Department of Computer Science & Engineering

&ldquo;IIT Jodhpur is committed to impart quality education to facilitate the creation of qualified, competent and responsible human resources to meet the emerging technological challenges of the world. It is working towards creating an equitable learning platform that is positive, accessible and effective in the face of the modern societal and scientific advancements.

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

The Physically Unclonable Functions (PUFs) are used in numerous security applications such as device authentication, secret key generation, FPGA intellectual property (IP) protection, and trusted computing. In this paper, compact implementations of Ring oscillator-based PUF (RO-PUF), Arbiter-based PUF (A-PUF) and RS Latch-based PUF (RS-LPUF) on an FPGA (Field Programmable Gate Array) platform are presented. The proposed schemes provide very competitive area trade-offs and effectively enable smallest FPGA implementations, reported so far, of RS-LPUF, RO-PUF, and A-PUF respectively. The designs have been validated by developing prototypes on Xilinx Spartan-6 FPGAs at core voltage of 1.2V and normal operating temperature. Finally, a detailed comparison of statistical analysis on their performance using measured PUF data have been carried out. It has been demonstrated that the proposed designs exhibit significantly improved performance in terms of statistical properties when compared to the existing works. © 2016 IEEE.

World University root node

Proceedings - 2017 30th International Conference on VLSI Design and 2017 16th International Conference on Embedded Systems, VLSID 2017

Compact Implementations of FPGA-based PUFs with Enhanced Performance

This paper considers the problem of finding pose matches between trajectories of multiple robots in their respective coordinate frames or equivalent matches between trajectories obtained during different sessions. Pose correspondences between trajectories are mediated by common landmarks represented in a topological map lacking distinct metric coordinates. Despite such lack of explicit metric level associations, we mine preliminary pose level correspondences between trajectories through a novel multi-scale heat-kernel descriptor and correspondence graph framework. These serve as an improved initialization for ICP (Iterative Closest Point) to yield dense pose correspondences. We perform extensive analysis of the proposed method under varying levels of pose and landmark noise and showcase its superiority in obtaining pose matches in comparison with standard ICP like methods. To the best of our knowledge, this is the first work of the kind that brings in elements from spectral graph theory to solve the problem of pose correspondences in a multi-robotic setting and differentiates itself from other works. © 2017 IEEE.

IEEE International Conference on Intelligent Robots and Systems

Multi-trajectory pose correspondences using scale-dependent topological analysis of pose-graphs

Object Tracking has primarily been characterized as the study of object motion trajectory over constraint subspaces under attempts to mimic human efficiency. However, the trend of monotonically increasing applicability and integrated relevance over distributed commercial frontiers necessitates that scalability be addressed. The present work proposes a system for fast large scale facial tracking over distributed systems beyond individual human capabilities leveraging the computational prowess of large scale processing engines such as Apache Spark. The system is pivoted on an interval based approach for receiving the input feed streams, which is followed by a deep encoder-decoder network for generation of robust environment invariant feature encoding. The system performance is analyzed while functionally varying various pipeline components, to highlight the robustness of the vector representations and near real-time processing performance. © 2017, Springer International Publishing AG.

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

Unsupervised Feature Descriptors Based Facial Tracking over Distributed Geospatial Subspaces

Our paper is motivated from the advancement in deep learning algorithms for various computer vision problems. We are proposing a novel end-to-end deep learning based framework for image super-resolution. This framework simultaneously calculates the convolutional features of low-resolution (LR) and high-resolution (HR) image patches and learns the non-linear function that maps these convolutional features of LR image patches to their corresponding HR image patches convolutional features. Here, proposed deep learning based image super-resolution architecture is termed as coupled deep convolutional auto-encoder (CDCA) which provides state-of-the-art results. Super-resolution of a noisy/distorted LR images results in noisy/distorted HR images, as super-resolution process gives rise to spatial correlation in the noise, and further, it cannot be de-noised successfully. Traditional noise resilient image super-resolution methods utilize a de-noising algorithm prior to super-resolution but de-noising process gives rise to loss of some high-frequency information (edges and texture details) and super-resolution of the resultant image provides HR image with missing edges and texture information. We are also proposing a novel end-to-end deep learning based framework to obtain noise resilient image super-resolution. Proposed end-to-end deep learning based framework for noise resilient super-resolution simultaneously perform image de-noising and super-resolution as well as preserves textural details. First, stacked sparse de-noising auto-encoder (SSDA) was learned for LR image de-noising and proposed CDCA was learned for image superresolution. Then, both image de-noising and super-resolution networks were cascaded. This cascaded deep learning network was employed as one integral network where pre-trained weights were serving as initial weights. The integral network was end-to-end trained or fine-tuned on a database having noisy, LR image as an input and target as an HR image. In fine-tuning, all layers of the combined end-to-end network was jointly optimized to perform image de-noising and super-resolution simultaneously. Experimental results show that proposed noise resilient super-resolution framework outperforms the conventional and state-of-the-art approaches in terms of PSNR and SSIM metrics. © 2017 IEEE.

Proceedings of the International Joint Conference on Neural Networks

Deep learning based frameworks for image super-resolution and noise-resilient super-resolution

The depth cues from multiple images are useful in accurate depth extraction while monocular cues from single still image are more versatile. In our paper, monocular cue which gives useful information about single frame and depth from motion using optical flow estimated from consecutive video frames are used to produce final depth maps. The machine learning approach is promising and new research direction in the field of depth estimation and thus 2-D to 3-D conversion. A fast automatic technique is proposed which utilizes a fixed point learning framework for the accurate estimation of depth maps of test images. For this task, a contextual prediction function is generated using training database of 2-D color and ground truth depth images. The depth maps obtained from monocular and motion depth cues of input video frames are used as input features for learning process. The depths generated from fixed point model are more accurate and reliable than MRF fusion of these depth cues. The stereo pairs are generated using depth maps predicted from fixed point learning. These final stereo pairs are converted to 3-D output video which is displayed on 3-DTV. For subjective evaluation, MOS score is calculated by showing final 3-D video to different viewers using 3-D glasses. © 2016 IEEE.

