Kirk Martinez

brief CV

I am a Professor in Electronics and Computer Science at the University of Southampton. I have a BSc in Physics from the University of Reading and a PhD in Image Processing from Electronic Systems Engineering at the University of Essex. Previously I was Arts-Computing Lecturer in University College London & Birkbeck College. I have been involved in nine European projects relating to technology for Cultural Heritage. My research has included content-based retrieval, the Semantic Web, image processing, Augmented Reality, Sensor networks and Internet of Things

In the VASARI project he helped design the world's first high resolution colorimetric imager for paintings. In MARC he worked with the National Gallery and the Technical University of Munich on a camera which was eventually used to capture their entire collection directly for the first time. In Viseum a new system was designed to allow web browsers to view high resolution images (which became IIPimage). These projects led to the VIPS image processing library. ACOHIR expanded this to turntable images. Artiste made a system for retrieving art images based on their content and allowed cross-collection searching through web services. SCULPTEUR moved this forward to use ontologies and search 3D objects by similarity in shape. eCHASE expanded the technology for commercial use by picture libraries. Recent research also includes Glacsweb and SemsorGrid4Env on sensor webs for the environment as well as Internet of Things sensing.

My research has been funded by the EU, EPSRC, NERC, AHRC, The Royal Society, Leverhulme, HP, ARM.

Outputs of my research are being used regularly in image handling (VIPS, IIPimage), environmental monitoring (Glacsweb and mountainsensing) as well as RTIimaging systems built for major museums and humanities researchers.

Publications include a summary of my imaging research in Transactions of the IEEE, wireless sensor networks, future networks, image processing, multimedia, web conference, semantic web conference, electronic imaging conferences and various invited talks around the world.

Current Research Interests

My current research interests include: environmental wireless sensor networks, Internet of Things, Web of Things. I also still create systems for imaging cultural heritage.

Research Projects

PhD : A new real-time feature extraction system for sketch generation. Using 110M LUT operations per second.
VASARI - made world's first hi-resolution colorimetric scanner for paintings
MARC - Methodology for Art reproduction in Colour. Made a 20kx20k camera.
Viseum - virtual museums networking, EU project
ACOHIR - object imaging and web browsing - EU project
Artiste - Content based retrieval of art images
Sculpteur - Semantic Web and Content based retrieval of 3D museum objects
eCHASE - semantic integration of photo libraries (incl. Getty Images)
Glacsweb - sensor networks for Glaciers
semsorgrid4env - semantic web meets sensor networks
Tijuana Estuary sensing - funded by NOAA
ARCOMEM - FP7 EU project

Currently running:
Colour and Space in Cultural Heritage (COSCH) European COST project
NERC IoT sensing proof of concept: mountainsensing
Innovate-UK funded project on novel IoT human interfaces

Software Publications

Glacial sediments computer-aided learning CD - winner of Acacemic Software 1997
VIPS/nip - Open Source image processing

Membership

member of the IEEE
member of the ACM
member of the American Geophysical Union
Executive Committee Earth and Space Sciences Informatics (ESSI) group of the AGU 2009-

Peer Review College - EPSRC and Economic and Social Research Council ESRC UK.

Editorships/Conference Committees etc

Programme Committee membership:

European Wireless Sensor Networks prog. committee 2012
Future Internet Symposium 2010

VAST2010 - 11th VAST International Symposium on Virtual Reality, Archaeology and Cultural Heritage

Computer Vision and Image Analysis of Art, Electronic Imaging 2010

ESSA 2009: Sensor Networks for Space and Science Applications.
1st International Workshop on the Semantic Sensor Web (SemSensWeb 2009)
3rd International Conference for GeoSensor Networks 2009
IEEE Consumer Communications and Networking Conference, 2009.
The 2008 International Workshop on Adaptive Wireless Sensor Networks (AWSN-08)
IAPR-TC19 Workshop on Computer Vision for Cultural Heritage, EVA 2008.
SPIE IS&T Computer image analysis in the study of art 2008

Semantic Web Conference workshop "Uncertainty Reasoning for the Semantic Web" 2006
Very High Resolution and Quality Imaging III, Electronic Imaging '98
Electronic Image Capture and Publishing in SYBEN '98, Zurich 1998
IEEE/IS&T Colour in Graphics, Imaging and Vision (CGIV) Apr 2002
 

co-Chair of Environmental Sensor Networks session of the American Geophysical Union Conference 2004
co-Chair of Emerging Technology for Environmental Sensor Networks conference at the AGU 2005
co-Chair of AGU/ESSI conference Advances in Environmental Sensor Networks 2006

Chair of AGU/ESSI conference Environmental Sensor Networks: Theory and Applications 2007
Chair of AGU/ESSI conference Environmental Sensor Networks: Real World Examples 2008

Commissioning Editor for Computers and the Humanities Journal
Founder of the Virtual Library for History of Art
co-founder of the EVA conference

Member of the Archimedes Palimpsest advisory team which looked into new image processing possibilities to read the manuscript. There is a Google Video explaining this project.

