NIH Image to Imagej: 25 Years of Image Analysis Caroline a Schneider, Wayne S Rasband & Kevin W Eliceiri
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Bag-Of-Features Image Indexing and Classification in Microsoft SQL Server Relational Database
Bag-of-Features Image Indexing and Classification in Microsoft SQL Server Relational Database Marcin Korytkowski, Rafał Scherer, Paweł Staszewski, Piotr Woldan Institute of Computational Intelligence Cze¸stochowa University of Technology al. Armii Krajowej 36, 42-200 Cze¸stochowa, Poland Email: [email protected], [email protected] Abstract—This paper presents a novel relational database ar- data directly in the database files. The example of such an chitecture aimed to visual objects classification and retrieval. The approach can be Microsoft SQL Server where binary data is framework is based on the bag-of-features image representation stored outside the RDBMS and only the information about model combined with the Support Vector Machine classification the data location is stored in the database tables. MS SQL and is integrated in a Microsoft SQL Server database. Server utilizes a special field type called FileStream which Keywords—content-based image processing, relational integrates SQL Server database engine with NTFS file system databases, image classification by storing binary large object (BLOB) data as files in the file system. Microsoft SQL dialect (Transact-SQL) statements I. INTRODUCTION can insert, update, query, search, and back up FileStream data. Application Programming Interface provides streaming Thanks to content-based image retrieval (CBIR) access to the data. FileStream uses operating system cache for [1][2][3][4][5][6][7][8] we are able to search for similar caching file data. This helps to reduce any negative effects images and classify them [9][10][11][12][13]. Images can be that FileStream data might have on the RDBMS performance. -
Bioimage Analysis Tools
Bioimage Analysis Tools Kota Miura, Sébastien Tosi, Christoph Möhl, Chong Zhang, Perrine Paul-Gilloteaux, Ulrike Schulze, Simon Norrelykke, Christian Tischer, Thomas Pengo To cite this version: Kota Miura, Sébastien Tosi, Christoph Möhl, Chong Zhang, Perrine Paul-Gilloteaux, et al.. Bioimage Analysis Tools. Kota Miura. Bioimage Data Analysis, Wiley-VCH, 2016, 978-3-527-80092-6. hal- 02910986 HAL Id: hal-02910986 https://hal.archives-ouvertes.fr/hal-02910986 Submitted on 3 Aug 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. 2 Bioimage Analysis Tools 1 2 3 4 5 6 Kota Miura, Sébastien Tosi, Christoph Möhl, Chong Zhang, Perrine Pau/-Gilloteaux, - Ulrike Schulze,7 Simon F. Nerrelykke,8 Christian Tischer,9 and Thomas Penqo'" 1 European Molecular Biology Laboratory, Meyerhofstraße 1, 69117 Heidelberg, Germany National Institute of Basic Biology, Okazaki, 444-8585, Japan 2/nstitute for Research in Biomedicine ORB Barcelona), Advanced Digital Microscopy, Parc Científic de Barcelona, dBaldiri Reixac 1 O, 08028 Barcelona, Spain 3German Center of Neurodegenerative -
Other Departments and Institutes Courses 1
Other Departments and Institutes Courses 1 Other Departments and Institutes Courses About Course Numbers: 02-250 Introduction to Computational Biology Each Carnegie Mellon course number begins with a two-digit prefix that Spring: 12 units designates the department offering the course (i.e., 76-xxx courses are This class provides a general introduction to computational tools for biology. offered by the Department of English). Although each department maintains The course is divided into two halves. The first half covers computational its own course numbering practices, typically, the first digit after the prefix molecular biology and genomics. It examines important sources of biological indicates the class level: xx-1xx courses are freshmen-level, xx-2xx courses data, how they are archived and made available to researchers, and what are sophomore level, etc. Depending on the department, xx-6xx courses computational tools are available to use them effectively in research. may be either undergraduate senior-level or graduate-level, and xx-7xx In the process, it covers basic concepts in statistics, mathematics, and courses and higher are graduate-level. Consult the Schedule of Classes computer science needed to effectively use these resources and understand (https://enr-apps.as.cmu.edu/open/SOC/SOCServlet/) each semester for their results. Specific topics covered include sequence data, searching course offerings and for any necessary pre-requisites or co-requisites. and alignment, structural data, genome sequencing, genome analysis, genetic variation, gene and protein expression, and biological networks and pathways. The second half covers computational cell biology, including biological modeling and image analysis. It includes homework requiring Computational Biology Courses modification of scripts to perform computational analyses. -
Read and Write Images from a Database Using SQL Server
