publications

2021

1. BDL-NeurIPS 2021

   Evaluating Predictive Uncertainty and Robustness to Distributional Shift Using Real World Data

   Lakara, Kumud, Bhandari, Akshat, Seth, Pratinav, and Verma, Ujjwal

   2021

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   Most machine learning models operate under the assumption that the training, testing and deployment data is independent and identically distributed (i.i.d.). This assumption does not generally hold true in a natural setting. Usually, the deployment data is subject to various types of distributional shifts. The magnitude of a model’s performance is proportional to this shift in the distribution of the dataset. Thus it becomes necessary to evaluate a model’s uncertainty and robustness to distributional shifts to get a realistic estimate of its expected performance on real-world data. Present methods to evaluate uncertainty and model’s robustness are lacking and often fail to paint the full picture. Moreover, most analysis so far has primarily focused on classification tasks. In this paper, we propose more insightful metrics for general regression tasks using the Shifts Weather Prediction Dataset. We also present an evaluation of the baseline methods using these metrics

2. NAACL - SMM4H

   BERT based Transformers lead the way in Extraction of Health Information from Social Media

   Ramesh, Sidharth, Tiwari, Abhiraj, Choubey, Parthivi, Kashyap, Saisha, Khose, Sahil, Lakara, Kumud, Singh, Nishesh, and Verma, Ujjwal

   In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task 2021

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3. IEEE TENCON 2021

   Enhancement of PSNR based Anomaly Detection in Surveillance Videos using Penalty Modules

   Vidyarthi, Bhavam, Sequeira, Neil, Lenka, Sushant, and Verma, Ujjwal

   In TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) 2021
4. IEEE TENCON 2021

   Vehicle Re-identification in Smart City Transportation using Hybrid Surveillance Systems

   Ashutosh Holla, B, Manohara Pai, M.M., Verma, Ujjwal, and Pai, Radhika M.

   In TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) 2021
5. IEEE TENCON 2021

   Anomaly Detection Using Classification CNN Models: A Video Analytic Approach

   S, Girisha, Pai, Manohara, Verma, Ujjwal, Pai, Radhika M., and S, Shreesha

   In TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) 2021
6. IEEE TENCON 2021

   Flood Magnitude Assessment from UAV Aerial Videos Based on Image Segmentation and Similarity

   Sharma, Ananya, and Verma, Ujjwal

   In TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) 2021
7. IEEE TENCON 2021

   Behavioural Pattern Analysis of Fishes for Smart Aquaculture: An Object Centric Approach

   S, Shreesha, M, Manohara Pai M., Verma, Ujjwal, Pai, Radhika M., and S, Girisha

   In TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) 2021
8. IEEE MysuruCon 2021

   Exploring Techniques for Photo-realistic Image Generation from 3D Models - A Deep Learning Approach

   Ghosh, Pranoy, Pai, Krithika M, M M, Manohara Pai, Verma, Ujjwal, Rivet, Frederic, Roul, Abhishek, and Venugopal, V

   In 2021 IEEE Mysore Sub Section International Conference (MysuruCon) 2021
9. IEEE CONNECT 2021

   LSTM-Based Prediction of Water Quality Parameters System in Backwaters

   S, Shreesha, M, Manohara Pai M, Pai, Radhika M, and Verma, Ujjwal

   In 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) 2021
10. IEEE MASCON 2021

    Semantic Segmentation with Enhanced Temporal Smoothness Using CRF in Aerial Videos

    Girisha, S, Pai, Manohara M.M, Verma, Ujjwal, and Pai, Radhika M.