Consultancy & Advice

Environmental Sensor Networks. Image processing, imaging, computer applications for museums.
I have worked with people in places as wide ranging as Imaging manuscripts and art, Wind Tunnels, book publishers to Historical Archives.

Appearances in the media

Visual telephones for the deaf, Anglia Television News, 1986.
A sketch videophone, HTV "Video and Chips" 1987.
Computer generated sketches, BBC "Big Top Science", July 1987.
Success on SUN, SUN Microsystems training video, 1993.
BBC Tomorrow's World cover of the VASARI scanner, 1994.
BBC programme on image processing Holbein's Ambassadors. 1996.
British Satellite News cover of ACOHIR project 1999.
Canadian news (CBS) - live interview about climate change/Glacsweb 2006
BBC News at 10 - Glacsweb 2006

BBC Digital Planet, Semantic and content-based image retrieval, 2007

BBC Digital Planet, Sensors, 2010

Guardian, "Scientists develop sensor to predict freak weather, from flash flooding to landslides", 2010

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" 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Demonstrating at the Royal Institution in London, 2000.

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SG/DbPyfKnK5DJkRQcgWkND11OpCYhTmdDJ4wjIBGqKNkzJDvLrwx8TmCa54Y6HZPBFMMlhNAJlDRjvhbHd+fnV33zwE4W+DVqEBNOwBFJF3JbxYyWNFynCu9JmWISHo7QRoxa2YyDwKB1O92brtSqGf/yP/8nDz3/1F//tT+HCaTFsqmY63MPkwmrz7MkZ6Huc0QzXooIgVWoRea986p8+Xrk81WD/YRnXm9WgLMHf/j9//j/++J//s5a8Vdlg5hUWNLoJfOj+rHed1b08yS82kBzzw+sbpVd0REU+xHfhheWtlVgblROm5HspFCFLo/Fx/S+4Vehd8EUkPnBE4uLcVprDUrenDg34LgrJ+TDw/saIv7iBXkWNqPB7mlN/nPHQAKq6E3bbED0EaDjLFVCkL8Nu96aqeRgwuxfgNCTk18J/6FOYVnk+UPLDUZ3itcEortfVqDBPHn/xy5/8NTbBg9GyjNSmxA1n/7Hei+h1E1S68qENqjG8zY5UFtfws2S4Pz2/mb/21ju/8/7v3bn3yo9/8sHq7Clo5iwr7736Ktweasf10uRTTjsJAMv6oF6xVBFEHm9YUxkX2+3RacLeVqwFBDbEONxLIiwoP4mHuGuJpHNL8cSCpYJNZFlJPNAtpDe3x7XJxtt42KTi1ote2bD8YoRoqQ2AqTt9hczTestRF0GRGzNbsAVFYpDuvEZTvo01kw3QLn/SCF4vqTL5yqkOIKH4bNlQFPO4n3AtllDJi8gyOcpuu9Cc6AKOFCusA6x+r9vNpq7G5RDLmbOZmQxA1jRDtmHfeDFwolNnEic5HEdpTM1Up9iIQy9E8BbhftJ0tL8PIcdb737jerlRWfF73/3e//yzP1Prq1deezAeTX/xi0+qxY0qBu1qNZ/PT09PHv7qVxZzIiEwYMHFFrIOKbi7Blnk63bv6GhZrdeL2ff/5J9ylkcCKHEAJMHjokx6xrMt9NauuwiJJFbGbypytyaC5tMhNWVCM7pIpDwgnJLSUKzkvMqJ5hvKAdDbaQLRW3KFREZTGw17hrRAQQdLjMmzd7gCnUaEcuB2iuLsiMzRek7ULCHR6UygiOYvjxEBzhdEUg3qs/D1CpOgCq3qjXd+cFm6TbWOTxVuVoehGzpVJZbSqrbiFxwcHOiTE7u8oVvGKV8U1RjqlEb3gXhDwqQKFx16R6MKNZ177B7S4KqPxuOsmB7cuv3oyTks9b3X33jl/huP6o8PD44f/fohCBN8AwhZNstqtVrk+cvgcVi5qPa9RopDASykwMa0g0EBkgyu52tfefDd7/4+6nmT9C5O8E6kZiybRHkp01G9PHicJFu0kSGMIlBu0E+gezBZHxpcRRPEZZzYfinlmywknSZusY0eov/EN4qvoEPiVEWWmn+3nFMgk0VlXqsD3csg78lIZAXQrCU9hF2KiTi3XPIoEi+okIRNAkAi1snD4RAnlgZ6TCkAJ9EsPBHhhBgmUHU5hmap4+Pjl19++eGHVyxY5DI51luM+SBGetl6/1OUdqK0jf6Z54Ur8qq2SWmwKdKY13/rt/bzwWq+/OLJF8NimCUGZ5LnOaeSR6PResPRkOOOWRYsrSkkpGYp8MwuF3Nl0n/w/b9/+87Jo9Wmo4DcRBlqyZ6LgmdlkCGhPhHXb6OWVYROScOgQNR82IPXM/HFYbVYIvk4Xv8yL8TbEzCqDrOlY7vJdyuAPtFM7BDHlRzxsVAM0n6iicCi4IJ7e3vycS7kHXDTlW8b0QGFBlI1mUxAYeHkQMPrQHoapxCh5QSbpJow0ALXAktRSO0zLItBkfvKK8kDZjXBMQkFWq+1MeIzi9kcLpINMnDiHz58eDwq3//Wdx7+8C/Kw71qcQ1/KrMS5NkizEuPisGqniP6ncJRWiN/iMF5AccYybHaFswU3NLh3n6WpGAEz8/PxwcTEN9RkZ3euffzH/34yUcfGYV9f6g/YEerdTIqQShBsJYLzPuXwyEo/PVsCXdelgNYglRjtIvLqtWmqV/5ymuvf/UrT68uGjNsuv6ga0JQIQ4pKnFIhQeX3tJokAT5Kb1PFmUUxdHhXe8C+spEdRIWBbPNwWcCGmBd9Y2jXpsxuqjloirczFZyXCyv1HZYkjjdIF9KlFbvuSvF/Q0OxxxXdVWJ2e0kZsTX9VUsRSGz718SzSlyrUNhUk6MjgY72ahrwEUJNxsl9WUR0WoRUYXJM9g6OxpmRTGYjMf3789//RnoMwuSt5jDBw6yIVziZnEzLDMkUW4RNIXRQEDfKpkei52PSUb4J3hyNABbhwxj4HEiIwz4cp6jorE0M8wqj3Fb4cxpLNOCR7mZzxATmJdEIo+otKbewLGYHOxdzRdg4N9482tYJczyIoPXJKJ15MTPZjMVzegSySjKIpYDecSC4iKVBubTRS0JO8suv8s6t4EATR58fcaTyaRSqRwn203PYrZEmHZ8QZm3KFeOf3fbyhh+gg/ughDH108t8cxioG+Dr0CwJsx3qQBSYF8Xe4gxneyU70zSTDaEPAiK/8m5PgwqCd1ltd1gHNE1G198TPPs6Padv/vdP/jv//7XHfrtCZwL17mq2YCUZQkNqceCZceellFc+se0rm19BKJI9JHGOEtrHH3SDPIMVO3T66tuucrKAl58794rkzE2CRaDkoBlHbjzJ8e3ZvNrhZPDa8WxRYusRqPBpDGI48wHg4urs717r/ydd9/tshSWAAK9GD0nAaAcdBecd++e1+D8prAEKS5j5hKPsJMJpUqof9j90rvYeTnAonVEqnAjbRf/U4QVvjVnL40nC+XCrmMT6aLWU36j+Dw710+2h0n7U7HdayMvhpVBLkXxv8OxQadeiNfEb5BIIVZLsczG7qEc5Vjn9ecA/9d0bb2uQAM1IBGgvyZF+ZW33/n0m+9//snPq6dfqBJHBlaLNSzYeFi29Ub5tmOqDzDWz/EUJDK86GhjYE+prESDNV3ObVPjuI66HpTl3ddeUc0a4RBtM5/PuksL+g10SrVqT09P33vvvRapmurlcr2aLcHBX8xnF5dX0/0pOAUXNws1