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Topaz Lens Effects Lens Topaz 3
User Manual Contents I. Installation 11. About 31 A. How to Install 3 B. Entering Your Key 3 IV. Settings & Parameters C. Host-Specific Installation 3 A. Effects & Presets 32 1. Paint Shop Pro 4 B. Bokeh - Center Focus 33 2. Photo Impact 5 C. Bokeh - SLR 36 Topaz Lens Effects Lens Topaz 3. Irfanview 6 D. Bokeh - Selective 39 D. Uninstalling 7 E. Camera - Pinhole 44 F. Camera - Tilt & Shift 46 II. Introduction G. Camera - Toy 50 A. About Topaz Lens Effects 8 H. Camera - Diffusion 54 1. Primary Functions 8 I. Filter - Dual Tone 55 B. Program Features 9 J. Filter - Fog 59 K. Filter - Graduated Color 6 III. New Features L. Filter - Graduated Neutral Density 61 A. Smart Brush 10 M. Filter - Polarization 62 B. Effects Menu 15 N. Filter - Reflector 63 C. Apply Button 16 O. Filter - Single Tone 64 D. Enhanced Sharpening 17 P. Filter - Streak 65 E. Split Screen View 18 Q. Filter - UV/Haze 65 F. Interface Style 19 R. Filter - Warmth 66 2 S. Lens - Creative Blur 67 IV. Work Area T. Lens - Dual Focus 68 A. Interface 20 U. Lens - Fisheye 70 B. Preview 24 V. Lens - Motion 71 C. Effects & Presets 26 W. Lens - Split Prism 72 1. Applying Presets 26 X. xF - Add Adjustment 73 2. Adding Presets 26 W. xF - Add Geometric Distortion 74 3. Deleting Presets 27 Z. xF - Add Grain 75 4. Importing Presets 28 AA. xF - Add Sharpening 75 5. Exporting Presets 28 AB. xF - Add Vignette 76 6. Sharing Presets 28 AC. xF - Add Vignette - Selective 76 D. -
Geotagging: an Innovative Tool to Enhance Transparency and Supervision
Geotagging: An Innovative Tool To Enhance Transparency and Supervision Transparency through Geotagging Noel Sta. Ines Geotagging Geotagging • Process of assigning a geographical reference, i.e, geographical coordinates (latitude and longitude) + elevation - to an object. • This could be done by taking photos, nodes and tracks with recorded GPS coordinates. • This allows geo-tagged object or SP data to be easily and accurately located on a map. WhatThe is Use Geotagging and Implementation application in of the Geo Philippines?-Tagging • A revolutionary and inexpensive approach of using ICT + GPS applications for accurate visualization of sub-projects • Device required is only a GPS enabled android cellphone, and access to freely available apps • Easily replicable for mainstreaming to Government institutions & CSOs • Will help answer the question: Is the right activity implemented in the right place? – (asset verification tool) Geotagging: An Innovative Tool to Enhance Transparency and Supervision Geotagging Example No. 1: Visualization of a farm-to-market road in a conflict area: showing specific location, ground distance, track / alignment, elevation profile, ground photos (with coordinates, date and time taken) of Farm-to-market Road, i.e. baseline information + Progress photos + 3D visualization Geotagging: An Innovative Tool to Enhance Transparency and Supervision Geotagging Example No. 2: Location and Visualization of rehabilitation of a city road in Earthquake damaged-area in Tagbilaran, Bohol, Philippines by the auditors and the citizen volunteers Geotagging: An Innovative Tool to Enhance Transparency and Supervision Geo-tagging Example No. 3 Visualization of a water supply project showing specific location, elevation profile from the source , distribution lines and faucets, and ground photos of community faucets Geotagging: An Innovative Tool to Enhance Transparency and Supervision Geo-tagging Example No. -
Geotagging Photos to Share Field Trips with the World During the Past Few
Geotagging photos to share field trips with the world During the past few years, numerous new online tools for collaboration and community building have emerged, providing web-users with a tremendous capability to connect with and share a variety of resources. Coupled with this new technology is the ability to ‘geo-tag’ photos, i.e. give a digital photo a unique spatial location anywhere on the surface of the earth. More precisely geo-tagging is the process of adding geo-spatial identification or ‘metadata’ to various media such as websites, RSS feeds, or images. This data usually consists of latitude and longitude coordinates, though it can also include altitude and place names as well. Therefore adding geo-tags to photographs means adding details as to where as well as when they were taken. Geo-tagging didn’t really used to be an easy thing to do, but now even adding GPS data to Google Earth is fairly straightforward. The basics Creating geo-tagged images is quite straightforward and there are various types of software or websites that will help you ‘tag’ the photos (this is discussed later in the article). In essence, all you need to do is select a photo or group of photos, choose the "Place on map" command (or similar). Most programs will then prompt for an address or postcode. Alternatively a GPS device can be used to store ‘way points’ which represent coordinates of where images were taken. Some of the newest phones (Nokia N96 and i- Phone for instance) have automatic geo-tagging capabilities. These devices automatically add latitude and longitude metadata to the existing EXIF file which is already holds information about the picture such as camera, date, aperture settings etc. -
