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VISART IV
Paper submission is now open - please use this site to submit your paper! via Pocket
analysis  art  arthistory  cfp  conference  germany  images  ml  recognition 
7 weeks ago by kintopp
Content Based Image Recognition for Early Modern Images
Archv is a tool to search an imageset to find the best matches with your seed image. This page allows you to upload and compare an image of your choice with either the English Broadside Ballad Archive imageset or a curated image set from the British Library's Flickr. via Pocket
analysis  history  images  recognition  tools 
12 weeks ago by kintopp
HIMANIS Project
Manuscripts are among the most important witnesses to our European shared cultural heritage. Despite a large digitization, the wealth of their content remains largely inaccessible : current handwritten text recognition technology is not accurate enough to allow full text search. via Pocket
ml  ocr  recognition  text 
may 2019 by kintopp
ArtMiner
Our goal in this paper is to discover near duplicate patterns in large collections of artworks. This is harder than standard instance mining due to differences in the artistic media (oil, pastel, drawing, etc), and imperfections inherent in the copying process. via Pocket
art  arthistory  iconography  images  recognition  search  deep 
march 2019 by kintopp
DATeCH International Conference 2019 - Call for Papers - IMPACT Centre of Competence
The International DATeCH (Digital Access to Textual Cultural Heritage) conference brings together researchers and practitioners seeking innovative approaches for the creation, transformation and exploitation of historical documents in digital form. via Pocket
analysis  belgium  cfp  culture  handwriting  nlp  ocr  recognition  text 
november 2018 by kintopp
Archetype
Helping straight from the outset, Archetype’s batch uploading allows you to bring multiple digital images into your repository in a single pass, avoiding repetitive and error-prone manual uploads. via Pocket
annotation  classification  handwriting  images  manuscripts  recognition  text  tools  ml 
november 2018 by kintopp
DCGAN for Archaeologists – Electric Archaeology
Melvin Wevers has been using neural networks to understand visual patterns in the evolution of newspaper advertisements in Holland. He and his team developed a tool for visually searching the newspaper corpus. via Pocket
images  newspapers  recognition  deep 
november 2018 by kintopp
Weakly Supervised Object Detection in Artworks
We propose a method for the weakly supervised detection of objects in paintings. At training time, only image-level annotations are needed. This enables one to learn new classes on-the-fly from globally annotated databases, avoiding the tedious task of manually marking objects. We also introduce a new database, IconArt, on which we perform detection experiments on classes that could not be learned on photographs, such as Jesus Child or Saint Sebastian. To the best of our knowledge, these are the first experiments dealing with the automatic (and in our case weakly supervised) detection of iconographic elements in paintings.
art  iconography  images  paper  recognition  arthistory  ml 
november 2018 by kintopp
Katherine McDonough | Historian
I am a historian of France working primarily on the eighteenth century. I write periodically here about my projects, digital humanities, higher ed, archives, and radio/podcasts. via Pocket
analysis  france  geo  nlp  recognition  space  text  infrastructure 
november 2018 by kintopp
+ Transkribus recognises early modern German correspondence – READ Project
The Gender History research group at the University of Jena (Thuringia, Germany) have been experimenting with Transkribus as part of a digital edition project on the correspondence of the eighteenth-century regent, Erdmuthe Benigna von Reuß-Ebersdorf (1670-1732). via Pocket
germany  history  letters  tools  recognition  report  text 
july 2018 by kintopp
VISART IV
Where Computer Vision Meets Art
4th Workshop on Computer Vision for Art Analysis
9th September 2018, Munich, Germany
analysis  art  arthistory  cfp  conference  deep  germany  images  recognition  ml 
july 2018 by kintopp
Mechanical Kubler: Visual Paths Through Time - Matthew Lincoln, PhD
Mechanical Kubler: Visual Paths Through Time I finally got the chance to push through a little idea about walking through visual time with a new Twitter bot I’m calling @MechaKubler. A pathway generated by Mechanical Kubler. via Pocket
art  images  recognition  analysis 
june 2018 by kintopp
One-shot object detection
This is more advanced than classification, which only tells you what the “main subject” of the image is — whereas object detection can find multiple objects, classify them, and locate where they are in the image. via Pocket
analysis  images  learn  recognition  classification  ml  deep 
june 2018 by kintopp
A gentle guide to deep learning object detection - PyImageSearch
I went through your previous blog post on deep learning object detection along with the followup tutorial for real-time deep learning object detection. Thanks for those. Ezekiel isn’t the only reader with those questions. via Pocket
howto  images  neural  python  recognition 
may 2018 by kintopp
arsexplorer.martinnadal.eu
A machine learning powered experiment that finds 'connections' between artworks from the Ars Electronica Archive.
