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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8671卷 610-617页

作者： Strokina, Nataliya Juránek, Roman Eerola, Tuomas Lensu, Lasse Zemčik, Pavel Kälviäinen, Heikki Tampere University of Technology Department of Signal Processing P.O. Box 527 Tampere33101 Finland Brno University of Technology Department of Computer Graphics and Multimedia Brno Czech Republic Lappeenranta University of Technology Machine Vision and Pattern Recognition Laboratory P.O. Box 20 Lappeenranta53851 Finland

Bubble detection is a complicated tasks since varying lighting conditions changes considerably the appearance of bubbles in liquid. The two common techniques to detect circular objects such as bubbles, the geometry-based and appearance-based approaches, have their advantages and weaknesses. The geometry-based methods often fail to detect small blob-like bubbles that do not match the used geometrical model, and appearance-based approaches are vulnerable to appearance changes caused by, e.g., illumination. In this paper, we compare a geometry-based concentric circular arrangements (CCA) and appearance-based sliding window methods as well as their combinations in terms of bubble detection, gas volume computation, and size distribution estimation. The best bubble detection performance was achieved with the sliding window method whereas the most precise volume estimate was produced by the CCA method. The combination of the two approaches gave only a minor advantage compared to the base methods. © Springer International Publishing Switzerland 2014.

关键词： Geometry

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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Fuzzy logic based selera recognition

Fuzzy logic based selera recognition

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2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014

作者： Das, Abhijit Pal, Umapada Ballester, Miguel Angel Ferrer Blumenstein, Michael Institute for Integrated and Intelligent Systems Griffith University QLD Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC University of Las Palmas de Gran Canaria Las Palmas Spain

ISBN: (纸本)9781479920723

In this paper a selera recognition and validation system is proposed. Here selera segmentation was performed by Fuzzy logic-based clustering. Since the selera vessels are not prominent, image enhancement was required. A Fuzzy logic-based Brightness Preserving Dynamic Fuzzy Histogram Equalization and discrete Meyer wavelet was used to enhance the vessel patterns. For feature extraction, the Dense Local Binary pattern (D-LBP) was used. D-LBP patch descriptors of each training image are used to form a bag of features, which is used to produce the training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset is used here for experimentation. An encouraging Equal Error Rate (EER) of 4.31% was achieved in our experiments. © 2014 IEEE.

关键词： Support vector machines

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Encoding structural similarity by cross-covariance tensors for image classification

Encoding structural similarity by cross-covariance tensors f...

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作者： Biagio, Marco San Martelli, Samuele Crocco, Marco Cristani, Marco Murino, Vittorio Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Via Morego 30 Genova16163 Italy Universitá degli Studi di Verona Dipartimento di Informatica Strada le Grazie 15 Verona37134 Italy

In computer vision, an object can be modeled in two main ways: by explicitly measuring its characteristics in terms of feature vectors, and by capturing the relations which link an object with some exemplars, that is, in terms of similarities. In this paper, we propose a new similaritybased descriptor, dubbed structural similarity cross-covariance tensor (SS-CCT), where self-similarities come into play: Here the entity to be measured and the exemplar are regions of the same object, and their similarities are encoded in terms of cross-covariance matrices. These matrices are computed from a set of low-level feature vectors extracted from pairs of regions that cover the entire image. SS-CCT shares some similarities with the widely used covariance matrix descriptor, but extends its power focusing on structural similarities across multiple parts of an image, instead of capturing local similarities in a single region. The effectiveness of SSCCT is tested on many diverse classification scenarios, considering objects and scenes on widely known benchmarks (Caltech-101, Caltech-256, PASCAL VOC 2007 and SenseCam). In all the cases, the results obtained demonstrate the superiority of our new descriptor against diverse competitors. Furthermore, we also reported an analysis on the reduced computational burden achieved by using and efficient implementation that takes advantage from the integral image representation. © World Scientific Publishing Company.

