Mansi Sharma

Bio: Mansi Sharma is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Computer science & Rendering (computer graphics). The author has an hindex of 6, co-authored 37 publications receiving 190 citations. Previous affiliations of Mansi Sharma include Indian Institutes of Technology & Indian Institute of Technology Delhi.

MEStereo-Du2CNN: A Novel Dual Channel CNN for Learning Robust Depth Estimates from Multi-exposure Stereo Images for HDR 3D Applications

Abstract: Display technologies have evolved over the years. It is critical to develop practical HDR capturing, processing, and display solutions to bring 3D technologies to the next level. Depth estimation of multi-exposure stereo image sequences is an essential task in the development of cost-effective 3D HDR video content. In this paper, we develop a novel deep architecture for multi-exposure stereo depth estimation. The proposed architecture has two novel components. First, the stereo matching technique used in traditional stereo depth estimation is revamped. For the stereo depth estimation component of our architecture, a mono-to-stereo transfer learning approach is deployed. The proposed formulation circumvents the cost volume construction requirement, which is replaced by a ResNet based dual-encoder single-decoder CNN with different weights for feature fusion. EfficientNet based blocks are used to learn the disparity. Secondly, we combine disparity maps obtained from the stereo images at different exposure levels using a robust disparity feature fusion approach. The disparity maps obtained at different exposures are merged using weight maps calculated for different quality measures. The final predicted disparity map obtained is more robust and retains best features that preserve the depth discontinuities. The proposed CNN offers flexibility to train using standard dynamic range stereo data or with multi-exposure low dynamic range stereo sequences. In terms of performance, the proposed model surpasses state-of-the-art monocular and stereo depth estimation methods, both quantitatively and qualitatively, on challenging Scene flow and differently exposed Middlebury stereo datasets. The architecture performs exceedingly well on complex natural scenes, demonstrating its usefulness for diverse 3D HDR applications.

Terrorism

Abstract: Terrorism has emerged as the newest menace to world peace and, in particular, India's national security. Terrorists are growing their sophistication and competence in every aspect of their operations. Weapon technology is becoming more accessible, and the buying power of terrorist groups is increasing due to the easy availability of both equipment and trained personnel to operate it. Terrorists threaten not just the values of democracy and independence, but also the life, prosperity, and development of mankind. To prevent terrorism, stringent security measures are essential. If a legislation against terrorism is applied in a country like India, it should be so stringent that the guilty individual is imprisoned and does not walk free due to loopholes or loopholes. It is impossible to overlook the necessity for special laws to fight terrorism; in reality, the issue lies in the execution of laws and the misuse of authority granted by the unique legislation.

2T-UNET: A Two-Tower UNet with Depth Clues for Robust Stereo Depth Estimation

Abstract: —Stereo correspondence matching is an essential part of the multi-step stereo depth estimation process. This paper revisits the depth estimation problem, avoiding the explicit stereo matching step using a simple two-tower convolutional neural network. The proposed algorithm is entitled as 2T-UNet. The idea behind 2T-UNet is to replace cost volume construction with twin convolution towers. These towers have an allowance for different weights between them. Additionally, the input for twin encoders in 2T-UNet are different compared to the existing stereo methods. Generally, a stereo network takes a right and left image pair as input to determine the scene geometry. However, in the 2T-UNet model, the right stereo image is taken as one input and the left stereo image along with its monocular depth clue information, is taken as the other input. Depth clues provide complementary suggestions that help enhance the quality of predicted scene geometry. The 2T-UNet surpasses state-of-the-art monocular and stereo depth estimation methods on the challenging Scene flow dataset, both quantitatively and qualitatively. The architecture performs incredibly well on complex natural scenes, highlight- ing its usefulness for various real-time applications. Pretrained weights and code will be made readily available.

