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.

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

A morphable model for the synthesis of 3D faces

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.

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

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.

/pdf/identification-of-phytochemical-inhibitors-against-main-1vjc8e96gy.pdf

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

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.

/pdf/covid-19-and-therapy-with-essential-oils-having-antiviral-4z3klah629.pdf

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

MEStereo-Du2CNN: a dual-channel CNN for learning robust depth estimates from multi-exposure stereo images for HDR 3D applications

Numerous stereoscopic 3D movies are released every year to theaters and created large revenues Despite the improvement in stereo capturing and 3D video post-production technology, stereoscopic artifacts which cause viewer discomfort continue to appear even in high-budget films Existing automatic 3D video quality measurement tools can detect distortions in stereoscopic images or videos, but they fail to consider the viewer's subjective perception of those artifacts, and how these distortions affect their choices In this paper, we introduce a novel recommendation system for stereoscopic 3D movies based on a latent factor model that meticulously analyse the viewer's subjective ratings and influence of 3D video distortions on their preferences To the best of our knowledge, this is a first-of-its-kind model that recommends 3D movies based on stereo-film quality ratings accounting correlation between the viewer's visual discomfort and stereoscopic-artifact perception The proposed model is trained and tested on benchmark Nama3ds1-cospad1 and LFOVIAS3DPh2 S3D video quality assessment datasets The experiments revealed that resulting matrix-factorization based recommendation system is able to generalize considerably better for the viewer's subjective ratings

Latent Factor Modeling of Users Subjective Perception for Stereoscopic 3D Video Recommendation.

In India, juvenile delinquency is a major problem that has ruined the lives of many young people. Juvenile criminality and related issues have a variety of effects for adolescents, their families, and society as a whole. The issue affects more than just crime victims. It does, however, have consequences for the juvenile delinquents household, career, and society at large. Victims of youthful delinquency are the most obvious victims. The most serious consequences of juvenile offences are socioeconomic and psychological issues that affect their family members and society as a whole. Juveniles participating in robberies, rapes, and assaults are sometimes significant because of psychiatric issues. Juveniles become accustomed to consuming alcohol or other substances as a result of their illicit actions. This study's main purpose is to look at the psychological and legal aspects of juvenile delinquency. The researcher has also thrown light on different judicial framework of Juvenile delinquency and the claim of juvenility with different case laws following with the conclusion.

Juvenile delinquency in India

The compression quality losses of depth sequences determine quality of view synthesis in free-viewpoint video. The depth map intra prediction in 3D extensions of the HEVC applies intra modes with auxiliary depth modeling modes (DMMs) to better preserve depth edges and handle motion discontinuities. Although such modes enable high efficiency compression, but at the cost of very high encoding complexity. Skipping conventional intra coding modes and DMMs in depth coding limits practical applicability of the HEVC for 3D display applications. In this paper, we introduce a novel low-complexity scheme for depth video compression based on low-rank tensor decomposition and HEVC intra coding. The proposed scheme leverages spatial and temporal redundancy by compactly representing the depth sequence as a high-order tensor. Tensor factorization into a set of factor matrices following CANDECOMP PARAFAC (CP) decomposition via alternating least squares give a low-rank approximation of the scene geometry. Further, compression of factor matrices with HEVC intra prediction support arbitrary target accuracy by flexible adjustment of bitrate, varying tensor decomposition ranks and quantization parameters. The results demonstrate proposed approach achieves significant rate gains by efficiently compressing depth planes in low-rank approximated representation. The proposed algorithm is applied to encode depth maps of benchmark Ballet and Breakdancing sequences. The decoded depth sequences are used for view synthesis in a multi-view video system, maintaining appropriate rendering quality.

Mansi Sharma

Papers

MEStereo-Du2CNN: a dual-channel CNN for learning robust depth estimates from multi-exposure stereo images for HDR 3D applications

Latent Factor Modeling of Users Subjective Perception for Stereoscopic 3D Video Recommendation.

Juvenile delinquency in India

A Flexible Lossy Depth Video Coding Scheme Based on Low-rank Tensor Modelling and HEVC Intra Prediction for Free Viewpoint Video.