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.

A Novel Image Fusion Scheme for FTV View Synthesis Based on Layered Depth Scene Representation & Scale Periodic Transform

Abstract: This paper presents a novel image fusion scheme for view synthesis based on a layered depth profile of the scene and scale periodic transform. To create a layered depth profile of the scene, we utilize the unique properties of scale transform considering the problem of depth map computation from reference images as a certain shift-variant problem. The problem of depth computation is solved without deterministic stereo correspondences or rather than representing image signals in terms of shifts. Instead, we pose the problem of image signals being representable as scale periodic function, and compute appropriate depth estimates determining the scalings of a basis function. The rendering process is formulated as a novel image fusion in which the textures of all probable matching points are adaptively determined, leveraging implicitly the geometric information. The results demonstrate superiority of the proposed approach in suppressing geometric, blurring or flicker artifacts in rendered wide-baseline virtual videos.

A Novel Algebaric Variety Based Model for High Quality Free-Viewpoint View Synthesis on a Krylov Subspace

Abstract: This paper presents a new depth-image-based rendering algorithm for free-viewpoint 3DTV applications. The cracks, holes, ghost countors caused by visibility, disocclusion, resampling problems associated with 3D warping lead to serious rendering artifacts in synthesized virtual views. This challenging problem of hole filling is formulated as an algebraic matrix completion problem on a higher dimensional space of monomial features described by a novel variety model. The high-level idea of this work is to exploit the linear or nonlinear structures of the data and interpolate missing values by solving algebraic varieties associated with Hankel matrices as a member of Krylov subspace. The proposed model effectively handles artifacts appear in wide-baseline spatial view interpolation and arbitrary camera movements. Our model has a low runtime and results excel with state-of-the-art methods in quantitative and qualitative evaluation.

Tactile-ViewGCN: Learning Shape Descriptor from Tactile Data using Graph Convolutional Network

Abstract: For humans, our “senses of touch” have always been necessary for our ability to precisely and efficiently manipulate objects of all shapes in any environment, but until recently, not many works have been done to fully understand haptic feedback. This work proposed a novel method for getting a better shape descriptor than existing methods for classifying an object from multiple tactile data collected from a tactile glove. It focuses on improving previous works on object classification using tactile data. The major problem for object classification from multiple tactile data is to find a good way to aggregate features extracted from multiple tactile images. We propose a novel method, dubbed as Tactile-ViewGCN, that hierarchically aggregate tactile features considering relations among different features by using Graph Convolutional Network. Our model outperforms previous methods on the STAG dataset with an accuracy of 81.82%.

A Novel Approach for Multi-View 3D HDR Content Generation via Depth Adaptive Cross Trilateral Tone Mapping

Abstract: In this work, we proposed a novel depth adaptive tone mapping scheme for stereo HDR imaging and 3D display. We are interested in the case where different exposures are taken from different viewpoints. The scheme employed a new depth-adaptive cross-trilateral filter (DA-CTF) for recovering High Dynamic Range (HDR) images from multiple Low Dynamic Range (LDR) images captured at different exposure levels. Explicitly leveraging additional depth information in the tone mapping operation correctly identify global contrast change and detail visibility change by preserving the edges and reducing halo artifacts in the synthesized 3D views by depth-image-based rendering (DIBR) procedure. The experiments show that the proposed DA-CTF and DIBR scheme outperforms state-of-the-art operators in the enhanced depiction of tone mapped HDR stereo images on LDR displays.

A novel variety-based 3DTV content generation scheme for casually captured sparse photo collections

Abstract: This paper presents a novel parameterized variety-based 3D exploration model to comprehend the sparse unstructured collection of photographs, and automatically plan virtual 3D tours of the world's landmarks through interesting viewpoints without explicit 3D reconstruction. The proposed system analyzes the collection of unstructured but related image data containing the same location or environment to create a parameterized scene graph: a data structure that conveys spatial relations and enable smooth virtual navigation between photos. A novel statistical-heuristic criteria is evolved exploiting the scene spatial layout and appearance to automatically identify best available portals between photographs. Once well connected, the graph is parameterized and consistently rendered choosing visually compelling 3D transition paths, maintaining a pleasing essence of parallax. The system's ability is demonstrated on several casually captured personal photo collections of heritage sites and imagery gathered from “Flickr” data.

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.