera configuration. Furthermore, we discuss possible extension of our method in vision tasks based on motion analysis. © 2013 IEEE.

2013 4th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, NCVPRIPG 2013

Symmetry based 3D reconstruction of repeated cylinders

In this paper, we explore the use of machine learning for multimedia indexing and retrieval involving single/multiple features. Indexing of large image collection has been well researched problem. However, machine learning for combination of features in image indexing and retrieval framework is not explored. In this context, the paper presents novel formulation of multiple kernel learning in hashing for multimedia indexing. The framework learns combination of multiple features/ modalities for defining composite document indices in genetic algorithm based framework. We have demonstrated the evaluation of framework on dataset of handwritten digit images. Subsequently, the utility of the framework is explored for development for multi-modal retrieval of document images. © 2013 Springer-Verlag.

Communications in Computer and Information Science

Indexing for image retrieval: A machine learning based approach

This paper presents a novel image variety-Based approach that elegantly models the space of a broad class of perspective and non-perspective stereo varieties within a single, unified framework. The basic concept of parameterized variety presented earlier by Genc and Ponce [1] is extended to represent the nonlinear space of images. An efficient algebraic framework is constructed to parameterize the variety associated with full perspective cameras. The algorithm seeks the manifolds that constrain this space of six-dimensional variety to generate compelling multi-perspective 3D effects from arbitrary virtual viewpoints. Combining geometric space of multiple uncalibrated perspective views with appearance space in a globally optimized way leads to numerous potential applications, especially in content creation for multi-perspective 3DTV. The proposed approach works for uncalibrated static/dynamic scenes, containing parallax and unstructured object motion. It even seamlessly deals with images or video sequences that do not share a common origin, thus provides an effective tool for montaging, indexing and virtual navigation. © 2013 IEEE.

Proceedings - 2013 International Conference on 3D Vision, 3DV 2013

Space-time parameterized variety manifolds: A novel approach for arbitrary multi-perspective 3D view generation

Nature offers several examples of self-organizing systems that automatically adjust to changing conditions without adversely affecting the system goals. We propose a self-organizing sensor network that is inspired from real-life systems for sampling a region in an energy-efficient manner. Mobile nodes in our network execute certain rules by processing local information. These rules enable the nodes to divide the sampling task in a manner such that the nodes self-organize themselves to reduce the total power consumed and improve the accuracy with which the phenomena are sampled. The digital hormone-based model that encapsulates these rules, provides a theoretical framework for examining this class of systems. This model has been simulated and implemented on cricket motes. Our results indicate that the model is more effective than a conventional model with a fixed rate sampling. © 2013 Indu Sreedevi et al.