关键词： Covariance matrix

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Multi-angle based lively sclera biometrics at a distance

Multi-angle based lively sclera biometrics at a distance

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2014 IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2014 - 2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management, CIBIM 2014

作者： Das, Abhijit Pal, Umapada Ballester, Miguel Angel Ferrer Blumenstein, Michael Institute for Integrated and Intelligent Systems Griffith University GriffithQLD Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC University of Las Palmas de Gran Canaria Las Palmas Spain

ISBN: (纸本)9781479945344

This piece of work proposes a liveliness based sclera eye biometric, validation and recognition technique at a distance. The images in this work are acquired by a digital camera in the visible spectrum at varying distance of about 1 meter from the individual. Each individual during registration as well as validation is asked to look straight and move their eye ball up, left and right keeping their face straight to incorporate liveliness of the data. At first the image is divided vertically into two halves and the eyes are detected in each half of the face image that is captured, by locating the eye ball by a Circular Hough Transform. Then the eye image is cropped out automatically using the radius of the iris. Next a C-means-based segmentation is used for sclera segmentation followed by vessel enhancement by the adaptive histogram equalization and Haar filtering. The feature extraction was performed by patch-based Dense-LDP (Linear Directive pattern). Furthermore each training image is used to form a bag of features, which is used to produce the training model. Each of the images of the different poses is combined at the feature level and the image level to obtain higher accuracy and to incorporate liveliness. The fusion that produces the best result is considered. Support Vector Machines (SVMs) are used for classification. Here images from 82 individuals (both left and right eye i.e. 164 different eyes) are used and an appreciable Equal Error Rate of 0.52% is achieved in this work. © 2014 IEEE.

关键词： Support vector machines

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Off-line Handwritten Thai Name Recognition for Student Identification in an Automated Assessment System

Off-line Handwritten Thai Name Recognition for Student Ident...

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International Joint Conference on Neural Networks

作者： Hemmaphan Suwanwiwat Vu Nguyen Michael Blumenstein Umapada Pal School of Information and Communication Technology Griffith University Computer Vision and Pattern Recognition Unit Indian Statistical Institute

ISBN: (纸本)9781479914821

In the field of pattern recognition, off-line handwriting recognition is one of the most intensive areas of study. This paper proposes an automatic off-line Thai language student name identification system which was built as a part of a completed off-line automated assessment system. There is limited work undertaken in developing off-line automatic assessment systems using handwriting recognition. To the authors' knowledge, none of the work on the proposed system has been performed on the Thai language. In addition the proposed system recognises each Thai name by using an approach for whole word recognition, which is different from the work found in the literature as most perform character-based recognition. In this proposed system, the Gaussian Grid Feature (GGF) and the Modified Direction Feature (MDF) extraction techniques are investigated on upper and lower contours, loops from full word contour images of each name sample, and artificial neural networks and support vector machine are used as classifiers. The encouraging recognition rates for both feature extraction techniques were achieved when applied on loop, upper and lower contour images (99.27% accuracy rate was achieved using MDF on artificial neural networks and 99.27% using GGF with a support vector machine classifier).

关键词： Off-line handwriting recognition Automated assessment system Student identification system Modified directional feature Gaussian grid feature

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A Study on Word-Level Multi-script Identification from Video Frames

A Study on Word-Level Multi-script Identification from Video...

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International Joint Conference on Neural Networks

作者： Nabin Sharma Umapada Pal Michael Blumenstein School of Information and Communication Technology Griffith University Computer Vision and Pattern Recognition Unit Indian Statistical Institute

