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 work, we propose an image analogy based super-resolution technique that can be used as an effective tool for document image compression and multi-resolution viewing of the document. The technique uses Dugad and Ahuja method for resizing document images. Next, image analogies framework is applied to add the missing high frequency information. The encoder allows user to compress spatially lower resolution version of the image using any standard image compression technique, thus enabling substantial compression. At the decoder end, the image is resized using Dugad and Ahuja method and then enhanced using image analogy by appending the missing high frequency details, using a training pair of the same class of document image. © 2011 IEEE.

World University root node

Proceedings - 2011 3rd National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, NCVPRIPG 2011

Image analogy based document image compression

The estimation capacities of two optimization methodologies, response surface methodology (RSM) and artificial neural network (ANN) were evaluated for prediction of biosorptive remediation of As(III) and As(V) species in batch as well as column mode. The independent parameters (viz. pH, initial arsenic concentration, temperature, and biomass dose in the case of batch mode and bed height, flow rate, and initial arsenic concentration in the case of column mode) were fed as input to the central composite design (CCD) of RSM and the ANN techniques, and the output was the uptake capacity of the sorbent. The CCD was used to evaluate the simple and combined effects of the independent parameters and to derive a second-order regression equation for predicting optimization of the process. The sets of input-output patterns were also used to train the multilayer feed-forward networks employing the backpropagation algorithm with MATLAB. The application of the RSM and ANN techniques to the available experimental data showed that ANN outperforms RSM indicating the superiority of a properly trained ANN over RSM in capturing the nonlinear behavior of the system and the simultaneous prediction of the output. © 2011 American Chemical Society.

Industrial and Engineering Chemistry Research

Bioadsorption of arsenic: An artificial neural networks and response surface methodological approach

One of the major challenges of face recognition is to design a feature extractor and matcher that reduces the intraclass variations and increases the inter-class variations. The feature extraction algorithm has to be robust enough to extract similar features for a particular subject despite variations in quality, pose, illumination, expression, aging, and disguise. The problem is exacerbated when there are two individuals with lower inter-class variations, i.e., look-alikes. In such cases, the intra-class similarity is higher than the inter-class variation for these two individuals. This research explores the problem of look-alike faces and their effect on human performance and automatic face recognition algorithms. There is three fold contribution in this research: firstly, we analyze the human recognition capabilities for look-alike appearances. Secondly, we compare human recognition performance with ten existing face recognition algorithms, and finally, proposed an algorithm to improve the face verification accuracy. The analysis shows that neither humans nor automatic face recognition algorithms are efficient in recognizing look-alikes. © 2011 IEEE.

2011 International Joint Conference on Biometrics, IJCB 2011

Face recognition for look-alikes: A preliminary study

The paper addresses the problem of finding top k influential nodes in large scale directed social networks. We propose a centrality measure for independent cascade model, which is based on diffusion probability (or propagation probability) and degree centrality. We use (i) centrality based heuristics with the proposed centrality measure to get k influential individuals. We have also found the same using (ii) high degree heuristics and (iii) degree discount heuristics. A Monte-Carlo simulation has been conducted with top k-nodes found through different methods. The result of simulation indicates, k nodes obtained through (i) significantly outperform those obtain by (ii) and (iii). We further verify the differences statistically using T-Test and found the minimum significance level (p-value) when k∈>∈5 is 0.022 compare with (ii) and 0.015 when comparing with (iii) for twitter data. © 2011 Springer-Verlag Berlin Heidelberg.

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

A new centrality measure for influence maximization in social networks

The main challenge in recognizing handwritten characters is to handle large-scale shape variations in the handwriting of different individuals. In this paper, we present a novel handwritten character recognition method based on the structural shape of a character irrespective of the viewing direction on the 2D plane. Structural shape of a character is described by different skeletal convexities of character strokes. Such skeletal convexity acts as an invariant feature for character recognition. Longest common subsequence matching is used for recognition. We have tested out method on a benchmark dataset of handwritten Bengali character images. Preliminary results demonstrate the efficacy of our approach. © 2011 IEEE.

