Study of Prevalence of Coronary Atherosclerosis in Bodies Subjected to Autopsy belonging to Age Group 21-40 Years in Local Population

BACKGROUND: Atherosclerotic plaques in the coronary arteries can protrude into the lumen and obstruct the blood flow to myocardium. Depending on the severity of the occlusion it may produce sudden death if the degree of occlusion is very severe. Therefore it is needed to assess the prevalence of coronary atherosclerosis in general population. AIM OF THE STUDY: To find out the prevalence of coronary atherosclerosis in bodies subjected to autopsy belonging to age group 21 – 40 years so as to determine their prevalence. METHODS: It was a prospective study with a sample size of 100 cases belonging to age groups 21 – 40 years including both males and females from January 2017 to June 2018. The heart of the cases were examined and degree of coronary atherosclerosis related narrowing of the right and coronary arteries is studied. RESULTS: Overall the prevalence of coronary atherosclerosis in age group 21-40 years is found to be 37% had varying degree of atherosclerotic plaque occlusion in their right and left coronary artery and 63% of cases were normal. Males had overall prevalence of 41% and females 28% CONCLUSION: The prevalence of coronary atherosclerosis in age group 21 – 40 years is significantly high with 37 % of cases showing some degree of atherosclerosis. Most common lesion in Right coronary artery is grade 2 in both males and females and Most common lesion in left coronary artery is grade 2 & grade 3 & showed equal prevalence in males and grade 2 in females

Purposive sample consensus: A paradigm for model fitting with application to visual odometry

© Springer International Publishing Switzerland 2015. ANSAC (random sample consensus) is a robust algorithm for model fitting and outliers' removal, however, it is neither efficient nor reliable enough to meet the requirement of many applications where time and precision is critical. Various algorithms have been developed to improve its performance for model fitting. A new algorithm named PURSAC (purposive sample consensus) is introduced in this paper, which has three major steps to address the limitations of RANSAC and its variants. Firstly, instead of assuming all the samples have a same probability to be inliers, PURSAC seeks their differences and purposively selects sample sets. Secondly, as sampling noise always exists; the selection is also according to the sensitivity analysis of a model against the noise. The final step is to apply a local optimization for further improving its model fitting performance. Tests show that PURSAC can achieve very high model fitting certainty with a small number of iterations. Two cases are investigated for PURSAC implementation. It is applied to line fitting to explain its principles, and then to feature based visual odometry, which requires efficient, robust and precise model fitting. Experimental results demonstrate that PURSAC improves the accuracy and efficiency of fundamental matrix estimation dramatically, resulting in a precise and fast visual odometry

Psychiatric stigma across cultures: Local validation in Bangalore and London

Public responses to depression have a powerful effect on patients’ personal experience of illness, the course and outcome of the illness, and their ability to obtain gainful employment. Mental illness-related stigma reduction has become a priority, and to be effective, it requires innovative and effective public mental health interventions informed by a clear understanding of what stigma means. Based on Goffman’s formulation as spoiled identity, local concepts of stigma were validated and compared in clinical cultural epidemiological studies of depression in Bangalore, India, and London, England, using the EMIC, an instrument for studying illness-related experience, its meaning, and related behaviour. Similar indicators were validated in both centres, and the internal consistency was examined to identify those that contributed to a locally coherent concept and scale for stigma. Qualitative meaning of specific features of stigma at each site was clarified from patients’ prose narrative accounts. Concerns about marriage figured prominently as a feature of illness experience in both centres, but it was consistent with other indicators of stigma only in Bangalore, not in London. Although stigma is a significant issue across societies, particular manifestations may vary, and the cultural validity of indicators should be examined locally. Analysis of cultural context in the narrative accounts of illness indicates the variation and complexity in the relationship between aspects of illness experience and stigma. This report describes an approach following from the application of cultural epidemiological methods for identifying and measuring locally valid features of stigma in a scale for cultural study, cross-cultural comparisons, and for baseline and follow-up assessment to monitor stigma reduction programmes

Learning and Matching Multi-View Descriptors for Registration of Point Clouds

Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space. The correspondence problem is generally addressed by the design of discriminative 3D local descriptors on the one hand, and the development of robust matching strategies on the other hand. In this work, we first propose a multi-view local descriptor, which is learned from the images of multiple views, for the description of 3D keypoints. Then, we develop a robust matching approach, aiming at rejecting outlier matches based on the efficient inference via belief propagation on the defined graphical model. We have demonstrated the boost of our approaches to registration on the public scanning and multi-view stereo datasets. The superior performance has been verified by the intensive comparisons against a variety of descriptors and matching methods

Progressive Structure from Motion

Structure from Motion or the sparse 3D reconstruction out of individual photos is a long studied topic in computer vision. Yet none of the existing reconstruction pipelines fully addresses a progressive scenario where images are only getting available during the reconstruction process and intermediate results are delivered to the user. Incremental pipelines are capable of growing a 3D model but often get stuck in local minima due to wrong (binding) decisions taken based on incomplete information. Global pipelines on the other hand need the access to the complete viewgraph and are not capable of delivering intermediate results. In this paper we propose a new reconstruction pipeline working in a progressive manner rather than in a batch processing scheme. The pipeline is able to recover from failed reconstructions in early stages, avoids to take binding decisions, delivers a progressive output and yet maintains the capabilities of existing pipelines. We demonstrate and evaluate our method on diverse challenging public and dedicated datasets including those with highly symmetric structures and compare to the state of the art.Comment: Accepted to ECCV 201