art  demos  images  recognition  visualization  ml 
may 2018 by kintopp
Where Computer Vision Meets Art (Munich, 8-14 Sep 18) - ArtHist.net: Netzwerk für Kunstgeschichte / Archiv
Following the success of the previous editions of the Workshop on Computer VISion for ART Analysis held in 2012, 2014 and 2016, we present the VISART IV workshop, in conjunction with the 2018 European Conference on Computer Vision (ECCV 2018). via Pocket
analysis  art  arthistory  cfp  conference  germany  images  recognition 
may 2018 by kintopp
In Codice Ratio
In Codice Ratio is a research project that aims at developing novel methods and tools to support content analysis and knowledge discovery from large collections of historical documents. via Pocket
handwriting  manuscripts  ocr  recognition  text 
may 2018 by kintopp
Microsoft Azure Cognitive Services
Infuse your apps, websites and bots with intelligent algorithms to see, hear, speak, understand and interpret your user needs through natural methods of communication.
api  language  recognition  speech  dev  algorithm  ml  deep 
march 2018 by kintopp
Using convolutional neural nets to detect facial keypoints tutorial — Daniel Nouri's Blog
This is a hands-on tutorial on deep learning. Step by step, we'll go about building a solution for the Facial Keypoint Detection Kaggle challenge. The tutorial introduces Lasagne, a new library for building neural networks with Python and Theano. via Pocket
howto  recognition  ml 
march 2018 by kintopp
Seebibyte Project
As the project progresses we will add full details on this website about our new software releases. In advance, here is a taster of previous software demonstrations. via Pocket
images  oxford  recognition  ml 
march 2018 by kintopp
Neural networks and deep learning
The human visual system is one of the wonders of the world. Consider the following sequence of handwritten digits: Most people effortlessly recognize those digits as 504192. That ease is deceptive. via Pocket
images  learn  recognition  deep 
march 2018 by kintopp
GitHub - jfrancis71/CognitoZoo: Mathematica neural net implementations (uses Mathematica MXNet v11 Machine Learning functionality)
CognitoZoo Mathematica neural net implementations (uses Mathematica Caffe v11 Machine Learning functionality) Please see the project Wiki (https://github. via Pocket
deep  ml  models  recognition  wolfram 
june 2017 by kintopp
HIMANIS Project
Manuscripts are among the most important witnesses to our European shared cultural heritage. Despite a large digitization, the wealth of their content remains largely inaccessible : current handwritten text recognition technology is not accurate enough to allow full text search. via Pocket
ocr  recognition  text  ml 
june 2017 by kintopp
Project Principal Components - The National Museum
Two of the results in the project are described in more detail below. As a starting point in attempting to train an algorithm to analyze our images, we used a neural network trained on ImageNet, written in Caffe [1], developed by Autonomous Perception Research Lab at Berkeley [2]. via Pocket
art  images  ml  recognition  search 
june 2017 by kintopp
EyeEm - Your source for outstanding imagery
License original work from a global community of 20 million creators. Trusted by Fortune 500 brands. License the best of 80 million royalty-free, real world images. All with simple pricing, perpetual worldwide usage, and releases of file. via Pocket
ml  photography  recognition 
may 2017 by kintopp
Amazon Rekognition – Deep learning-based image analysis
Amazon Rekognition is a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. via Pocket
analysis  api  images  recognition 
april 2017 by kintopp
Vision API - Image Content Analysis  |  Google Cloud Platform
Google Cloud Vision API enables developers to understand the content of an image by encapsulating powerful machine learning models in an easy to use REST API. It quickly classifies images into thousands of categories (e.g. via Pocket
api  images  recognition 
april 2017 by kintopp
Replica | DHLAB
In the future, how can digital tools help study images of works of art? Art History aims to establish genealogical relationships between artworks. via Pocket
art  arthistory  recognition  swiss  ml 
april 2017 by kintopp
Visual Geometry Group Home Page
Retrieve objects or scenes in a movie with the ease, speed and accuracy with which Google retrieves web pages containing particular words. Retrieve shots containing particular people/actors in video using an imaged face as the query. via Pocket
art  datasets  images  oxford  recognition  search  ml  deep 
april 2017 by kintopp
Ryan Baumann - /etc - Finding Near-Matches in the Rijksmuseum with Pastec
I’ve been interested in experimenting with content-based image retrieval (CBIR) on large humanities datasets for a while, and John Resig brought Pastec to my attention in the Digital Humanities Slack. via Pocket
arthistory  recognition  tools 
march 2017 by kintopp
Introducing Similarity Search at Flickr | Yahoo Research
At Yahoo, our Computer Vision team works closely with Flickr, one of the world’s largest photo-sharing communities. The billions of photos hosted by Flickr allow us to tackle some of the most interesting real-world problems in image and video understanding. via Pocket
images  recognition  search 
march 2017 by kintopp
PHAROS: Art Research Database
Upload an image to find other similar images. via Pocket
art  database  images  recognition 
march 2017 by kintopp
Computer Vision Algorithms Detect Human Figures In Cubist Art
The human visual system has evolved to recognise people in almost any pose under a vast range of lighting conditions. via Pocket
arthistory  analysis  recognition  images  deep 