    In 2021 IEEE Madras Section Conference (MASCON) 2021
11. CAGEO

    DeepRivWidth : Deep learning based semantic segmentation approach for river identification and width measurement in SAR images of Coastal Karnataka

    Verma, Ujjwal, Chauhan, Arjun, M.M., Manohara Pai, and Pai, Radhika

    Computers & Geosciences 2021

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12. IEEE JSTARS

    UVid-Net: Enhanced Semantic Segmentation of UAV Aerial Videos by Embedding Temporal Information

    Girisha*, S, Verma*, Ujjwal, Pai, Manohara Pai, and Pai, Radhika

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021

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    Semantic segmentation of aerial videos has been extensively used for decision making in monitoring environmental changes, urban planning, and disaster management. The reliability of these decision support systems is dependent on the accuracy of the video semantic segmentation algorithms. The existing CNN based video semantic segmentation methods have enhanced the image semantic segmentation methods by incorporating an additional module such as LSTM or optical flow for computing temporal dynamics of the video which is a computational overhead. The proposed research work modifies the CNN architecture by incorporating temporal information to improve the efficiency of video semantic segmentation. In this work, an enhanced encoder-decoder based CNN architecture (UVid-Net) is proposed for UAV video semantic segmentation. The encoder of the proposed architecture embeds temporal information for temporally consistent labelling. The decoder is enhanced by introducing the feature-refiner module, which aids in accurate localization of the class labels. The proposed UVid-Net architecture for UAV video semantic segmentation is quantitatively evaluated on extended ManipalUAVid dataset. The performance metric mIoU of 0.79 has been observed which is significantly greater than the other state-of-the-art algorithms. Further, the proposed work produced promising results even for the pre-trained model of UVid-Net on urban street scene with fine tuning the final layer on UAV aerial videos.

13. IEEE Access

    Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning

    Shorewala, Shantam, *, Armaan Ashfaque, *, R Sidharth, and Verma, Ujjwal

    IEEE Access 2021

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2020

1. RSC Adv.

   Thread integrated smart-phone imaging facilitates early turning point colorimetric assay for microbes

   Prabhu, Anusha, Nandagopal, Giri, Yegneswaran, Prakash Peralam, Prabhu, Vijendra, Verma, Ujjwal, and Mani, Naresh Kumar

   RSC Advances 2020

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2. Improved Semantic Segmentation of Water Bodies and Land in SAR Images Using Generative Adversarial Networks

   Pai, Manohara, Mehrotra, Vaibhav, Verma, Ujjwal, and Pai, Radhika

   International Journal of Semantic Computing 2020

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   The availability of computationally efficient and powerful Deep Learning frameworks and high-resolution satellite imagery has created new approach for developing complex applications in the field of remote sensing. The easy access to abundant image data repository made available by different satellites of space agencies such as Copernicus, Landsat, etc. has opened various avenues of research in monitoring the world’s oceans, land, rivers, etc. The challenging research problem in this direction is the accurate identification and subsequent segmentation of surface water in images in the microwave spectrum. In the recent years, deep learning methods for semantic segmentation are the preferred choice given its high accuracy and ease of use. One major bottleneck in semantic segmentation pipelines is the manual annotation of data. This paper proposes Generative Adversarial Networks (GANs) on the training data (images and their corresponding labels) to create an enhanced dataset on which the networks can be trained, therefore, reducing human effort of manual labeling. Further, the research also proposes the use of deep-learning approaches such as U-Net and FCN-8 to perform an efficient segmentation of auto annotated, enhanced data of water body and land. The experimental results show that the U-Net model without GAN achieves superior performance on SAR images with pixel accuracy of 0.98 and F1 score of 0.9923. However, when augmented with GANs, the results saw a rise in these metrics with PA of 0.99 and F1 score of 0.9954.

3. Computer Vision Based Fish Tracking And Behaviour Detection System

   Shreesha, S, Pai, Manohara, Verma, Ujjwal, and Pai, Radhika

   In 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) 2020
4. Efficient Vehicle Counting by Eliminating Identical Vehicles in UAV aerial videos

   Holla, Ashutosh, Pai, Manohara, Verma, Ujjwal, and Pai, Radhika

   In 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) 2020
5. Semantic Segmentation of UAV Videos based on Temporal Smoothness in Conditional Random Fields (BEST PAPER AWARD)

   Girisha, S., Pai, Manohara, Verma, Ujjwal, and Pai, Radhika

   In 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) 2020

2019

1. Automatic segmentation of river and land in sar images: A deep learning approach

   Pai, Manohara, Mehrotra, Vaibhav, Aiyar, Shreyas, Verma, Ujjwal, and Pai, Radhika