HOWTO: Read and write images from a database using SQL Server Atalasoft is an imaging SDK and we do not directly have any dependencies on, or specific functionality for Database access (with the single exception of our DbImageSource class). However, since the typical means of image storage in a database is a BLOB (Binary Long Object), developers can take advantage of the AtalaImage.ToByteArray and AtalaImage.FromByteArray methods to use in their own code to store and retrieve image data to and from databases respectively. Atalasoft dotImage has streaming capabilities that can be used jointly with ADO.NET to read and write images directly to a database without saving to a temporary file. The following code snippets demonstrates this in C# and VB.NET. Please note that the code examples below are provided as a courtesy/convenience only and are not meant to be production code or represent the only way to access a database. The code is provided as is as but an example of how to get the needed data in a byte array suitable for use in storing to a database and how to take binary data containing an image back into an AtalaImage to use in our SDK. Write to a Database C# rivate void SaveToSqlDatabase(AtalaImage image) { SqlConnection myConnection = null; try { // Save image to byte array. // we will be storing this image as a Jpeg using our JpegEncoder with 75 quality // you could use any of our ImageEncoder classes to store as the respective image types byte[] imagedata = image.ToByteArray(new Atalasoft.Imaging.Codec.JpegEncoder(75)); // Create the SQL statement to add the image data. -
Peoplesoft Financials/Supply Chain Management 9.2 (Through Update Image 40) Hardware and Software Requirements
PeopleSoft Financials/Supply Chain Management 9.2 (through Update Image 40) Hardware and Software Requirements July 2021 PeopleSoft Financials/Supply Chain Management 9.2 (through Update Image 40) Hardware and Software Requirements Copyright © 2021, Oracle and/or its affiliates. This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this is software or related documentation that is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, then the following notice is applicable: U.S. GOVERNMENT END USERS: Oracle programs (including any operating system, integrated software, any programs embedded, installed or activated on delivered hardware, and modifications of such programs) and Oracle computer documentation or other Oracle data delivered to or accessed by U.S. Government end users are "commercial computer software" or "commercial computer software -
Powerhouse(R) 4GL Migration Planning Guide
COGNOS(R) APPLICATION DEVELOPMENT TOOLS POWERHOUSE(R) 4GL FOR MPE/IX MIGRATION PLANNING GUIDE Migration Planning Guide 15-02-2005 PowerHouse 8.4 Type the text for the HTML TOC entry Type the text for the HTML TOC entry Type the text for the HTML TOC entry Type the Title Bar text for online help PowerHouse(R) 4GL version 8.4 USER GUIDE (DRAFT) THE NEXT LEVEL OF PERFORMANCETM Product Information This document applies to PowerHouse(R) 4GL version 8.4 and may also apply to subsequent releases. To check for newer versions of this document, visit the Cognos support Web site (http://support.cognos.com). Copyright Copyright © 2005, Cognos Incorporated. All Rights Reserved Printed in Canada. This software/documentation contains proprietary information of Cognos Incorporated. All rights are reserved. Reverse engineering of this software is prohibited. No part of this software/documentation may be copied, photocopied, reproduced, stored in a retrieval system, transmitted in any form or by any means, or translated into another language without the prior written consent of Cognos Incorporated. Cognos, the Cognos logo, Axiant, PowerHouse, QUICK, and QUIZ are registered trademarks of Cognos Incorporated. QDESIGN, QTP, PDL, QUTIL, and QSHOW are trademarks of Cognos Incorporated. OpenVMS is a trademark or registered trademark of HP and/or its subsidiaries. UNIX is a registered trademark of The Open Group. Microsoft is a registered trademark, and Windows is a trademark of Microsoft Corporation. FLEXlm is a trademark of Macrovision Corporation. All other names mentioned herein are trademarks or registered trademarks of their respective companies. All Internet URLs included in this publication were current at time of printing. -
Eloquence: HP 3000 IMAGE Migration by Michael Marxmeier, Marxmeier Software AG
Eloquence: HP 3000 IMAGE Migration By Michael Marxmeier, Marxmeier Software AG Michael Marxmeier is the founder and President of Marxmeier Software AG (www.marxmeier.com), as well as a fine C programmer. He created Eloquence in 1989 as a migration solution to move HP250/HP260 applications to HP-UX. Michael sold the package to Hewlett-Packard, but has always been responsible for Eloquence development and support. The Eloquence product was transferred back to Marxmeier Software AG in 2002. This chapter is excerpted from a tutorial that Michael wrote for HPWorld 2003. Email: [email protected] Eloquence is a TurboIMAGE compatible database that runs on HP-UX, Linux and Windows.Eloquence supports all the TurboIMAGE intrinsics, almost all modes, and they behave identically. HP 3000 applications