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

In this paper, we have proposed an ontology-based context-aware framework for providing intelligent services such as smart surveillance, which employ IoT technologies to ensure better quality of life in a smart city. An IoT network such as a smart surveillance system combines the working of Closed-circuit television (CCTV) cameras and various sensors to perform real-time computation for identifying threats and critical situations with the help of valuable context information. This information is perceptual in nature and needs to be converted into higher-level abstractions that can further be used for reasoning to recognize situations. Semantic abstractions for perceptual inputs are possible with the use of a multimedia ontology encoded using Multimedia Web Ontology Language (MOWL) that helps to define concepts, properties and structure of a possible environment. MOWL also allows for a dynamic modeling of real-time situations by employing Dynamic Bayesian networks (DBN), which suits the requirements of a intelligent IoT system. In this paper, we show the application of this framework in a smart surveillance system. Surveillance is enhanced by not only helping to analyze past events, but by predicting dangerous situations for which preventive actions can be taken. In our proposed approach, continuous video stream of data captured by CCTV cameras can be processed on the fly to give real-time alerts to concerned authorities. These alerts can be disseminated using e-mail, text messaging, on-screen alerts and alarms. © 2018 Association for Computing Machinery.

World University root node

ACM International Conference Proceeding Series

An ontology-based context-aware IoT framework for smart surveillance

The varied road conditions, chaotic and unstructured traffic, lack of lane discipline and wide variety of vehicles in countries like India, Pakistan and so on pose a need for a novel traffic monitoring system. In this study, the authors propose a novel camera-based traffic monitoring and prediction scheme without identifying or tracking vehicles. Spatial interest points (SIPs) and spatio-temporal interest points (STIPs) are extracted from the video stream of road traffic. SIP represents the number of vehicles and STIP represents the number of moving vehicles. The distributions of these features are then classified using Gaussian mixture model. In the proposed method, they learn the road state pattern using dynamic Bayesian network and predict the future road traffic state within a specific time delay. The predicted road state information can be used for traffic planning. The proposed method is computationally light, yet very powerful and efficient. The algorithm is tested for different weather conditions as well. They have validated their algorithm using Synchro Studio simulator and got 95.7% as average accuracy and on real-time video we got an accuracy of 84%. © The Institution of Engineering and Technology 2017.

IET Intelligent Transport Systems

Video-based road traffic monitoring and prediction using dynamic Bayesian networks

Bayesian Sparse Signal Recovery (SSR) for Multiple Measurement Vectors, when elements of each row of solution matrix are correlated, is addressed in the paper. We propose a standard linear Gaussian observation model and a three-level hierarchical estimation framework, based on Gaussian Scale Mixture (GSM) model with some random and deterministic parameters, to model each row of the unknown solution matrix. This hierarchical model induces heavy-tailed marginal distribution over each row which encompasses several choices of distributions viz. Laplace distribution, Student's t distribution and Jeffery prior. Automatic Relevance Determination (ARD) phenomenon introduces sparsity in the model. It is interesting to see that Block Sparse Bayesian Learning framework is a special case of the proposed framework when induced marginal is Jeffrey prior. Experimental results for synthetic signals are provided to demonstrate its effectiveness. We also explore the possibility of using Multiple Measurement Vectors to model Dynamic Hand Posture Database which consists of sequence of temporally correlated hand posture sequence. It can be seen that by exploiting temporal correlation information present in the successive image samples, the proposed framework can reconstruct the data with less linear random measurements with high fidelity. © 2018 ACM.

Sparse Signal Recovery for Multiple Measurement Vectors with Temporally Correlated Entries: A Bayesian Perspective

Hyperspectral cameras are used to preserve fine spectral details of scenes that are not captured by traditional RGB cameras that comprehensively quantizes radiance in RGB images. Spectral details provide additional information that improves the performance of numerous image based analytic applications, but due to high hyperspectral hardware cost and associated physical constraints, hyperspectral images are not easily available for further processing. Motivated by the performance of deep learning for various computer vision applications, we propose a 2D convolution neural network and a 3D convolution neural network based approaches for hyperspectral image reconstruction from RGB images. A 2D-CNN model primarily focuses on extracting spectral data by considering only spatial correlation of the channels in the image, while in 3D-CNN model the inter-channel co-relation is also exploited to refine the extraction of spectral data. Our 3D-CNN based architecture achieves very good performance in terms of MRAE and RMSE. In contrast to 3D-CNN, our 2D-CNN based architecture also achieves comparable performance with very less computational complexity. © 2018 IEEE.

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

2D-3D CNN based architectures for spectral reconstruction from RGB images

This edited volume represents the beginnings of the journey in digital heritage of India with a focus on the digital preservation of a UNESCO world heritage site with tangible and intangible heritage artefacts of great historic value. The vision of the sponsoring agency, the Department of Science and Technology of the Government of India, in digital heritage has expanded to a larger canvas of inter-disciplinary Cyber-Physical Systems with heritage as one of the central themes. Starting with cultural heritage of Hampi, the future directions are to include other cultural heritage sites, ecological heritage, socio-cultural heritage of dwellings of cities, of music, soundscapes and spoken languages; the list goes on. With emerging technologies of underwater mapping, LIDAR scanners, drones, virtual and augmented realities, 3D printing etc., the future directions for Indian digital heritage seem endless. This chapter elaborates a few directions which have now been encapsulated as a detailed project report and submitted as guidance to the Department of Science and Technology for the next phase of funded research and development. © Springer Nature Singapore Pte Ltd. 2017.