Geotag Propagation in Social Networks Based on User Trust Model
1 Geotag Propagation in Social Networks Based on User Trust Model Ivan Ivanov, Peter Vajda, Jong-Seok Lee, Lutz Goldmann, Touradj Ebrahimi Multimedia Signal Processing Group Ecole Polytechnique Federale de Lausanne, Switzerland Multimedia Signal Processing Group Swiss Federal Institute of Technology Motivation 2 We introduce users in our system for geotagging in order to simulate a real social network GPS coordinates to derive geographical annotation, which are not available for the majority of web images and photos A GPS sensor in a camera provides only the location of the photographer instead of that of the captured landmark Sometimes GPS and Wi-Fi geotagging determine wrong location due to noise http: //www.pl acecas t.net Multimedia Signal Processing Group Swiss Federal Institute of Technology Motivation 3 Tag – short textual annotation (free-form keyword)usedto) used to describe photo in order to provide meaningful information about it User-provided tags may sometimes be spam annotations given on purpose or wrong tags given by mistake User can be “an algorithm” http://code.google.com/p/spamcloud http://www.flickr.com/photos/scriptingnews/2229171225 Multimedia Signal Processing Group Swiss Federal Institute of Technology Goal 4 Consider user trust information derived from users’ tagging behavior for the tag propagation Build up an automatic tag propagation system in order to: Decrease the anno ta tion time, and Increase the accuracy of the system http://www.costadevault.com/blog/2010/03/listening-to-strangers Multimedia -
UC Berkeley International Conference on Giscience Short Paper Proceedings
UC Berkeley International Conference on GIScience Short Paper Proceedings Title Tweet Geolocation Error Estimation Permalink https://escholarship.org/uc/item/0wf6w9p9 Journal International Conference on GIScience Short Paper Proceedings, 1(1) Authors Holbrook, Erik Kaur, Gupreet Bond, Jared et al. Publication Date 2016 DOI 10.21433/B3110wf6w9p9 Peer reviewed eScholarship.org Powered by the California Digital Library University of California GIScience 2016 Short Paper Proceedings Tweet Geolocation Error Estimation E. Holbrook1, G. Kaur1, J. Bond1, J. Imbriani1, C. E. Grant1, and E. O. Nsoesie2 1University of Oklahoma, School of Computer Science Email: {erik; cgrant; gkaur; jared.t.bond-1; joshimbriani}@ou.edu 2University of Washington, Institute for Health Metrics and Evaluation Email: [email protected] Abstract Tweet location is important for researchers who study real-time human activity. However, few studies have examined the reliability of social media user-supplied location and description in- formation, and most who do use highly disparate measurements of accuracy. We examined the accuracy of predicting Tweet origin locations based on these features, and found an average ac- curacy of 1941 km. We created a machine learning regressor to evaluate the predictive accuracy of the textual content of these fields, and obtained an average accuracy of 256 km. In a dataset of 325788 tweets over eight days, we obtained city-level accuracy for approximately 29% of users based only on their location field. We describe a new method of measuring location accuracy. 1. Introduction With the rise of micro-blogging services and publicly available social media posts, the problem of location identification has become increasingly important. -
Geotagging with Local Lexicons to Build Indexes for Textually-Specified Spatial Data
Geotagging with Local Lexicons to Build Indexes for Textually-Specified Spatial Data Michael D. Lieberman, Hanan Samet, Jagan Sankaranarayanan Center for Automation Research, Institute for Advanced Computer Studies, Department of Computer Science, University of Maryland College Park, MD 20742, USA {codepoet, hjs, jagan}@cs.umd.edu Abstract— The successful execution of location-based and feature-based queries on spatial databases requires the construc- tion of spatial indexes on the spatial attributes. This is not simple when the data is unstructured as is the case when the data is a collection of documents such as news articles, which is the domain of discourse, where the spatial attribute consists of text that can be (but is not required to be) interpreted as the names of locations. In other words, spatial data is specified using text (known as a toponym) instead of geometry, which means that there is some ambiguity involved. The process of identifying and disambiguating references to geographic locations is known as geotagging and involves using a combination of internal document structure and external knowledge, including a document-independent model of the audience’s vocabulary of geographic locations, termed its Fig. 1. Locations mentioned in news articles about the May 2009 swine flu pandemic, obtained by geotagging related news articles. Large red circles spatial lexicon. In contrast to previous work, a new spatial lexicon indicate high frequency, and small circles are color coded according to recency, model is presented that distinguishes between a global lexicon of with lighter colors indicating the newest mentions. locations known to all audiences, and an audience-specific local lexicon. -
Geotagging Tweets Using Their Content
