A Novel Video Analytics Framework for Microscopic Tracking of Microbes
01 Jan 2018-pp 115-128
TL;DR: A new computing paradigm for video analytics is developed which will be helpful for the comprehensive understanding of the microbial data context in the form of video files along with effective management of that data with less human intervention.
Abstract: Micro-organisms or microbes are single- or multi-cellular living organisms viewed under a microscope because they are too tiny to be seen with naked eyes. Tracking them is important as they play a vital role in our lives in terms of breaking down substances, production of medicines, etc., as well as causing several diseases like malaria, tuberculosis, etc., which need to be taken care of. For a pathological study, the images of these microbes are captured from the microscope and image processing is done for further analysis. These operations involved for the analysis requires skilled technicians for error-free results. When the number of images increases, it becomes cumbersome for those technicians as there is a chance of ambiguity in results, which hampers the sensitivity of the study. Further, image processing is a bit challenging and time-consuming as a single image provides only a snapshot of the scene. In this situation, video has come into the picture which works on different frames taken over time making it possible to capture motion in the images keeping track of the changes temporally. Video combines a sequence of images, and the capability of automatically analyzing video to determine temporal events is known as video analytics. The aim of this paper is to develop a new computing paradigm for video analytics which will be helpful for the comprehensive understanding of the microbial data context in the form of video files along with effective management of that data with less human intervention. Since video processing requires more processing speed, a scalable cluster computing framework is also set up to improve the sensitivity and scalability for detecting microbes in a video. The HDP, an open source data processing platform for scalable data management, is used to set up the cluster by combining a group of computers or nodes. Apache Spark, a powerful and fast data processing tool is used for the analysis of these video files along with OpenCV libraries in an efficient manner which is monitored with a Web UI known as Apache Ambari for keeping in track all the nodes in the cluster.
01 Jan 2000
TL;DR: The Handbook of Image and Video Processing contains a comprehensive and highly accessible presentation of all essential mathematics, techniques, and algorithms for every type of image and video processing used by scientists and engineers.
Abstract: 1.0 INTRODUCTION 1.1 Introduction to Image and Video Processing (Bovik) 2.0 BASIC IMAGE PROCESSING TECHNIQUES 2.1 Basic Gray-Level Image Processing (Bovik) 2.2 Basic Binary Image Processing (Desai/Bovik) 2.3 Basic Image Fourier Analysis and Convolution (Bovik) 3.0 IMAGE AND VIDEO PROCESSING Image and Video Enhancement and Restoration 3.1 Basic Linear Filtering for Image Enhancement (Acton/Bovik) 3.2 Nonlinear Filtering for Image Enhancement (Arce) 3.3 Morphological Filtering for Image Enhancement and Detection (Maragos) 3.4 Wavelet Denoising for Image Enhancement (Wei) 3.5 Basic Methods for Image Restoration and Identification (Biemond) 3.6 Regularization for Image Restoration and Reconstruction (Karl) 3.7 Multi-Channel Image Recovery (Galatsanos) 3.8 Multi-Frame Image Restoration (Schulz) 3.9 Iterative Image Restoration (Katsaggelos) 3.10 Motion Detection and Estimation (Konrad) 3.11 Video Enhancement and Restoration (Lagendijk) Reconstruction from Multiple Images 3.12 3-D Shape Reconstruction from Multiple Views (Aggarwal) 3.13 Image Stabilization and Mosaicking (Chellappa) 4.0 IMAGE AND VIDEO ANALYSIS Image Representations and Image Models 4.1 Computational Models of Early Human Vision (Cormack) 4.2 Multiscale Image Decomposition and Wavelets (Moulin) 4.3 Random Field Models (Zhang) 4.4 Modulation Models (Havlicek) 4.5 Image Noise Models (Boncelet) 4.6 Color and Multispectral Representations (Trussell) Image and Video Classification and Segmentation 4.7 Statistical Methods (Lakshmanan) 4.8 Multi-Band Techniques for Texture Classification and Segmentation (Manjunath) 4.9 Video Segmentation (Tekalp) 4.10 Adaptive and Neural Methods for Image Segmentation (Ghosh) Edge and Boundary Detection in Images 4.11 Gradient and Laplacian-Type Edge Detectors (Rodriguez) 4.12 Diffusion-Based Edge Detectors (Acton) Algorithms for Image Processing 4.13 Software for Image and Video Processing (Evans) 5.0 IMAGE COMPRESSION 5.1 Lossless Coding (Karam) 5.2 Block Truncation Coding (Delp) 5.3 Vector Quantization (Smith) 5.4 Wavelet Image Compression (Ramchandran) 5.5 The JPEG Lossy Standard (Ansari) 5.6 The JPEG Lossless Standard (Memon) 5.7 Multispectral Image Coding (Bouman) 6.0 VIDEO COMPRESSION 6.1 Basic Concepts and Techniques of Video Coding (Barnett/Bovik) 6.2 Spatiotemporal Subband/Wavelet Video Compression (Woods) 6.3 Object-Based Video Coding (Kunt) 6.4 MPEG-I and MPEG-II Video Standards (Ming-Ting Sun) 6.5 