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

Image registration is an essential step in many computer vision applications which demands high accuracy for significantly random and complex deformations. In medical image processing applications image registration is a basic preprocessing step to non-rigidly align images from different acquisition environments to an atlas image. We propose a novel non-rigid image registration method to get more reliable registration even under noisy conditions with manageable time complexity. The proposed method explores the the inherent multi-resolution capability of wavelets to perform nonrigid registration in a graph environment. The inherent time complexity of wavelet feature map calculation is avoided using Chebyshev Polynomial approximations for the wavelet operators. © 2018 ACM.

World University root node

ACM International Conference Proceeding Series

Non-rigid Registration using Spectral Graph Wavelet Features

Automatic image annotation aims at predicting a set of semantic labels for an image. Because of large annotation vocabulary, there exist large variations in the number of images corresponding to different labels (“class-imbalance”). Additionally, due to the limitations of human annotation, several images are not annotated with all the relevant labels (“incomplete-labelling”). These two issues affect the performance of most of the existing image annotation models. In this work, we propose 2-pass k-nearest neighbour (2PKNN) algorithm. It is a two-step variant of the classical k-nearest neighbour algorithm, that tries to address these issues in the image annotation task. The first step of 2PKNN uses “image-to-label” similarities, while the second step uses “image-to-image” similarities, thus combining the benefits of both. We also propose a metric learning framework over 2PKNN. This is done in a large margin set-up by generalizing a well-known (single-label) classification metric learning algorithm for multi-label data. In addition to the features provided by Guillaumin et al. (2009) that are used by almost all the recent image annotation methods, we benchmark using new features that include features extracted from a generic convolutional neural network model and those computed using modern encoding techniques. We also learn linear and kernelized cross-modal embeddings over different feature combinations to reduce semantic gap between visual features and textual labels. Extensive evaluations on four image annotation datasets (Corel-5K, ESP-Game, IAPR-TC12 and MIRFlickr-25K) demonstrate that our method achieves promising results, and establishes a new state-of-the-art on the prevailing image annotation datasets. © 2016, Springer Science+Business Media New York.

International Journal of Computer Vision

Image Annotation by Propagating Labels from Semantic Neighbourhoods

In this paper, we present a comprehensive methodology to support IoT based semantic traffic monitoring in smart cities to derive the status of traffic on a road segment in order to optimize traffic flow during rush hours. Smart traffic monitoring involves real-time traffic scene analysis from CCTV cameras to detect and identify stalled vehicles and measure traffic density on roads. The scene analysis essentially necessitates tracking and counting the number of moving and stopped vehicles as they progress along the image sequence by utilizing computer vision techniques. The obtained features along with manually annotated traffic data are translated to a higher level information using semantic web technologies, thus providing a common platform to exchange the information. As the perceptual modelling of traffic data involves media patterns so we have employed Multimedia Web Ontology Language (MOWL) for the semantic interpretation and to tackle inherent uncertainties involved in these media documents. MOWL supports hierarchical probabilistic reasoning framework and utilizes time-varying Dynamic Bayesian Networks (DBN) to perform semantic comparison and predict the evolution of changing situations. In this paper, we discuss the key tasks of vision and probabilistic reasoning components that provide a feasible solution to identify the cause of traffic jam. The proposed approach can be used to trigger an automatic warning or alerts to traffic authorities in case any anomalous activity is encountered and guide users to take some different route to avoid jam. The experimental results shows effectiveness of real-time vehicle monitoring to assess congestion on road and offer user an assistive environment to operate. © 2017 IEEE.

TENSYMP 2017 - IEEE International Symposium on Technologies for Smart Cities

An ontology-driven context aware framework for smart traffic monitoring

Despeckling of ultrasound images is essential for subsequent computational analysis. In this paper, an edge aware geometric filter (GF) is proposed for speckle reduction. The behaviour of conventional GF is approximated using commonly used functions like unit step. These approximations help in identifying the natural relationship between GF and other existing spatially adaptive filters. Subsequently, the modifications in GF framework are proposed to take the advantage of edge characteristics. The proposed filter requires almost no parameter tuning and provides good quality outputs for synthetic as well as real ultrasound images. It is compared with the state-of-the-art speckle reducing filters. Improvements of 10.46% and 42% are noticed in mean square error and figure of merit, respectively. © Springer International Publishing AG 2017.

Communications in Computer and Information Science

Edge aware geometric filter for ultrasound image enhancement

is paper exhibits a novel context-Aware service framework for IoT based Smart Traffic Management using ontology to regulate smooth traffic flow in smart cities by analyzing real-Time traffic environment. The proposed approach makes smarter use of transport networks to achieve objectives related to performance of transport system. This requires efficient traffic planning measures which relate to the actions designed to adjust the demand and capacity of the network in time and space by use of IoT technologies,e adoption of sensors and IoT devices in Smart Traffic System helps to capture the user's preferences and context information which can be in the form of travel time, weather conditions or real-life driving pa.erns. We have employed multimedia ontology to derive higher level descriptions of traffic conditions and vehicles from perceptual observation of traffic information which provides important grounds for our proposed IoT framework. The multimedia ontology encoded in Multimedia Web Ontology Language(MOWL) helps to de€ne classes, properties, and structure of a possible traffic environment to provide insights across the transportation network. MOWL supports Dynamic Bayesian networks (DBN) to deal with time-series data and uncertainties linked with context observations which €ts the de€nition of an intelligent IoT system. Thus, our proposed smart tra?c framework aggregates information corresponding to traffic domain such as tra?c videos captured using CCTV cameras and allows automatic prediction of dynamically changing situations which helps to make tra?c authorities more responsive. We have illustrated use of our approach by utilizing contextual information, to assess real-Time congestion situation on roads thus allowing to visualize planning services. Once the congestion situation is predicted, alternate congestion free routes which are in accordance with the coveted criteria are suggested that can be propagated through text-messages or e-mails to the users. © 2017 ACM.

Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017

An IoT approach for context-Aware smart tra€ic management using ontology

This paper introduces a novel end-to-end deep learning framework to learn space-time super-resolution (SR) process. We propose a coupled deep convolutional auto-encoder (CDCA) which learns the non-linear mapping between convolutional features of up-sampled low-resolution (LR) video sequence patches and convolutional features of high-resolution (HR) video sequence patches. The upsampling in LR video refers to tri-cubic interpolation both in space and time. We also propose a H.264/AVC compatible video space-time SR framework by using learned CDCA, which enables to super-resolve compressed LR video with less computational complexity. The experimental results prove that the proposed H.264/AVC compatible framework performs better than the state-of-art techniques on space-time SR in terms of quality and time complexity. © 2017, Springer International Publishing AG.

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

Space-Time Super-Resolution Using Deep Learning Based Framework

In today’s competitive telecommunication industry understanding the causes that influence the revenue is of importance. In a continuously evolving business environment, the causes that influence the revenue keeps changing. To understand and quantify the effect of different factors we model it as a non-stationary temporal causal network. To handle the massive volume of data, we propose a novel framework as part of which we define rules to identify the concept drift and propose an incremental algorithm for learning non-stationary temporal causal structure from streaming data. We apply the framework on a telecommunication operator’s data and the framework detects the concept drift related to changes in revenue associated with data usage and the incremental causal network learning algorithm updates the knowledge accordingly. © 2017, Springer International Publishing AG.

Incremental Learning of Non-stationary Temporal Causal Networks for Telecommunication Domain

Automated video surveillance is a rapidly evolving area and has been gaining importance in the research community in recent years due to its capabilities of performing more efficient and effective surveillance by employing smart cameras. In this article, we present the design and implementation of an FPGA-based smart camera system for automated video surveillance. The complete system is prototyped on Xilinx ML510 FPGA platform and meets the real-time requirements of video surveillance applications while aiming at FPGA resource reduction. The implemented smart camera system is capable of automatically performing real-time motion detection, real-time video history generation, real-time focused region extraction, real-time filtering of frames of interest, and real-time object tracking of identified target with automatic purposive camera movement. The system is designed to work in real-time for live color video streams of standard PAL (720×576) resolution, which is the most commonly used video resolution for current generation surveillance systems. The implemented smart camera system is also capable of processing HD resolution video streams in real-time. © Springer Nature Singapore Pte Ltd 2017.

FPGA-based smart camera system for real-time automated video surveillance

Bin picking using vision based sensors requires accurate estimation of location and pose of the object for positioning the end effector of the robotic arm. The computational burden and complexity depends upon the parametric model adopted for the task. Learning based techniques to implement the scheme using low dimensional manifolds offer computationally more efficient alternatives. In this paper we have employed Locally Linear Embedding (LLE) and Deep Learning (with auto encoders) for manifold learning in the visual domain as well as for the parameters of robotic manipulator for visual servoing. Images of clusters of cylindrical pellets were used as the training data set in the visual domain. Corresponding parameters of the six degrees of freedom robot for picking designated cylindrical pellet formed the training dataset in the robotic configuration space. The correspondence between the weight coefficients of LLE manifold in the visual domain and robotic domain is established through regression. Autoencoders in conjunction with feed forward neural networks were used for learning of correspondence between the high dimensional visual space and low dimensional configuration space. We have compared the results of the two implementations for the same dataset and found that manifold learning using auto encoders resulted in better performance. The eye-in-hand configuration used with KUKA KR5 robotic arm and Basler camera offers a potentially effective and efficient solution to the bin picking problem through learning based visual servoing. © Springer Science+Business Media Singapore 2017.

Advances in Intelligent Systems and Computing

Bin picking using manifold learning

Autoencoders are deep learning architectures that learn feature representation by minimizing the reconstruction error. Using an autoencoder as baseline, this paper presents a novel formulation for a class sparsity based supervised encoder, termed as CSSE. We postulate that features from the same class will have a common sparsity pattern/support in the latent space. Therefore, in the formulation of the autoencoder, a supervision penalty is introduced as a joint-sparsity promoting l2,1-norm. The formulation of CSSE is derived for a single hidden layer and it is applied for multiple hidden layers using a greedy layer-by-layer learning approach. The proposed CSSE approach is applied for learning face representation and verification experiments are performed on the LFW and PaSC face databases. The experiments show that the proposed approach yields improved results compared to autoencoders and comparable results with state-of-the-art face recognition algorithms. © 2016 IEEE.