   In 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) 2019

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   The ubiquitousness of satellite imagery and powerful, computationally efficient Deep Learning frameworks have found profound use in the field of remote sensing. Augmented with easy access to abundant image data made available by different satellites such as LANDSAT and European Space Agency’s Copernicus missions, deep learning has opened various avenues of research in monitoring the world’s oceans, land, rivers, etc. One significant problem in this direction is the accurate identification and subsequent segmentation of surface-water in images in the microwave spectrum. Typically, standard image processing tools are used to segment the images which are time-inefficient. However, in recent years, deep learning methods for semantic segmentation is the preferred choice given its high accuracy and ease of use. This paper proposes the use of deep-learning approaches such as U-Net to perform an efficient segmentation of river and land. Experimental results show that our approach achieves vastly superior performance on SAR images with pixel accuracy of 0.98 and F1 score of 0.99.

2. Semantic segmentation of UAV aerial videos using convolutional neural networks

   Girisha, S., Pai, Manohara, Verma, Ujjwal, and Pai, Radhika

   In 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) 2019

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   Semantic segmentation of complex aerial videos enables a better understanding of scene and context. This enhances the performance of automated video processing techniques like anomaly detection, object detection, event detection and other applications. But, there is a limited study of semantic segmentation in aerial videos due to non-availability of the relevant dataset. To address this, an aerial video dataset is captured using DJI Phantom 3 professional drone and is annotated manually. In addition, the proposed research work investigates the performance of semantic segmentation algorithms for aerial videos implemented using Fully Convolution Networks (FCN) and U-net architectures. In this study, two classes (greenery, road) are considered for semantic segmentation. It is observed that both architectures perform competitively on the aerial videos of Unmanned Aerial Vehicle (UAV) with a pixel accuracy of 89.7% and 87.31% respectively.

3. IEEE Access

   Performance Analysis of Semantic Segmentation Algorithms for Finely Annotated New UAV Aerial Video Dataset (ManipalUAVid)

   Girisha, S, Pai, Manohara, Verma, Ujjwal, and Pai, Radhika

   IEEE Access 2019

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   Semantic segmentation of videos helps in scene understanding, thereby assisting in other automated video processing techniques like anomaly detection, object detection, event detection, etc. However, there has been limited study on semantic segmentation of videos acquired using Unmanned Aerial Vehicles (UAV), primarily due to the absence of standard dataset. In this paper, a new UAV aerial video dataset (ManipalUAVid) for semantic segmentation is presented. The videos have been acquired in a closed university campus, and fine annotation is provided for four background classes viz. constructions, greeneries, roads, and waterbodies. Also, the performance of four semantic segmentation approaches: Conditional Random Field (CRF), U-Net, Fully Convolutional Network (FCN) and DeepLabV3+ are analysed on ManipalUAVid dataset. It is seen that these algorithms perform competitively on UAV aerial video dataset and achieves an mIoU of 0.86, 0.86, 0.86 and 0.83 respectively

2015

1. EURASIP JIVP

   Segmentation and size estimation of tomatoes from sequences of paired images

   Verma, Ujjwal, Rossant, Florence, and Bloch, Isabelle

   EURASIP Journal on Image and Video Processing 2015

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2. Segmentation of tomatoes in open field images with shape and temporal constraints

   Verma, Ujjwal, Rossant, Florence, Bloch, Isabelle, Orensanz, Julien, and Boisgontier, Denis

   2015

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2014

1. ICPRAM

   Shape-based segmentation of tomatoes for agriculture monitoring

   Verma, Ujjwal, Rossant, Florence, Bloch, Isabelle, Orensanz, Julien, and Boisgontier, Denis

   In International Conference on Pattern Recognition Applications and Methods (ICPRAM) 2014

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2. Tomato development monitoring in an open field, using a two-camera acquisition system

   Verma, Ujjwal, Rossant, Florence, Bloch, Isabelle, Orensanz, Julien, Boisgontier, Denis, and Lagarrigue, Marthe

   In 12th International Conference on Precision Agriculture 2014

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