Proceedings - 2nd International Conference on Emerging Applications of Information Technology, EAIT 2011

Recognition of Bengali handwritten characters using skeletal convexity and dynamic programming

In this paper we propose a thinning methodology applicable to character images. It is novel in terms of its ability to adapt to local character shape while constructing the thinned skeleton. Our method does not produce many of the distortions in the character shapes which normally result from the use of existing thinning algorithms. The proposed thinning methodology is based on the medial axis of the character. The skeleton has a width of one pixel. As a by-product of our thinning approach, the skeleton also gets segmented into strokes in vector form. Hence further stroke segmentation is not required. We have conducted experiments with printed and handwritten characters in several scripts such as English, Bengali, Hindi, Kannada and Tamil. We obtain less spurious branches compared to other thinning methods. Our method does not use any kind of post processing. © 2011 World Scientific Publishing Company.

International Journal of Pattern Recognition and Artificial Intelligence

Skeletonizing character images using a modified medial axis-based strategy

In this paper, we present novel features based on the topography of a character as visible from different viewing directions on a 2D plane. By topography of a character we mean the structural features of the strokes and their spatial relations. In this work we develop topographic features of strokes visible with respect to views from different directions (e.g. North, South, East, and West). We consider three types of topographic features: closed region, convexity of strokes, and straight line strokes. We have tested the proposed method on printed and handwritten Bengali and Hindi isolated character images. Initial results demonstrate the efficacy of our approach. © Springer-Verlag Berlin Heidelberg 2011.

Communications in Computer and Information Science

A novel topographic feature extraction method for indian character images

3D Shape matching is an important problem in computer vision. One of the major difficulties in finding dense correspondences between 3D shapes is related to the topological discrepancies that often arise due to complex kinematic motions. In this paper we propose a shape matching method that is robust to such changes in topology. The algorithm starts from a sparse set of seed matches and outputs dense matching. We propose to use a shape descriptor based on properties of the heat-kernel and which provides an intrinsic scale-space representation. This descriptor incorporates (i) heat-flow from already matched points and (ii) self diffusion. At small scales the descriptor behaves locally and hence it is robust to global changes in topology. Therefore, it can be used to build a vertex-to-vertex matching score conditioned by an initial correspondence set. This score is then used to iteratively add new correspondences based on a novel seed-growing method that iteratively propagates the seed correspondences to nearby vertices. The matching is farther densified via an EM-like method that explores the congruency between the two shape embeddings. Our method is compared with two recently proposed algorithms and we show that we can deal with substantial topological differences between the two shapes. © 2011 IEEE.

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Topologically-robust 3D shape matching based on diffusion geometry and seed growing

We present a video compression scheme using epitome based texture coding that uses a low quality video as side information and improves it by using the epitome. The side information is sent at different levels of quantization and resolution to optimize the quality against the bit rate. The concept of motion threading is used for propagation of epitome information from one frame to another. The proposed scheme shows promising results for low bit rate side information case because of the reduction in the level of blurring due to the introduction of high frequency information derived from the epitome. © 2011 IEEE.

Parametric video compresssion using epitome based texture synthesis

Script identification in a multi-lingual document environ- ment has numerous applications in the field of document image analysis, such as indexing and retrieval or as an initial step towards optical character recognition. In this paper, we propose a novel hierarchical framework for script identification in bi-lingual documents. The framework presents a top-down approach by performing page, block/paragraph and word level script identification in multiple stages. We utilize texture and shape based information embedded in the documents at different levels for feature extraction. The prediction task at different levels of hierarchy is performed by Support Vector Machine (SVM) and Rejection based classifier defined using AdaBoost. Experimental evaluation of the proposed concept on document collections of Hindi/English and Bangla/English scripts have shown promising results. Copyright © 2011 ACM.