Proceedings of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference Geotagging Tweets Using Their Content Sharon Paradesi Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology [email protected] Abstract Harnessing rich, but unstructured information on social networks in real-time and showing it to relevant audi- ence based on its geographic location is a major chal- lenge. The system developed, TwitterTagger, geotags tweets and shows them to users based on their current physical location. Experimental validation shows a per- formance improvement of three orders by TwitterTag- ger compared to that of the baseline model. Introduction People use popular social networking websites such as Face- book and Twitter to share their interests and opinions with their friends and the online community. Harnessing this in- formation in real-time and showing it to the relevant audi- ence based on its geographic location is a major challenge. Figure 1: Architecture of TwitterTagger The microblogging social medium, Twitter, is used because of its relevance to users in real-time. The goal of this research is to identify the locations refer- Pipe POS tagger1. The noun phrases are then compared with enced in a tweet and show relevant tweets to a user based on the USGS database2 of locations. Common noun phrases, that user’s location. For example, a user traveling to a new such as ‘Love’ and ‘Need’, are also place names and would place would would not necessarily know all the events hap- be geotagged. To avoid this, the system uses a greedy ap- pening in that place unless they appear in the mainstream proach of phrase chunking. -
TAGGS: Grouping Tweets to Improve Global Geotagging for Disaster Response Jens De Bruijn1, Hans De Moel1, Brenden Jongman1,2, Jurjen Wagemaker3, Jeroen C.J.H
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2017-203 Manuscript under review for journal Nat. Hazards Earth Syst. Sci. Discussion started: 13 June 2017 c Author(s) 2017. CC BY 3.0 License. TAGGS: Grouping Tweets to Improve Global Geotagging for Disaster Response Jens de Bruijn1, Hans de Moel1, Brenden Jongman1,2, Jurjen Wagemaker3, Jeroen C.J.H. Aerts1 1Institute for Environmental Studies, VU University, Amsterdam, 1081HV, The Netherlands 5 2Global Facility for Disaster Reduction and Recovery, World Bank Group, Washington D.C., 20433, USA 3FloodTags, The Hague, 2511 BE, The Netherlands Correspondence to: Jens de Bruijn ([email protected]) Abstract. The availability of timely and accurate information about ongoing events is important for relief organizations seeking to effectively respond to disasters. Recently, social media platforms, and in particular Twitter, have gained traction as 10 a novel source of information on disaster events. Unfortunately, geographical information is rarely attached to tweets, which hinders the use of Twitter for geographical applications. As a solution, analyses of a tweet’s text, combined with an evaluation of its metadata, can help to increase the number of geo-located tweets. This paper describes a new algorithm (TAGGS), that georeferences tweets by using the spatial information of groups of tweets mentioning the same location. This technique results in a roughly twofold increase in the number of geo-located tweets as compared to existing methods. We applied this approach 15 to 35.1 million flood-related tweets in 12 languages, collected over 2.5 years. In the dataset, we found 11.6 million tweets mentioning one or more flood locations, which can be towns (6.9 million), provinces (3.3 million), or countries (2.2 million). -
An Introduction to Georss: a Standards Based Approach for Geo-Enabling RSS Feeds
Open Geospatial Consortium Inc. Date: 2006-07-19 Reference number of this document: OGC 06-050r3 Version: 1.0.0 Category: OpenGIS® White Paper Editors: Carl Reed OGC White Paper An Introduction to GeoRSS: A Standards Based Approach for Geo-enabling RSS feeds. Warning This document is not an OGC Standard. It is distributed for review and comment. It is subject to change without notice and may not be referred to as an OGC Standard. Recipients of this document are invited to submit, with their comments, notification of any relevant patent rights of which they are aware and to provide supporting do Document type: OpenGIS® White Paper Document subtype: White Paper Document stage: APPROVED Document language: English OGC 06-050r3 Contents Page i. Preface – Executive Summary........................................................................................ iv ii. Submitting organizations............................................................................................... iv iii. GeoRSS White Paper and OGC contact points............................................................ iv iv. Future work.....................................................................................................................v Foreword........................................................................................................................... vi Introduction...................................................................................................................... vii 1 Scope.................................................................................................................................1 -
From RDF to RSS and Atom
