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

This paper strives to answer the following question: Is it possible to recognize an intersection when seen from different road segments that constitute the intersection? An intersection or a junction typically is a meeting point of three or four road segments. Its recognition from a road segment that is transverse to or 180 degrees apart from its previous sighting is an extremely challenging and yet a very relevant problem to be addressed from the point of view of both autonomous driving as well as loop detection. This paper formulates this as a problem of video recognition and proposes a novel LSTM based Siamese style deep network for video recognition. For what is indeed a challenging problem and the limited annotated dataset available we show competitive results of recognizing intersections when approached from diverse viewpoints or road segments. Specifically, we tabulate effective recognition accuracy even as the approaches to the intersection being compared are disparate both in terms of viewpoints and weather/illumination conditions. We show competitive results on both synthetic yet highly realistic data mined from the gaming platform GTA as well as on real world data made available through Mapillary. © 2018 IEEE.

World University root node

IEEE International Conference on Intelligent Robots and Systems

Towards View-Invariant Intersection Recognition from Videos using Deep Network Ensembles

The List Colouring Problem (LCP) is an NP-Hard optimization problem in which the goal is to find a proper list colouring of a graph G=(V, E) such that the number of colours used is minimized. This paper deals with the development and implementation of a Genetic Algorithm for the list colouring of graphs. Initial solutions are generated using two heuristics in equal proportion. Experiments have been carried out on randomly generated planar graphs with a list of colours for each vertex. Results for 32 graphs with size varying from 10 to 750 show that the proposed Genetic Algorithm implementation tends towards lower values of the number of colours used. © Springer Nature Singapore Pte Ltd. 2018.

Smart Innovation, Systems and Technologies

List colouring of graphs using a genetic algorithm

This book presents a unique guide to heritage preservation problems and the corresponding state-of-the-art digital techniques to achieve their plausible solutions. It covers various methods, ranging from data acquisition and digital imaging to computational methods for reconstructing the original (pre-damaged) appearance of heritage artefacts.The case studies presented here are mostly drawn from India's tangible and non-tangible heritage, which is very rich and multi-dimensional. The contributing authors have been working in their respective fields for years and present their methods so lucidly that they can be easily reproduced and implemented by general practitioners of heritage curation. The preservation methods, reconstruction methods, and corresponding results are all illustrated with a wealth of colour figures and images.The book consists of sixteen chapters that are divided into five broad sections, namely (i) Digital System for Heritage Preservation, (ii) Signal and Image Processing, (iii) Audio and Video Processing, (iv) Image and Video Database, and (v) Architectural Modelling and Visualization. The first section presents various state-of-the-art tools and technologies for data acquisition including an interactive graphical user interface (GUI) annotation tool and a specialized imaging system for generating the realistic visual forms of the artefacts. Numerous useful methods and algorithms for processing vocal, visual and tactile signals related to heritage preservation are presented in the second and third sections. In turn, the fourth section provides two important image and video databases, catering to members of the computer vision community with an interest in the domain of digital heritage. Finally, examples of reconstructing ruined monuments on the basis of historic documents are presented in the fifth section. In essence, this book offers a pragmatic appraisal of the uses of digital technology in the various aspects of preservation of tangible and intangible heritages. © Springer Nature Singapore Pte Ltd. 2018. All Rights Reserved.

Heritage Preservation: A Computational Approach

Heritage preservation: A computational approach

In this world of digitization, screen reading has grown immensely due to the availability of affordable display devices. Most of the people prefer to read on display devices as compared to the print media. To make the reading experience of the reader pleasant and comfortable, the font designers strive hard to choose suitable typographical properties of the text such as font type, font size etc. Some of the researchers suggest that the typography of the text affects the reading performance of the readers to some extent. However, the research focusing on the effect of typography on the reading behavior of the readers is limited and it is hardly touched upon for the Indian scripts. Therefore, the proposed paper aims to find out the effect of Devanagari font type on the reading performance, especially reading comprehension of the readers. In addition to this, a method to reduce the error in the gaze estimation of the eye tracker is also proposed. In order to understand the reading behavior, an eye tracking experiment is performed on 14 participants asking them to read 22 pages, in 3 different font types, presented on the screen of the eye tracker. The performance of the readers is analyzed in terms of total reading time, comprehension score, number of fixations, fixation duration and number of regressions. Our results show that there is a significant difference in the fixation duration, a number of fixations and the comprehension score, when the same document is read in different font type. Thus, there is a scope for improvement in the reading comprehension, by changing the physical properties of the document without changing its content. These findings might be useful to understand the readers’ preference for the font and to design a proper font type for online reading. © Springer Nature Switzerland AG 2018.