Digital Hampi: Preserving Indian Cultural Heritage

Indian digital heritage: The next steps

The book represents the culmination of a hugely successful heritage preservation project initiated by the Government of India’s Department of Science and Technology. It presents extensive research on the digital preservation of the history, mythology, art, architecture and culture of the world heritage site Hampi in Karnataka, the seat of the Vijayanagara dynasty in medieval India. Further, the book introduces readers to a range of techniques developed by Indian technical research groups for digitally preserving both the tangible and intangible cultural heritage of the region. These techniques are sufficiently generic to be applied in heritage preservation efforts for other historical sites around the world as well. Technological advances have made it possible to not only create digital archives of these heritage artifacts, but to also share these resources for people to view, explore, experience, and analyze. This book showcases how cutting-edge technology can be combined with cultural and historical research to digitize and preserve heritage. It is the consolidation of work conducted under the Indian Digital Heritage project, a unique initiative of the Department of Science & Technology (DST), Government of India. The project involved collaboration between researchers in the areas of Technology, Computer Science, Architecture and the Humanities for the digital documentation and interpretation of India’s tangible and intangible heritage. It highlights the art, architecture, and cultural legacy of the world heritage site of Hampi in Karnataka, the medieval capital of the 14th-16th century Vijayanagara dynasty. The contributors to this book are scientists and technology experts from prominent academic institutes in India such as the IITs (Indian Institutes of Technology), NIIT, and NID (National Institute of Design) working in collaboration with some of India’s top architects, art historians, anthropologists, heritage groups and multi-disciplinary cultural institutions such as the National Institute of Advanced Studies (NIAS). Their papers will introduce readers to cutting-edge technologies from research areas such as computer vision, 3D modeling and artificial intelligence as they are employed to preserve art and culture in the digital domain. The book is divided into four parts. Part 1 details efforts and techniques for modeling and representing the tangible heritage of Hampi, such as the reconstruction of damaged structures, realistic walk-throughs, and haptic rendering. Part 2 includes chapters detailing the analysis and digital restoration of artifacts such as mural paintings, inscriptions and sculptures, as well as mobile-based visual search for artifacts. Part 3 includes chapters on conjectural re-constructions of the architectural life, social life and traditions of Hampi. Lastly, Part 4 addresses the knowledge-based archiving and exploration of cultural heritage. © Springer Nature Singapore Pte Ltd. 2017.

Digital hampi: Preserving indian cultural heritage

The study and analysis of epigraphy is important for knowing about the past. From around third century to modern times, about 90,000 inscriptions have been discovered from different parts of India. © Springer Nature Singapore Pte Ltd. 2017.

Processing of historic inscription images

Vascular region segmentation in ultrasound images is necessary for applications like automatic registration, and surgical navigation. In this paper, a pipelined network comprising of a convolutional neural network (CNN) followed by unsupervised clustering is proposed to perform vessel segmentation in liver ultrasound images. The work is motivated by the tremendous success of CNNs in object detection and localization. CNN here is trained to localize vascular regions, which are subsequently segmented by the clustering. The proposed network results in 99.14% pixel accuracy and 69.62% mean region intersection over union on 132 images. These values are better than some existing methods. © 2018 IEEE.

Proceedings - IEEE International Symposium on Circuits and Systems

Segmentation of Vascular Regions in Ultrasound Images: A Deep Learning Approach

Anisotropic diffusion filters are one of the best choices for speckle reduction in the ultrasound images. These filters control the diffusion flux flow using local image statistics and provide the desired speckle suppression. However, inefficient use of edge characteristics results in either oversmooth image or an image containing misinterpreted spurious edges. As a result, the diagnostic quality of the images becomes a concern. To alleviate such problems, a novel anisotropic diffusion-based speckle reducing filter is proposed in this paper. A probability density function of the edges along with pixel relativity information is used to control the diffusion flux flow. The probability density function helps in removing the spurious edges and the pixel relativity reduces the oversmoothing effects. Furthermore, the filtering is performed in superpixel domain to reduce the execution time, wherein a minimum of 15% of the total number of image pixels can be used. For performance evaluation, 31 frames of three synthetic images and 40 real ultrasound images are used. In most of the experiments, the proposed filter shows a better performance as compared to the state-of-the-art filters in terms of the speckle region's signal-to-noise ratio and mean square error. It also shows a comparative performance for figure of merit and structural similarity measure index. Furthermore, in the subjective evaluation, performed by the expert radiologists, the proposed filter's outputs are preferred for the improved contrast and sharpness of the object boundaries. Hence, the proposed filtering framework is suitable to reduce the unwanted speckle and improve the quality of the ultrasound images. © 2017 IEEE.

IEEE Transactions on Image Processing

Edge Probability and Pixel Relativity-Based Speckle Reducing Anisotropic Diffusion

In many surveillance applications, the cameras are placed at overhead heights for human identification. In such real-world scenarios, the person of interest might be walking away from the camera and the only information available is 'image of the person's head'. In this research, we investigate the usage of head images for person recognition and propose it as a soft-biometric modality. With its viability for human recognition, application of head images can also be extended with other face recognition algorithms for surveillance. We propose a head image database pertaining to 103 subjects with more than 600 images. In addition to the database, we propose a framework for head image-based person verification. As a pre-processing stage, the framework includes evaluation of two segmentation algorithms. We also perform benchmarking evaluations of various texture, key-point, and learning-based representation algorithms and establish the baseline results. The experiments suggest that head images can be effectively used to ascertain human identity and the availability of this database could pave further research in this field. © 2018 IEEE.