From RDF to RSS and Atom: Content Syndication with Linked Data Alex Stolz Martin Hepp Universität der Bundeswehr München Universität der Bundeswehr München E-Business and Web Science Research Group E-Business and Web Science Research Group 85577 Neubiberg, Germany 85577 Neubiberg, Germany +49-89-6004-4277 +49-89-6004-4217 [email protected] [email protected] ABSTRACT about places, artists, brands, geography, transportation, and related facts. While there are still quality issues in many sources, For typical Web developers, it is complicated to integrate content there is little doubt that much of the data could add some value from the Semantic Web to an existing Web site. On the contrary, when integrated into external Web sites. For example, think of a most software packages for blogs, content management, and shop Beatles fan page that wants to display the most recent offers for applications support the simple syndication of content from Beatles-related products for less than 10 dollars, a hotel that wants external sources via data feed formats, namely RSS and Atom. In to display store and opening hours information about the this paper, we describe a novel technique for consuming useful neighborhood on its Web page, or a shopping site enriched by data from the Semantic Web in the form of RSS or Atom feeds. external reviews or feature information related to a particular Our approach combines (1) the simplicity and broad tooling product. support of existing feed formats, (2) the precision of queries Unfortunately, consuming content from the growing amount of against structured data built upon common Web vocabularies like Semantic Web data is burdensome (if not prohibitively difficult) schema.org, GoodRelations, FOAF, SIOC, or VCard, and (3) the for average Web developers and site owners, for several reasons: ease of integrating content from a large number of Web sites and Even in the ideal case of having a single SPARQL endpoint [2] at other data sources of RDF in general. -
Plastic Fantastic Toy Camera Show
LightBox Photographic Gallery 1045 Marine Drive Astoria, Oregon 97103 503-468-0238 [email protected] LightBox Photographic Gallery Call for Entries PLASTIC FANTASTIC TOY CAMERA SHOW Toy cameras are special with the wondrous and dreamlike images they create, photographic subjects take on a unique special quality that is rarely found from any other capture device. We at LightBox have a special love of these cameras and images and in celebration of our 1st anniversary we wish to feature photographers that share our love of these special cameras. We need your help in starting an annual tradition here at LightBox by entering this 1st Plastic Fantastic show and help us treat our gallery visitors to the wonders of the Toy camera. The Plastic Fantastic Show will run from June 12th to July 7, 2010. Eligibility The list of toy cameras is extensive, the requirement is that the camera either have a plastic body or lens, no auto anything and decidedly low tech. Prints may be traditional optical prints or scanned negatives printed digitally as c-prints or pigment ink prints. Digital manipulations are not encouraged. Optical prints are preferred. Deadlines Deadline for submissions is May 20,2010. Deadline for delivery of accepted works is June 4, 2010. Requirement for Submission You may email jpegs or send us a CD, or proof prints. Files/proofs must be titled with last and first name and title. Sample: DoeJoe-Title SASE must be included if you wish your CD or Proofs to be returned. Entry Fee There is a non refundable entry fee of $10 each, or $25 for 3, and $5 each for each additional after three. -
Arxiv:1810.03067V1 [Cs.IR] 7 Oct 2018
Geocoding Without Geotags: A Text-based Approach for reddit Keith Harrigian Warner Media Applied Analytics Boston, MA [email protected] Abstract that will require more thorough informed consent processes (Kho et al., 2009; European Commis- In this paper, we introduce the first geolocation sion, 2018). inference approach for reddit, a social media While some social platforms have previously platform where user pseudonymity has thus far made supervised demographic inference dif- approached the challenge of balancing data access ficult to implement and validate. In particu- and privacy by offering users the ability to share lar, we design a text-based heuristic schema to and explicitly control public access to sensitive at- generate ground truth location labels for red- tributes, others have opted not to collect sensitive dit users in the absence of explicitly geotagged attribute data altogether. The social news website data. After evaluating the accuracy of our la- reddit is perhaps the largest platform in the lat- beling procedure, we train and test several ge- ter group; as of January 2018, it was the 5th most olocation inference models across our reddit visited website in the United States and 6th most data set and three benchmark Twitter geoloca- tion data sets. Ultimately, we show that ge- visited website globally (Alexa, 2018). Unlike olocation models trained and applied on the real-name social media platforms such as Face- same domain substantially outperform models book, reddit operates as a pseudonymous website, attempting to transfer training data across do- with the only requirement for participation being mains, even more so on reddit where platform- a screen name.