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

Effect of Devanagari font type in reading comprehension: An eye tracking study

Drought is one of the severe abiotic stress that affects the productivity of rice, an important staple crop that is consumed all over the world. The traits responsible for enhancing or adapting drought resistance in rice plants can be selected and studied to improve their growth under stress conditions. Experiments have been conducted on indica rice varieties comprising Sahabhagidhan as drought tolerant variety and IR64, MTU1010 categorized as drought sensitive varieties. Various root related biochemical and morphological traits such as root length, relative water content (RWC), xylem number, xylem area, proline content, and malondialdehyde content have been investigated for a comparative study of the plant response to drought stress in different rice varieties. The results of differential root transcriptome analysis have revealed that there is a notable difference in gene expression of OsPIP2;5 and OsNIP2;1 in various indica varieties of rice at different time periods of stress. The present work aims at assessing the correlation between genotypic and phenotypic traits that can contribute towards the emerging field of rice phenomics. © 2018 Elsevier Masson SAS

Plant Physiology and Biochemistry

Assessing the correlation of genotypic and phenotypic responses of indica rice varieties under drought stress

In this article, a local change detection technique for underwater video sequences is proposed to detect the positions of the moving objects. The proposed change detection scheme integrates the Mixture of Gaussian (MoG) process in a Wronskian framework. It uses spatiotemporal modes (an integration of spatio-contextual and temporal modes) arising over the underwater video sequences to detect the local changes. The Wronskian framework takes care of the spatio-contextual modes whereas MoG models the temporal modes arising due to inter-dependency of a pixel in a video. The proposed scheme follows two steps: background construction and background subtraction. It takes initial few frames to construct a background model and thereby detection of the moving objects in the subsequent frames. During background construction stage; the linear dependency test between the region of supports/ local image patch in the target image frame and the reference background model are carried out using the Wronskian change detection model. The pixel values those are linearly dependent are assumed to be generated from an MoG process and are modeled using the same. Once the background is constructed, then the background subtraction and update process starts from the next frame. The efficiency of the proposed scheme is validated by testing it on two benchmark underwater video databases: fish4knowledge and underwaterchangedetection and one large scale outdoor video database: changedetection.net. The effectiveness of the proposed scheme is demonstrated by comparing it with eighteen state-of-the-art local change detection algorithms. The performance of the proposed scheme is carried out using one subjective and three quantitative evaluation measures. © 2017 Elsevier Ltd

Expert Systems with Applications

Spatio-contextual Gaussian mixture model for local change detection in underwater video

Iris recognition systems may be vulnerable to presentation attacks such as textured contact lenses, print attacks, and synthetic iris images. Increasing applications of iris recognition have raised the importance of efficient presentation attack detection algorithms. In this paper, we propose a novel algorithm for detecting iris presentation attacks using a combination of handcrafted and deep learning based features. The proposed algorithm combines local and global Haralick texture features in multi-level Redundant Discrete Wavelet Transform domain with VGG features to encode the textural variations between real and attacked iris images. The proposed algorithm is extensively tested on a large iris dataset comprising more than 270,000 real and attacked iris images and yields a total error of 1.01%. The experimental evaluation demonstrates the superior presentation attack detection performance of the proposed algorithm as compared to state-of-the-art algorithms. © 2018 IEEE.

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Fusion of handcrafted and deep learning features for large-scale multiple iris presentation attack detection

The widespread use of smartphones has spurred the research in mobile iris devices. Due to their convenience, these mobile devices are also utilized in unconstrained outdoor scenarios. This has necessitated the development of reliable iris recognition algorithms for such uncontrolled environment. At the same time, iris presentation attacks pose a major challenge to current iris recognition systems. It has been shown that print attacks and textured contact lens may significantly degrade the iris recognition performance. Motivated by these factors, we present a novel Mobile Uncontrolled Iris Presentation Attack Database (MUIPAD). The database contains more than 10,000 iris images that are acquired with and without textured contact lenses in indoor and outdoor environments using a mobile sensor. We also investigate the efficacy of textured contact lens in identity impersonation and obfuscation. Moreover, we demonstrate the effectiveness of deep learning based features for iris presentation attack detection on the proposed database. © 2018 IEEE.

Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018

Iris presentation attack via textured contact lens in unconstrained environment

Iris recognition in visible spectrum has developed into an active area of research. This has elevated the importance of efficient presentation attack detection algorithms, particularly in security based critical applications. In this paper, we present the first detailed analysis of the effect of textured contact lenses on iris recognition in visible spectrum. We introduce the first contact lens database in visible spectrum, Unconstrained Visible Contact Lens Iris (UVCLI) Database, containing samples from 70 classes with subjects wearing textured contact lenses in indoor and outdoor environments across multiple sessions. We observe that textured contact lenses degrade the visible spectrum iris recognition performance by over 25% and thus, may be utilized intentionally or unintentionally to attack existing iris recognition systems. Next, three iris presentation attack detection (PAD) algorithms are evaluated on the proposed database and highest PAD accuracy of 82.85%c is observed. This illustrates that there is a significant scope of improvement in developing efficient PAD algorithms for detection of textured contact lenses in unconstrained visible spectrum iris images. © 2017 IEEE.

IEEE International Joint Conference on Biometrics, IJCB 2017

Unconstrained visible spectrum iris with textured contact lens variations: Database and benchmarking

Existing research in the field of face recognition with variations due to disguises focuses primarily on images captured in controlled settings. Limited research has been performed on images captured in unconstrained environments, primarily due to the lack of corresponding disguised face datasets. In order to overcome this limitation, this work presents a novel Disguised Faces in the Wild (DFW) dataset, consisting of over 11,000 images for understanding and pushing the current state-of-the-art for disguised face recognition. To the best of our knowledge, DFW is a first-of-a-kind dataset containing images pertaining to both obfuscation and impersonation for understanding the effect of disguise variations. A major portion of the dataset has been collected from the Internet, thereby encompassing a wide variety of disguise accessories and variations across other covariates. As part of CVPR2018, a competition and workshop are organized to facilitate research in this direction. This paper presents a description of the dataset, the baseline protocols and performance, along with the phase-I results of the competition. © 2018 IEEE.