ISBN: (纸本)9781479914821

The presence of multiple scripts in multi-lingual document images makes Optical Character Recognition (OCR) of such documents a challenging task. Due to the unavailability of a single OCR system which can handle multiple scripts, script identification becomes an essential step for choosing the appropriate OCR. Although, there are various techniques available for script identification from handwritten and printed documents having simple backgrounds, however script identification from video frames has been seldom explored. Video frames are coloured and suffer from low resolution, blur, complex background and noise to mention a few, which makes the script identification process a challenging task. This paper presents a study of various combinations of features and classifiers to explore whether the traditional script identification techniques can be applied to video frames. A texture based feature namely, Local Binary pattern (LBP), Gradient based features namely, Histogram of Oriented Gradient (HoG) and Gradient Local Auto-Correlation (GLAC) were used in the study. Combination of the features with SVMs and ANNs where used for classification. Three popular scripts, namely English, Bengali and Hindi were considered in the present study. Due to the inherent problems with the video, a super resolution technique was applied as a pre-processing step. Experiments show that the GLAC feature has performed better than the other features, and an accuracy of 94.25% was achieved when testing on 1271 words from three different scripts. The study also reveals that gradient features are more suitable for script identification than the texture features when using traditional script identification techniques on video frames.

关键词： Video document analysis Script identification Word segmentation OCR

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Entropy Computations of Document Images in Run-Length Compressed Domain

Entropy Computations of Document Images in Run-Length Compre...

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International Conference on Signal and Image Processing (ICSIP)

作者： P. Nagabhushan Mohammed Javed B.B. Chaudhuri Department of Studies in Computer Science University of Mysore Mysore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Compression of documents, images, audios and videos have been traditionally practiced to increase the efficiency of data storage and transfer. However, in order to process or carry out any analytical computations, decompression has become an unavoidable pre-requisite. In this research work, we have attempted to compute the entropy, which is an important document analytic directly from the compressed documents. We use Conventional Entropy Quantifier (CEQ) and Spatial Entropy Quantifiers (SEQ) for entropy computations [1]. The entropies obtained are useful in applications like establishing equivalence, word spotting and document retrieval. Experiments have been performed with all the data sets of [1], at character, word and line levels taking compressed documents in run-length compressed domain. The algorithms developed are computational and space efficient, and results obtained match 100% with the results reported in [1].

关键词： Entropy Image coding Feature extraction Energy measurement Data mining Position measurement

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Multi robot SLAM for features based environment modelling

Multi robot SLAM for features based environment modelling

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IEEE International Conference on Mechatronics and Automation

作者： Syed Riaz un Nabi Jafri Waheed Ahmed Zubair Ashraf Ryad Chellali Electronic Engineering Department NED UET Karachi Pakistan Pattern Analysis and Computer Vision LAB-IIT Genova Italy

ISBN: (纸本)9781479939800

This paper is presenting a multi robot simultaneous localization and mapping (SLAM) framework for environment modelling using 2D and 3D features. The proposed solution is using a team of mobile robots which are exploring unknown environment with unknown poses. Each team member is allowed to build its independent features based SLAM solution and to share the local map model among other mates. By matching the overlapping tendency of any two mates, a map merging strategy is introduced which in result is building global map. The overall approach has tested in 2D and 3D features based environment and results have shown.

关键词： Simultaneous localization and mapping Cameras Robot vision systems Lasers Feature extraction

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Segmentation-Free Keyword Spotting for Bangla Handwritten Documents

Segmentation-Free Keyword Spotting for Bangla Handwritten Do...

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International Workshop on Frontiers in Handwriting Recognition

作者： Xi Zhang Umapada Pal Chew Lim Tan School of Computing National University of Singapore Singapore Computer Vision And Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this paper, a segmentation-free keyword spotting method is proposed for Bangla handwritten documents. In order to tolerate large variations in handwritten scenarios, we extracted key points based on SIFT key point detector, and the end and intersection points found by morphological operations. Heat Kernel signature (HKS) is used to present the local characteristics of detected key points. Instead of using the same size of patch for all the key points, we apply a method dynamically deciding the patch size. Furthermore, our spotting method reduces the scope of searching on the document by only considering the candidate local zones with similar candidate key points, and does not need pre-processing steps. From the experiment on Bangla handwritten text we obtained encouraging results.

关键词： Feature extraction Entropy Hidden Markov models Detectors Heating Kernel Indexes

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