can usually be ported with no or only minor changes. Compatibility goes beyond intrinsic calls (and also includes a performance profile.) Applications are built on assumptions and take advantage of specific behavior. If those assumptions are no longer true, the application may still work, but no longer be useful. For example, an IMAGE application can reasonably expect that a DBFIND or DBGET execute fast, independently of the chain length and that DBGET performance does not differ substantially between modes. If this is no longer true, the application may need to be rewritten, even though all intrinsic calls are available. Applications may also depend on external utilities or third party tools. If your application relies on a specific tool (let’s say, Robelle’s SUPRTOOL), you want it available (Suprtool already works with Eloquence). If a tool is not available, significant changes to the application would be required. -
Turboimage/XL Database Management System Reference Manual
TurboIMAGE/XL Database Management System Reference Manual HP e3000 MPE/iX Computer Systems Edition 8 Manufacturing Part Number: 30391-90012 E0701 U.S.A. July 2001 Notice The information contained in this document is subject to change without notice. Hewlett-Packard makes no warranty of any kind with regard to this material, including, but not limited to, the implied warranties of merchantability or fitness for a particular purpose. Hewlett-Packard shall not be liable for errors contained herein or for direct, indirect, special, incidental or consequential damages in connection with the furnishing or use of this material. Hewlett-Packard assumes no responsibility for the use or reliability of its software on equipment that is not furnished by Hewlett-Packard. This document contains proprietary information which is protected by copyright. All rights reserved. Reproduction, adaptation, or translation without prior written permission is prohibited, except as allowed under the copyright laws. Restricted Rights Legend Use, duplication, or disclosure by the U.S. Government is subject to restrictions as set forth in subparagraph (c) (1) (ii) of the Rights in Technical Data and Computer Software clause at DFARS 252.227-7013. Rights for non-DOD U.S. Government Departments and Agencies are as set forth in FAR 52.227-19 (c) (1,2). Acknowledgments UNIX is a registered trademark of The Open Group. Hewlett-Packard Company 3000 Hanover Street Palo Alto, CA 94304 U.S.A. © Copyright 1985, 1987, 1989, 1990, 1992, 1994, 1997, 2000, 2001 by Hewlett-Packard Company 2 Contents 1. Introduction Data Security . 27 Rapid Data Retrieval and Formatting . 28 Program Development . -
Oracle Multimedia Image PL/SQL API Quick Start
Oracle Multimedia Image PL/SQL API Quick Start Introduction Oracle Multimedia is a feature that enables Oracle Database to store, manage, and retrieve images, audio, video, and other heterogeneous media data in an integrated fashion with other enterprise information. Oracle Multimedia extends Oracle Database reliability, availability, and data management to multimedia content in media-rich applications. This article provides simple PL/SQL examples that upload, store, manipulate, and export image data inside a database using Oracle Multimedia’s PL/SQL packages. Some common pitfalls are also highlighted. The PL/SQL package used here is available in Oracle Database release 12c Release 2 or later with Oracle Multimedia installed (the default configuration provided by Oracle Universal Installer). The functionality in this PL/SQL package is the same as the functionality available with the Oracle Multimedia relational interface and object interface. For more details refer to Oracle Multimedia Reference and Oracle Multimedia User’s Guide. The following examples will show how to store images within the database in BLOB columns so that the image data is stored in database tablespaces. Oracle Multimedia image also supports BFILEs (pointers to files that reside on the filesystem), but this article will not demonstrate the use of BFILEs. Note that BFILEs are read -only so they can only be used as the source for image processing operations (i.e. you can process from a BFILE but you can’t process into a BFILE). NOTE: Access to an administrative account is required in order to grant the necessary file system privileges. In the following examples, you should change the command connect sys as sysdba to the appropriate one for your system: connect sys as sysdba Enter password: password The following examples also connect to the database using connect ron Enter password: password which you should change to an actual user name and password on your system. -
Bioimage Informatics