A free viewpoint 3DTV system based on parameterized variety model

Abstract: This paper presents a novel parameterized variety based architecture for interactive 3DTV and Free viewpoint TV (FTV) applications. The proposed signal representation scheme allows to render free viewpoint images, taking only few sample images of the scene acquired by arbitrary, uncalibrated cameras. We extend the Parameterized Image Variety approach for rendering proposed earlier by Genc and Ponce [1] to handle full perspective cameras. A fast and efficient algebraic framework is proposed for the parameterized representation of 3D scene, in terms of image pixel positions corresponding to only three reference scene points. The key aspects of the novel FTV architecture based on this variety model are i) interactive stereoscopic view synthesis from arbitrary viewpoint ii) intuitive interface for content based virtual view specification iii) facilitation to add special effects like 3D scene augmentation.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Structural Basis of Potential Inhibitors Targeting SARS-CoV-2 Main Protease

Abstract: The Coronavirus disease-19 (COVID-19) pandemic is still devastating the world causing significant social, economic, and political chaos. Corresponding to the absence of globally approved antiviral drugs for treatment and vaccines for controlling the pandemic, the number of cases and/or mortalities are still rising. Current patient management relies on supportive treatment and the use of repurposed drugs as an indispensable option. Of a crucial role in the viral life cycle, ongoing studies are looking for potential inhibitors to the main protease (Mpro) of severe acute respiratory syndrome Coronavirus -2 (SARS-CoV-2) to tackle the pandemic. Although promising results have been achieved in searching for drugs inhibiting the Mpro, work remains to be done on designing structure-based improved drugs. This review discusses the structural basis of potential inhibitors targeting SARS-CoV-2 Mpro, identifies gaps, and provides future directions. Further, compounds with potential Mpro based antiviral activity are highlighted.

Identification of phytochemical inhibitors against main protease of COVID-19 using molecular modeling approaches.

Abstract: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a novel corona virus that causes corona virus disease 2019 (COVID-19). The COVID-19 rapidly spread across the nations with high mortality rate even as very little is known to contain the virus at present. In the current study, we report novel natural metabolites namely, ursolic acid, carvacrol and oleanolic acid as the potential inhibitors against main protease (Mpro) of COVID-19 by using integrated molecular modeling approaches. From a combination of molecular docking and molecular dynamic (MD) simulations, we found three ligands bound to protease during 50 ns of MD simulations. Furthermore, the molecular mechanic/generalized/Born/Poisson-Boltzmann surface area (MM/G/P/BSA) free energy calculations showed that these chemical molecules have stable and favourable energies causing strong binding with binding site of Mpro protein. All these three molecules, namely, ursolic acid, carvacrol and oleanolic acid, have passed the ADME (Absorption, Distribution, Metabolism, and Excretion) property as well as Lipinski's rule of five. The study provides a basic foundation and suggests that the three phytochemicals, viz. ursolic acid, carvacrol and oleanolic acid could serve as potential inhibitors in regulating the Mpro protein's function and controlling viral replication. Communicated by Ramaswamy H. Sarma.

COVID-19 and therapy with essential oils having antiviral, anti-inflammatory, and immunomodulatory properties.

Abstract: Coronavirus disease of 2019 (COVID-19) has emerged as a global health threat. Unfortunately, there are very limited approved drugs available with established efficacy against the SARs-CoV-2 virus and its inflammatory complications. Vaccine development is actively being researched, but it may take over a year to become available to general public. Certain medications, for example, dexamethasone, antimalarials (chloroquine/hydroxychloroquine), antiviral (remdesivir), and IL-6 receptor blocking monoclonal antibodies (tocilizumab), are used in various combinations as off-label medications to treat COVID-19. Essential oils (EOs) have long been known to have anti-inflammatory, immunomodulatory, bronchodilatory, and antiviral properties and are being proposed to have activity against SARC-CoV-2 virus. Owing to their lipophilic nature, EOs are advocated to penetrate viral membranes easily leading to membrane disruption. Moreover, EOs contain multiple active phytochemicals that can act synergistically on multiple stages of viral replication and also induce positive effects on host respiratory system including bronchodilation and mucus lysis. At present, only computer-aided docking and few in vitro studies are available which show anti-SARC-CoV-2 activities of EOs. In this review, role of EOs in the prevention and treatment of COVID-19 is discussed. A discussion on possible side effects associated with EOs as well as anti-corona virus claims made by EOs manufacturers are also highlighted. Based on the current knowledge a chemo-herbal (EOs) combination of the drugs could be a more feasible and effective approach to combat this viral pandemic.