HL39u988Or07r5oaTluGnclJYPGXMHk8HqvtUE4WRJSNuCNi+2I1IAIkixxfR+RGZNdEyAXRguKTcV7KRVPfOSukbX8bsk1yTnQUJLoI277zkNeIveL3Whqsp8NUMPEjUWO1Xc2cEngDWFxBNYTzizJC5FhNCT8hG8FkpWWMg5GMuR9XqUJNwLqeOWlY5sjUV9Vtg/77zXLZdHacJN/7oz/6wf9OP/jhpVrOq8EoyXF4Dugt1flWbkeZJYqe6YIYQYX2hA1IYbvYrG4W8+n0eL3ZzM8v9LBIjZ7sjZeXJeins88/xcCGqXtxcAWYiQE6R6o6e/YMdNPBwdFgMHzt6Gg+m33y8w87sGZZUl8+u/P1d7/xnd/vknRZW2avclE6RnYoDrJ0RKjPiWX2umKlIg3Aogz4Ie7wjmC9UIx0BJ9SwTR7JzoC65meyUyzDyce/Y4ulNswYXTK84MO4n8KNMPfjzHD4UCFWdeirlBjxUpYHDdGSYt+6iJ0kQsWugfwR0B4UVr+EHRNjcNLXF6kKgOFlbSb9XI1g7D85Oj4G9/6ttLtB3/5Q3VxgfmQorB1S8Uz45DqiA4HQXGZPhZnkg8GYALX63K1Wc/Xq6dPn6hWf3lx9euzLwYH03w83N8bgS91cflMWayoJkWmnRmMRrYzm1W9nC2OXz56+MWv65ubNUQALrlz587BdO/w9imWaGoUwTffevule/eeLXFKU5ogEJLjLxvYixkGLqyyKkoTmDD0hp0bXhPR/fFSy9bGDH3xC+J0QOzBxDz78eeyBykbLFfmqHPnI5wg6KPucP5d2JRFz/G7JHK0IWOAgptiZsFto274lVi0l+vGEocQAGIyaUOLpphqBi0xz3FHVObMaYuzl7B3kNksnOfj2lSIicwKi83VOGOyRf5P9+XTZ4fHx+9959twpn724x+3yyXEUZnGUhLCANGLxLAdThDEriAZigIirBsX2TBP4Sds1GwxX1wtLq9uNnmS7Y0Pbt86OpwORuUr9++385vF5UWzqdZg7RCyMrAddkC8ffrW2fnZxfkVfE+13nz56NHwwfC111776JOPl5vVg99978FXvnaz3EDEW+EhxvozWxPRWHzSdpQWLytLnlQ2ZCfgBRCQikMje6nDKN4dV1pEdkevoMBlqYsOMM/k4F94/22QBsvo3iBYrJPkT6yZ5M7djg8T5U5dZL5jHYwCHc1fFXH0GkvAtbwcNvRG8hmV/kwRLIz8sYSccM0QfhfnFTwQfjeOAdCe9ELjrDnwS7pNvdFpAwsAlguLPKs5fOXhdP/tb3xzPJl+9ouPLx6frRZzCG+IgUg7HFeWFePx/snJ0dERaIizszPMedYt6K1yhB1woJTBPO7vTZbazVebq6fn88vZdDT97a+/e/306fnjR5vl8vGXX9aLdZon9XoDyhI+otlswESm5bBLMohqnl4903maj8fLzfrdb753fHr6+OnZ6PDI1W1tNzmxdux0qsCCsKCI6UlCgy4WPTQWNGJ7JKZNAmrZOceYMCzobcF322hyBF9A05pC0Kkj+RZFwvcjZTtDcxI5aMCPs44ohYlSmjwV6hwnCC/pCNoxfKaLOCl29CWCADRzFGOOXuOI7rTmhmyHo/MSJCryIA6aB4kYMeTNYnnim+OEBA8mB60G2qitmwwnMqjJ3iRBG1HBCpTDEj6a0jedAR8nodlJFgFq4Svj5MBNjRAa1bTw5UbDobLNIJmunZ3drJYblx/defV39o9fny1nN6FmR6qeDbSzcKhNli46VJUQdk0Pj+x6OZ/d7E/GswUm8WHZRllWrVu7aW+qVV1vulal01tJWh7qdD2/Xq+ov7TdPDn74vbJCaiudj0b7B9uag2ytvji4Vffeuv3//AP7z14AAsEZrrDTJpdVcuiPKRuC2R/EaWlsF9vJhkp/hObIcyU1PWaUh4pERiz6QTdye6ijWaOKhqvQZtMxIC2z0RwP5cVpUL1QHjlYr2OG1CJDQD1TVN5TodEU/IJ+xIaRflnpLwjLk4McRLDQlxtVg6LYzzxg7AlLHBRsa7zCBz81pPp3mK1hFOQZp7zsepsjt4NIS+a1jaOONW8+KaxZjahcwN+LqibwAbkv/OJeD8PmGH7OIsrxESiKo1hT1YTlbBO88LkLs1xgA410cP3reGWaxxJCO9PXFakA2PKoSlGyXiSUHNDR50UWL9xNnGEmtmgiRzvH5Z5BqesGI1vl6OnX365Pxyu5gtYIZD+9aq6uZrlgwHG3mnWYW7UDrOknAzGOBkaxGsBoV45HAzGo3nT4KzoplUHx4d3Tl5+9ZWDWydgydCe6y4fZKMceeIbZwiAqMWIiPPRa50wVQ/OMXF1av7+2LYbze0VofQPIfzonZrel/LLTg0sbEMcUX5qKhmkgaAf/0QJCMFuOx4N5z/RGT9T0nHTCvoXWBhTrSVUhd5KaFkMtAlE4Izr+6rx92cX5xXN8sR4RmHuF64GEVmaZ7IgluHeTGvAWGZZIPFGQapSorJgGIxoSMlY7Fhf8eX5ap1v/MXeBEyT4uphDRnWn+aBIVcsEaFh8cxkBUSOdmrrzQTsFYP+QUOCaQX3n3RqN5yW6/VmfbOaHOzBe19/7dW7p3f/7//684z6U4rhcHJwaM0NCM1wOh3RhKO6qepq4MBZwvp23VVreEJhQ85k3bnRwf5kuj+YTF+9/8bdV169ms1HkwncLUm8SYkGF65NRIw9XbnIADvvsQuB+r7DNASbGFklQ8Gtjn3nrvemqZfCsS7zKHXK6HpHNuEPlblUKOZcBJcMCCe22DXhsmMXw7+iQZjxzzZi5JatJ0NGE39sn7jqyIFG8ims0RH+gDFhOEUUW+dV5Pu7QHflR/fq58peXLzkUIi5kWQp45MXBChiPAtpGBOGATHVYEJlTUek6lYlw2EBH5inlCm2OEmWPb7p/h7JPLInghzUVQWWBWnpujYdTtvZ2fziRuXp6Z27IEknL9198uknIAoZkQPAGZpMpyBYYJinh/uoRarBZrNoqxVOfNaqyIsWXLTp9E5RglOyd3QMkjMcTVGA2ctB/DHWA9g72bRdOZ7okPrTUa6OoWmWcHCCKWW9rshnihOSkvuJs1Zyhvm5juY5KuV5dZiWSPu0kAp/hRUrRVP6klEXxQRdb2TJl9BOb2lKFwWGO8+EG3MsWHKfHTfI0NyalFCfcPRk38E76voopXfQU9FAotD4nDGXpqVM4K1bt+D5y8vL1WbpAj+9F+fQxNhFZDUuDlZBRBi9bRwvd0IlEYPmgowyQlAxpYBVLaOLEjNVzPNcb9YVqKkVappqvfzaW+9cv3R5c3U9u7z88ONfgdN4eTMDm6hpLy9nV6u6K0fDfFBePjs3OIvGmaIY5SB1E/DTWPk11RoB2pfXF4vVojnLy8Hd0Xg4KGdPz0EGBxDuEWiECPIQwsPiIMG5bzQIQT4/4iC/aivq+sFsLrffBGVEGgFnfPrpip5h2jP2MIeFt5GksdBDYivle7ZIKcSRu6PI3UQJVQZUEBqWjkFgaBIxsgHxF4eiO5qCDY8N86NpSLqqNnWSebRMa/uECP4k+9IFUkp2JVOuMemQN+MjGNcKBX3V+xmdD19F9sX26TCYgJ+nWAlhefj6RGWJ4WvBC8DdoZIpth6DfGU5mu2UYAi+O9bYzIKbprMWBQ/W9uUHD1629y/PL24urz7/9LNffPa5m8+Gg6QBV73aYHIiK/PhYDSdTJHnhghjW5yNC8+BMBtywS2NCs8m+7gizsyXi7Orq19+8djobLhcFeXSZA3etPK0BdwHYaNiA68GD7cSFR7vkDxsfPZUH/R5y8B/NlsVi/jc0877Bht8YZpRh5yMn6EymvaN472fFPlbYkZiNSZb9vw902dbESx/MwjQp3ooBxgommlGrfe4FIHZv4sGtKAUdQFUKp/HkfOIUkcQPYEtu7i4sMRQYFJmMVcqQk+z2LIrpqN2TbpOhn4UvotaPentjqLPjscoEBMqvDDLMaeepHkXGCTwSJZtNhjZMcIoZpdXSPiRZlaZWyenx6d3nj59ulncPPn1L5rGpeVg/+A4A7d9PNB5enT7BHxdmlOygu+82NRmjfXHpqlObh+C7lo2TY7DxtJ8PC3yQTqEKNKBv5WPBtj0YUEobQ4qAgfXJdZ5t11HmUDOggo2nEuBZDiYuolhbMz6SFpEGxXSPZqgTtYnHrmGSPEBvqILPInMj8KhOpFs0xqillIBvc6tGGS/cojckQmD3FZCeCONutqqxako