Bioimage Informatics Lecture 22, Spring 2012 Empirical Performance Evaluation of Bioimage Informatics Algorithms Image Database; Content Based Image Retrieval Emerging Applications: Molecular Imaging Lecture 22 April 23, 2012 1 Outline • Empirical performance evaluation of bioimage Informatics algorithm • Image database; content-based image retrieval • Introduction to molecular imaging 2 • Empirical performance evaluation of bioimage Informatics algorithm • Image database; content-based image retrieval • Introduction to molecular imaging 3 Overview of Algorithm Evaluation (I) • Two sets of data are required - Input data - Corresponding correct output (ground truth) • Sources of input data - Actual/experimental data, often from experiments - Synthetic data, often from computer simulation • Actual/experimental data - Essential for performance evaluation - May be costly to acquire - May not be representative - Ground truth often is unknown Overview of Algorithm Evaluation (II) • What exactly is ground truth? Ground truth is a term used in cartography, meteorology, analysis of aerial photographs, satellite imagery and a range of other remote sensing techniques in which data are gathered at a distance. Ground truth refers to information that is collected "on location." - http://en.wikipedia.org/wiki/Ground_truth • Synthetic (simulated) data Advantages - Ground truth is known - Usually low-cost Disadvantages - Difficult to fully represent the original data 5 Overview of Algorithm Evaluation (III) • Simulated data Advantages - Ground truth is known - Usually low-cost Disadvantages - Difficult to fully represent the original data • Realistic synthetic data - To use as much information from real experimental data as possible • Quality control of manual analysis Test Protocol Development (I) • Implementation - Source codes are not always available and often are on different platforms. • Parameter setting - This is one of the most challenging issues. -
An Introduction to R Franz X
An Introduction to R Franz X. Mohr December, 2019 Contents 1 Introduction 2 2 Installation 2 2.1 R .................................... 3 2.2 A development environment: RStudio ................ 4 3 R(eal) Basics 5 3.1 Functions and packages ........................ 5 3.2 Before you start ............................ 7 3.3 Getting help .............................. 8 3.4 Importing data ............................. 9 4 Data transformation 12 4.1 Standard method ........................... 12 4.2 Manipulating data with the dplyr package ............. 15 5 Saving and exporting data 17 5.1 Saving data in an R format ...................... 17 5.2 Exporting data into a csv file ..................... 18 5.3 Exporting data with the openxlsx package ............. 19 6 Graphical data analysis with the ggplot2 package 20 6.1 Line plots ............................... 20 6.2 Bar charts ............................... 24 6.3 Scatterplots .............................. 25 6.4 Saving plots .............................. 27 7 Loops and random numbers 28 7.1 Cross-sectional data .......................... 28 7.2 Time series data ............................ 31 8 Estimating econometric models 33 8.1 Linear regression with cross sectional data .............. 34 8.2 ARIMA models ............................ 37 9 References 38 9.1 Literature ............................... 38 9.2 Further resources ........................... 38 1 2 INSTALLATION 1 Introduction Data analysis is becoming increasingly important in today’s professional life and learning some basic data science skills appears to be a worthwhile effort. It can boost your productivity at work and improve your attractiveness on the job market. Among the myriad of programming languages out there, R has emerged as one of the most popular starting points for such an endeavour. It is a powerful application to analyse data, it is free and has a steep learning curve. -
Datascientist Manual
DATASCIENTIST MANUAL . 2 « Approcherait le comportement de la réalité, celui qui aimerait s’epanouir dans l’holistique, l’intégratif et le multiniveaux, l’énactif, l’incarné et le situé, le probabiliste et le non linéaire, pris à la fois dans l’empirique, le théorique, le logique et le philosophique. » 4 (* = not yet mastered) THEORY OF La théorie des probabilités en mathématiques est l'étude des phénomènes caractérisés PROBABILITY par le hasard et l'incertitude. Elle consistue le socle des statistiques appliqué. Rubriques ↓ Back to top ↑_ Notations Formalisme de Kolmogorov Opération sur les ensembles Probabilités conditionnelles Espérences conditionnelles Densités & Fonctions de répartition Variables aleatoires Vecteurs aleatoires Lois de probabilités Convergences et théorèmes limites Divergences et dissimilarités entre les distributions Théorie générale de la mesure & Intégration ------------------------------------------------------------------------------------------------------------------------------------------ 6 Notations [pdf*] Formalisme de Kolmogorov Phé nomé né alé atoiré Expé riéncé alé atoiré L’univérs Ω Ré alisation éléméntairé (ω ∈ Ω) Evé némént Variablé aléatoiré → fonction dé l’univér Ω Opération sur les ensembles : Union / Intersection / Complémentaire …. Loi dé Augustus dé Morgan ? Independance & Probabilités conditionnelles (opération sur des ensembles) Espérences conditionnelles 8 Variables aleatoires (discret vs reel) … Vecteurs aleatoires : Multiplét dé variablés alé atoiré (discret vs reel) … Loi marginalés Loi