Emerging MPEG Standards: MPEG-IV and MPEG-VII (Kossentini) 7.0 IMAGE AND VIDEO ACQUISITION 7.1 Image Scanning, Sampling, and Interpolation (Allebach) 7.2 Video Sampling and Interpolation (Dubois) 8.0 IMAGE AND VIDEO RENDERING AND ASSESSMENT 8.1 Image Quantization, Halftoning, and Printing (Wong) 8.2 Perceptual Criteria for Image Quality Evaluation (Pappas) 9.0 IMAGE AND VIDEO STORAGE, RETRIEVAL AND COMMUNICATION 9.1 Image and Video Indexing and Retrieval (Tsuhan Chen) 9.2 A Unified Framework for Video Browsing and Retrieval (Huang) 9.3 Image and Video Communication Networks (Schonfeld) 9.4 Image Watermarking (Pitas) 10.0 APPLICATIONS OF IMAGE PROCESSING 10.1 Synthetic Aperture Radar Imaging (Goodman/Carrera) 10.2 Computed Tomography (Leahy) 10.3 Cardiac Imaging (Higgins) 10.4 Computer-Aided Detection for Screening Mammography (Bowyer) 10.5 Fingerprint Classification and Matching (Jain) 10.6 Probabilistic Models for Face Recognition (Pentland/Moghaddam) 10.7 Confocal Microscopy (Merchant/Bartels) 10.8 Automatic Target Recognition (Miller) Index
TL;DR: This work constructs application-oblivious models for the cost prediction by using learned knowledge about the workloads at the hypervisor (also called VMM) level and evaluates the models using five representative workloads on a Xen virtualized environment.
Abstract: Live migration of virtual machine (VM) provides a significant benefit for virtual server mobility without disrupting service. It is widely used for system management in virtualized data centers. However, migration costs may vary significantly for different workloads due to the variety of VM configurations and workload characteristics. To take into account the migration overhead in migration decision-making, we investigate design methodologies to quantitatively predict the migration performance and energy consumption. We thoroughly analyze the key parameters that affect the migration cost from theory to practice. We construct application-oblivious models for the cost prediction by using learned knowledge about the workloads at the hypervisor (also called VMM) level. This should be the first kind of work to estimate VM live migration cost in terms of both performance and energy in a quantitative approach. We evaluate the models using five representative workloads on a Xen virtualized environment. Experimental results show that the refined model yields higher than 90% prediction accuracy in comparison with measured cost. Model-guided decisions can significantly reduce the migration cost by more than 72.9% at an energy saving of 73.6%.
TL;DR: The image classification system is designed to positively identify malaria parasites present in thin blood smears, and differentiate the species of malaria, and makes the method highly sensitive at diagnosing a complete sample provided many views are analysed.
Abstract: Malaria is a serious global health problem, and rapid, accurate diagnosis is required to control the disease An image processing algorithm to automate the diagnosis of malaria on thin blood smears is developed The image classification system is designed to positively identify malaria parasites present in thin blood smears, and differentiate the species of malaria Images are acquired using a charge-coupled device camera connected to a light microscope Morphological and novel threshold selection techniques are used to identify erythrocytes (red blood cells) and possible parasites present on microscopic slides Image features based on colour, texture and the geometry of the cells and parasites are generated, as well as features that make use of a priori knowledge of the classification problem and mimic features used by human technicians A two-stage tree classifier using backpropogation feedforward neural networks distinguishes between true and false positives, and then diagnoses the species (Plasmodium falciparum, P vivax, P ovale or P malariae) of the infection Malaria samples obtained from the Department of Clinical Microbiology and Infectious Diseases at the University of the Witwatersrand Medical School are used for training and testing of the system Infected erythrocytes are positively identified with a sensitivity of 85% and a positive predictive value (PPV) of 81%, which makes the method highly sensitive at diagnosing a complete sample provided many views are analysed Species were correctly determined for 11 out of 15 samples
TL;DR: The overall architecture of the ASKALON tool set is described and the basic functionality of the four constituent tools are outlined, enabling tool interoperability and demonstrating the usefulness and effectiveness of ASKalON by applying the tools to real‐world applications.
Abstract: Performance engineering of parallel and distributed applications is a complex task that iterates through various phases, ranging from modeling and prediction, to performance measurement, experiment ...
TL;DR: This study presents an original method for quantification and classification of erythrocytes in stained thin blood films infected with Plasmodium falciparum, which showed a specificity of 99.7% and a sensitivity of 94%.
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