2pB/9iLF50SpAGXyzzr6GqyASHGiTslT5NKoqcDVRbAwHYpUKQ9jEjSPCBaIlAz64b59eH5YjtDZV3rHkdoRLH9pDr9ruKbBtDk6nFwewTsZDsewEW1Vu8YxtxuaY2IOAZnAnGxDMg/7kRZpYsEfurh4plGFYHH33v3XX//qm1nqHn/x8fzmshgMweuaL6vp/sF8sS6L4SDPx5MJz5JBLCLaQbDJELshf/PYYHbt4gaHk90+He4dHo2Gk9PTl6bT6QrClVWV5Ngv75KUB5WI96lCupIXVGIaOVE8SHIXTuBc3NrcaxEev8GqKZxY60sOpt/wvicHtEYjfjDpfxM+d3eeYCw3O8pJBZ8sFqzOdzxsKWCKLDCkTIMJbpA2xOtUZOwNxGti2fhPqJni0IDrrMzQyt46z7pghTybYfLGUccwY/34Ab8zC+8Ox5el2S/sobO/xdhUNDE4A9cp6tdL+GDSvMwsL4nTCLvj8JB3xhqEooMVPbp9Cqd/srdPy9GBmGy69atffRMHIVIHUDZKV02TDUqQd5AJzMCEg4R4EpoebHIDTl85xu05Pn0pxa7SjNkhq6a9ns3TvBiMR3AWHMlKXpZNQKqISLkoTBGXgJ8pixxp1jknyZ2iNX7rIpRiUjCKuediQXuNfYqoTHFJ64rymbijsA7U1orjPvOcxsRxJ1bbBe9Os69lMHflWhosqki7MkaOfGqd43VcLF47giUyFPLvjrDtvm8RfEP4HW4MocYI6yMzR90uiaYCaBAeUdveGe39PnpKmC04oGOqLh1gKsoQYabaKkzy74yJVhE4nz1WMEa2GI4GA4M2xed7UKKTHghA+5Npmm+TZTmz81CbKHqB9NVxZGZnkQaCb4aRidYNrcNhmWlagmpNLCk/mn3BSFNOKhvvUmiefYHtPprSCNiIilNX6N4QSQoi3lGiB5OVSNdCRRIxE/Tgf8bOe7+AxlXVmkyhEpBxUhDDMUU8ft3CBZE1uK01rAVNls78JHa8FJxtxAUNBzR8kEwHvqd1lkk6+6jTsKyQHJtAYyQP8Xdli8VXjn/KhnI+30blRXba4HjYlKZi45Z5kjCcMI9U2Fs5eh2gz1uQVqmWM0CU0ZJ8TCnUoQ/TW9qVH902sZHXrkwoDVatSWiCDU2Kso6HjngTYTRBF6jXHYGX5JuE6yTcpWXcEq+fYccFMdwQ44HvNNUuS8jbTDsi6UKktuaaUsLuoPVpQ4etjjmIjJEmNmrDtRjA4T04nMqSWOohJe/IJs8xSHk/KQSJYq/Y6YTvimETfrMOFKUitkdwRyzx86QEn2/wwi3fAeYpmIbXesYORY3zgyFWPsrhEPHj9Cng9nW0dFr3JNtKMUcv9qH08yBVn8vt1K5TpaOM6I5U4aOl/AXVfFtjWXpIEsAAejWEcGT4IhRG8IgDx1Y1/FTcpSMfY0PqQsJAycIx3rm1DVFfhuRbpEilmB9fDf4PFsYoHdewGCDAs1mc1yWM6zIUZ1OjsIuxGQjFYq6B1jD6GbaPUh54RnMEDGLXfOs3mBgMsFJGspJSLcyfafj/zNPk4hIwhI/6z7KCkiCM6+WzQZV0OeKyK+Ge+00SwULFW5ZtV7O5FGoXFutYvYnbRHvvPRCLjY+ekHI4ntAW+qZOkgsWRSUhRC9Y6Kt75wxVgOuLiVk52DGFYk/irX/+NXT/tO81gVFZCpMewsW0lNi0qLfq5fwz5XyVXJHvDH7heYU66iuCdW+Iqsjp3RhbFEwXtR/h24M25peFQJLyH9T14Q8WIRkwptFYk5N5134j6WdRog+XtLRnne8rhwvWbUPsp3RikYkDx4lhXq1FogdKmHk/yFGyCJRJKpqVhp9T4tHnogw2N6TIBq1wmIEOesgLR2TlZSfEavgFTakJt/OoNUt8VyRGCRXR6BBqX/MxOLNuQ2YQPQ3Q2eiKkEelSLdVfgQpL19QSNR6rmk2KH0qbx+6c5b9Pz/uBu843qOdk++ipIN8o4TCqJa2vSPWeyRWIfodxZQEcta6sE0R75KIairEXOwPyEeyiuIGRR1CxVIX+MxzQY8KCYt40fk4N6uK6xvSiAe3Sikr4hlXtA3oOvDNJRxRiTKgrBmnGbnav41UQeb4jrQrOrewjHDYiwxiYTOfXeM2B24y/s6pMu269u+mOJTmoybMB6S5boPOGPcP47OxxopPpHN99lnOgArJTPkKmntAAyLZBgRLEoYxDYfDrAQVjsIEX7SlfgzkpmJzFu6VVgCtU6ZlrGmvXmJ9Y8LwEjSaloBMuj/bKsLp68jHknuToNCFji4OfpOALA37Qh+HF9+a8NBrLOlGSgP9A7+Hxy52gSba/4m/pzbxN3EBCKbD6IB+GyC6wR7j8DVC4En9M6xyaHGwVMD0Oip15FY7/m7+iyFhF5VQ+ARapm/BzuwM3F6SL4vkMfB2yu7DJ8GGue0HkRPqAQS5XKXV7BAgBIQhsQzFQ66OpGPmKkSCdK34WLEhU9tgbiPEL4pCQvQCNY9bogWgK7vOdNymR44Q4g4UROUQmDI6Cr4rcxTgRlLPmWKKJVo5HlxMKykS1ZtCERrOb8HiUGNJVeQj4TjdFvdd7eUVCtscdoWjM6wIWIxLhc6GEyCMVi+QKvhJidCABzLcHo895cikAOs6TNIkw80Cx7HeVPDN87JwfmIhJ/E6XrUK6Y0Mpjoxek8Z6ge3Mj0ewxvX6zUCGql+jzw/SQ6aMg3DzKnHi2urmBk11D9I+hmte92AiCsG0JFG9BAxNgsZho6KZLej/jXQiNgctrc34Y0ndweNLbZuKDcB36UjYjEOfxqGdWHNi3POCc0YZmHRHIsFwYqPsg6dS6J+yGwrnr4Y5quEUTGBiKHzsxd4ax0F8zhWfbOp+Mh11DOXFqmmfj1LZ0sEArcFNbemFKzuu3qJ0YQvivEyUpJVi5ub+XJ5fPsuCZZWW8Pf4Bhn/LtjViuK7YzzWX6n+vycoeYVWl5PVwarpjvf2MgzVoh0yP/k3osUBYWwOPCVatjFtsGUiu0GRYkNOLitK0TbKIf9Hzh6ijp2HEHfSesUxSDPU2y7onfCGiHYhPa4a+qEhhzh6c8Jmk2gepwOXaS88TjDjipriAVsbEYFKcVN3DzK20IwqLOi1D66Tk1quWdPceqD2nWp8I78EdzajwMYsQ1Wd6Sw4RfkbE2RDywHq55nELd3NVWmKZOSEr+UHGL8LEwzdcxKLTpbzJyNaBS50QozdlnWbmrPexiOb4YFkDR2aCJDRmPk2HHCPAkGIETZA/8LlFIshc639OQFDyGnu/PmAn2Ftq7GwxJWfbNaZhiO2tnFk7/52Yff/oPvIZMZts2jC5dQdyhsCewzBsGohtD4orxiqI1NkKA+kKiKrDJiT5iJVhn2sDhmANmCtxZl1mLnpvGtEggMwek98ClpmPsHwkbHLjEIP+csvA1Z05AANrYjpc1LllBXJpdj08PDMWdWZawUUttlyXqxdFgSZCIL0uf4T0wis96JHBTUEfFcPx1BoNooS8mfTkUFPcwz9gXZaCIWYzwaEokF76KlbANdihr6FXLC6GBkDQX8OdcobS9VrJ2c7hPmYizELAqrBad8NWX+CD22i73UNNRJjGYIYjCqVU3nIkSW8nkjTCjIBwdwFeY8kQnKpMQ2wLujiPtdcSIUF7/twJeEyx/u771672UwD9YbU9p9TNJ11AjMK0mhi2Ziko5nxodV6N0yprdmIi/xfcPxsJyPI91JkSYtXj+BU4dWE0WeuEBjXUjU8i9M4iMP9tn5xbFghTdiSz6ZyERONi/u3t4ebANzmHTR3KzJZMLJMxf6uHXUGuAi/AUrjBiRItk42WkdmKViX1VFrAeEXsxB2SwWi9jn8L/wOkbuqjxYsFoaLcaiibWDqh68cLxK+CnLqANAEsu426N+4xfsuokY92LzZY5l8n50SELJlQ5nBZCadQlo5b2DwzcGo85knbArh3sQp3DnzNAtbU16ir+FXCE+Y+q5Adj8grRvpHxuOaIvb+SAshMtWWXeuXhd5Diy12mblq2bEBzyt+K0vjwp28nqhzM3nBg0YciR3LocA0dBRhIoUCSEgefBd5R0iUgqGVem4/IX5NZhE7G7xIJlg66IV02H6E90bfz11W948ExocftYuTo8ObkO+cwudFapqIq3c83U4MBfcMEQDN7WbefCaic1eijg7iPjDfguWV7sDcazdZXorVy8CHr8EVY6V9VWb6q8K3INt2RRuPHi1XM8J1GpFwidPG8iboKOJ2lFrZIc2cKf4MTLM33uTrsKBdfxFWTWHMjB5eWlFHFld3nDOLRUgciLb4O5EuTWxT7mdDXWT5wiEUEX3aYCni6h8+UnzYdVZgnjkqiIOP/CrQFuO3PNb4RwJAmkKVIh9Rkvenl8t/In2T//Kfh8Fp8reXQRvk2F462pLQrNOaaYLFFhI3wx8TNE0IvCGeRYlO0wY+eonUub+KvFX3BHIGRt/5YTsvOQ7xJrQRQspnB4Xg+3EbmqKC0bsEHyFkmHqhCX6gjgAJ8bZzFUZBHARIpgxeeeZ2zEqxCfMHmZnIQ8L8QginVTBPhJA8+CCtkdBEAiHXcP+OH77Kh5RD1neshXNTuCxQ9m3k4D2Y6lHmIIUCiI2zK7/DvX7G2UPWYfiwR3q9gS7324jbC86FApmpPuEIxkEESUInkeQswxRCGAko9kW2wTHozGarsmKAIh2ZP4q8WfK2eP95SWIq46eESGjepdIqOp237EF+oCdUyMMmXZYgPahuk/8Pt4PBYFI34x3H9mfLUtRP6+qxhpPCLgvI1azZKon92RQZRPiVdHDOUL0wHy+47c2yjzFAt6LKzy4PFaLhJxOWNdaEnthGeRslqUxOoBNvKTB2jJR/vDzY6E9jpJR+nlpJ9Z1H8peEfOFGoUiMHn1C1lU1cWZ7Oh56GwV0V1SVD/VLbsxzzLKsVXjm/VhfRVLFu9oG9lrZT3EIMLFL84jac3xVpHfN7YVdc0VIjL0jaaBqvINMRaLbwRE5eMxBWh5tdfX1/zP4UgT0f0c9qD5fHB5TO4vovsgoCDs7RvyOQjyK4bJh4DQqg3PUGIRfHIVxZN7EWKNzgaCs+vl8+FdRD9rUNFCx1QaqZSzz3k68eKgfIFmms1JvRd8UenYRJdbNnhD3VHaQKVgEVsaosEFAtMhd5/8Dq/uG6RIyMtQVc7Fbho41MX31J8h16hOPv8n2TRPE16VMvekUJRWlgrZJORBnZA1jfsbHIYz7Eeu1NcIuW/SiDmgqPdRhM1VMizGxyV40dkybQjEVYVzcNlMeJQi9uEZDbQ/v4+Q274xSxzKBAbTIeyuyPxRBKoCeX2xBxj1IlkTxWzoWx7hFoEkX924T51IDrzbQLUMGgD0ZwLSGIqEXGyqJ/oJFfmK4h0Ok5NYoqyPy0mNB9IUJWGXnvN+gQZzDAb6IxnAmfKPhHBhPqCUPHTGMSqaXXk/YjKZ1RLbJpFvMRMpTSsgHHJ4yEhpjovZPTtcHlhLVm5spAwvA/puIWmto0mILAvLBpIBSeX3yO3yEvsJTRNZSMj5Ym1KqSXHAy4apGETqmyLEW6bVTJku/GAiSbyiStibBlBr1S4LgD/y4JuMR7cwKR8EVx3/YkBIod0WayXYsVqt97TePg6GsykpGvLEkpHaj0WCITEiytt7wT0TfRsvRefPJceoIfbBZiW6HJxzL8NTEkxNxDXg7386GccExiIQ8PEuJZrjKp3m8TqRIFIxp6R42F5eLIKYbfRV8sqHD5glrgVUJuG6sZOdxsp2RF5AuryGLKd06ZUiv6ArwHier7YOXisnCiUsU/Y4izC8R24rSpaECDjth/5MpigmOdLDfp71NhAjY1/dCibhvUpsKcHH/EzRY7chKRpmAuNETBvKCWiOgz7GZrRaBVKEtLQk7FCCW85gvlSqnIpdO9IwhhvOq4SQsT6tzkhAeA9wWiQ0fVQ3Kqic8MdVufqdmRp1iwXMAQyLp5wSI1FRa83zJWOgmxCsjJFPXkG1ZFPnRw20V7mSgfwbGPfs4zdSHoELfM+UkK9YjwQJxJUmF4emxx5O3xNV1IurKeg/eyjuEcbLz6XWBGlAuKGxS/zK+f5ZqiKHOUAIkcRVh7j1ChL9VGmSexnjyVXqntqbuOslPKhlJmT8m3cyf+435zVM9VVDlFXWDTyBnpgNRBWKhBxCCpUbAJKL6wBQh/gxPUUVMh6a/ndNKOPO387GWahszz+9uQM5A98q9RfeOgDs1asGv/H7ataSdgKwFmAAAAAElFTkSuQmCC" name="Image2" align="bottom" width="200" height="204" border="0">

working on the glacier in Norway 2004.