Proceedings of 9th International Conference on Electrical and Computer Engineering, ICECE 2016

2-D to 3-D Conversion of videos using fixed point learning approach

With the objective of creating an interface for experimenting with electronic circuits embedded in documents or images, in this paper we have presented a system for parsing and understanding of electronic circuit diagrams. The developed system consists of following steps- symbol extraction, symbol recognition, optimization and netlistrepresentation. Firstly, symbols are extracted from the image by removing text and connection lines using computer vision techniques. For symbol recognizer a probabilistic-SVM classifier is built using HOG and radon features on training data. A Bayesian framework is used to incorporate domain knowledge information to improve the performance of the probabilistic symbol recognizer. An novel optimization approach based on top-down features is used to remove the errors that occurs in the symbol extraction and recognition task. A depth first traversal algorithm is used to find the connections between the symbols and then image is represented in the form of usable data structure. The system is evaluated on a dataset of 20 analog electronic circuit images collected from various sources and the results are presented. © Springer International Publishing AG 2017.

Recognizing electronic circuits to enrich web documents for electronic simulation

In this work, we propose a novel multimedia ontology based framework to recommend garments to the users for a specific occasion to form a complement outfit pair, which requires dealing with intrinsic subjectivity involved in modeling the domain. The framework automatically derives personal traits of a user such as body color, dimensions from her photograph and garment attributes such as color, pattern from input garment to interpret their respective context. Depending on these contexts, system then recommends outfits that are complementary and satisfactory in terms of reference garment color and user personality using semantic web and image processing techniques. The recommendation model compiles knowledge of clothing in an ontology encoded in Multimedia Web Ontology Language (MOWL) which is capable of describing domain concepts in terms of their media properties and utilizes probabilistic reasoning scheme to reason with them. We have experimented and validated our approach with clothing liking of various models on a collection of garments downloaded from various websites, thus providing an effective garment recommendation interface to the user. © 2017 IEEE.

2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017

Multimedia ontology based complementary garment recommendation

High-throughput identification of digital traits encapsulating the changes in plant’s internal structure under drought stress, based on hyperspectral imaging (HSI) is a challenging task. This is due to the high spectral and spatial resolution of HSI data and lack of labelled data. Therefore, this work proposes a novel framework for phenotypic discovery based on autoencoders, which is trained using Simple Linear Iterative Clustering (SLIC) superpixels. The distinctive archetypes from the learnt digital traits are selected using simplex volume maximisation (SiVM). Their accumulation maps are employed to reveal differential drought responses of wheat cultivars based on t-distributed stochastic neighbour embedding (t-SNE) and the separability is quantified using cluster silhouette index. Unlike prior methods using raw pixels or feature vectors computed by fusing predefined indices as phenotypic traits, our proposed framework shows potential by separating the plant responses into three classes with a finer granularity. This capability shows the potential of our framework for the discovery of data-driven phenotypes to quantify drought stress responses. © 2017, Springer International Publishing AG.

Extraction of Phenotypic Traits for Drought Stress Study Using Hyperspectral Images

Recognizing text from noisy low-resolution (LR) images is extremely challenging and is an open problem for the computer vision community. Super-resolving a noisy LR text image results in noisy High Resolution (HR) text image, as super-resolution (SR) leads to spatial correlation in the noise, and further cannot be de-noised successfully. Traditional noise-resilient text image super-resolution methods utilize a denoising algorithm prior to text SR but denoising process leads to loss of some high frequency details, and the output HR image has missing information (texture details and edges). This paper proposes a noise-resilient SR framework for text images and recognizes the text using a deep BLSTM network trained on high resolution images. The proposed end-To-end deep learning based framework for noise-resilient text image SR simultaneously perform image denoising and super-resolution as well as preserves missing details. Stacked sparse denoising auto-encoder (SSDA) is learned for LR text image denoising, and our proposed coupled deep convolutional auto-encoder (CDCA) is learned for text image super-resolution. The pretrained weights for both these networks serve as initial weights to the end-To-end framework during finetuning, and the network is jointly optimized for both the tasks. We tested on several Indian Language datasets and the OCR performance of the noise-resilient super-resolved images is at par with the original HR images. © 2017 IEEE.

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

A Noise-Resilient Super-Resolution Framework to Boost OCR Performance

The present day computers can outperform the human in many complicated tasks very precisely and efficiently. However, in many scenarios like pattern recognition and more importantly, character recognition; a school going child can outperform the sophisticated machines available today. The modern machines present today find handwritten, calligraphic text difficult to recognize because such texts hardly contain rationalized straight lines or perfect loops or circles. Therefore, most of the optical character recognition systems fail to recognize the characters beyond certain levels of distortions and noise. On the other hand, the human brain has achieved a remarkable ability to recognize visual patterns or characters in various distortion conditions with high speed. The present work tries to understand how human perceive, process and recognize the Devanagari characters under various distortion levels. In order to achieve this objective, eye tracking experiment was performed on 20 graduate participants by presenting stimuli in decreasing level of distortions (from highly distorted to more normal one). The eye fixation patterns along with the time course of recognition gave us the moment-to-moment processing involved in letter identification. Upon understanding the level of distortion acceptable for correct letter recognition and the processes involved in the identification of the letters, the OCR can be made more robust and the gap between human reading and machine reading can be narrowed down. © 2017, Springer International Publishing AG.