december 2016 by kintopp
GitHub - inejc/painters: Winning solution for the Painter by Numbers competition on Kaggle
This repository contains a 1st place solution for the Kaggle competition Painter by Numbers. Below is a brief description of the dataset and approaches I've used to build and validate a predictive model. via Pocket
arthistory  analysis  recognition  images  deep 
december 2016 by kintopp
KiWi - BBC R&D
In this project we have investigated the possibility of automatically assigning topics to large programme archives in a reasonable time. via Pocket
audio  recognition  sound  speech  tools  topics 
november 2016 by kintopp
+ Presentations from the READ partners now available! | READ Project
The READ project was launched in January 2016 at the ‘Technology meets Scholarship’ conference at the Hessian State Archives in Marburg (Germany).  This conference was organised by the co:op (community as opportunity – the creative archives’ and users’ network) project. via Pocket
conference  europe  infrastructure  ocr  recognition  text 
august 2016 by kintopp
My Top 9 Favorite Python Deep Learning Libraries - PyImageSearch
So you’re interested in deep learning and Convolutional Neural Networks. But where do you start? Which library do you use? There are just so many! Inside this blog post, I detail 9 of my favorite Python deep learning libraries. via Pocket
dev  python  recognition  tools  deep 
july 2016 by kintopp
PrintART
WHAT IS THE PRINTART PROJECT? via Pocket
art  recognition 
june 2016 by kintopp
printart.isr.ist.utl.pt
Paper submission is now open - please use this site to submit your paper! Following the success of the previous editions of the Workshop on Computer VISion for ART Analysis held in 2012 and 2014 , we present the VISART III workshop, in conjunction with ECCV 2016. via Pocket
art  conference  analysis  processing  recognition  images 
june 2016 by kintopp
Visual search tool for satellite imagery | FlowingData
Terrapattern is a fun prototype that lets you search satellite imagery simply by clicking on a map. For example, you can click on a tennis court, and through machine learning, the application looks for similar areas. via Pocket
images  recognition  search  ml 
june 2016 by kintopp
Getting Pastec Installed on Mac
Not that I don’t have a zillion other things to do, but what the hell. Here’s how I got Pastec.io installed on my Mac laptop. (I have several thousand images from Instagram related to the bone trade. I’m hoping that Pastec can help me find/deduce/map/elucidate the visual grammar of all this). via Pocket
recognition  tools  ml 
may 2016 by kintopp
CaptionBot - For pictures worth the thousand words
How did I do? Thank you for your feedback :) 5 stars 4 stars 3 stars 2 stars 1 star Or Powered by Microsoft Cognitive Services Privacy & Cookies | Terms of Use | Trademark | Report abuse | © Microsoft 2015 via Pocket
recognition  tools 
may 2016 by kintopp
Similarity based image search | Digitale Bibliothek der BSB
Version 1.6.8 - HHI V 2.9 [22.04.2013] - 0.85/0. via Pocket
recognition 
may 2016 by kintopp
Birdsnap: It's a Plover, it's a Turnstone, it's Killdeer! | UMD Department of Computer Science
As a part of Ben Shneiderman's course on How to do Great Research, graduate students are writing short articles on research that inspires them. via Pocket
nature  recognition 
may 2016 by kintopp
Ryan Baumann - /etc - Finding Near-Matches in the Rijksmuseum with Pastec
I’ve been interested in experimenting with content-based image retrieval (CBIR) on large humanities datasets for a while, and John Resig brought Pastec to my attention in the Digital Humanities Slack. via Pocket
arthistory  analysis  recognition  tools  images 
april 2016 by kintopp
tranScriptorium | tranScriptorium
tranScriptorium is a STREP of the Seventh Framework Programme in the ICT for Learning and Access to Cultural Resources challenge. via Pocket
handwriting  recognition  tools 
april 2016 by kintopp
How can I train a logo detector? - Online Technical Discussion Groups—Wolfram Community
(Unmark) Mark as an Answer Be respectful. Review our Community Guidelines to understand your role and responsibilities. via Pocket
recognition  mathematica  images 
december 2015 by kintopp
First international co:op convention | co:op
Technology meets Scholarship, or how Handwritten Text Recognition will Revolutionize Access to Archival Collections. via Pocket
conference  handwriting  ocr  recognition 
november 2015 by kintopp
Historical text reuse: what is it? |
What is text reuse? At its most basic level, text reuse is a form of text repetition or borrowing. Text reuse can take the form of an allusion, a paraphrase or even a verbatim quotation, and occurs when one author borrows or reuses text from an earlier or contemporary author. via Pocket
classics  recognition  text  mining 
october 2015 by kintopp
hci.iwr.uni-heidelberg.de
The search algorithm used in the "Passion Search" is designed to eliminate the flaws of the bag-of-words model and should therefore be able to work with arbitrary search requests. via Pocket
art  analysis  recognition  search  images 
july 2015 by kintopp
deepdre.am
deepdre.am run Google's Deep Dream magic on your own images Dream Mode Wave Mode Crazy Mode Whoops. That file won't work. Try a JPEG, PNG or GIF less than 12 MB. via Pocket
art  google  analysis  recognition  images  deep 
july 2015 by kintopp
VGG - Visual Search of Paintings
Elliot J. Crowley and Andrew Zisserman Overview The objective of this research is to find objects in paintings by learning classifiers from photographs on the internet. via Pocket
art  arthistory  metadata  oxford  recognition  search  images 
july 2015 by kintopp
Research Blog: Inceptionism: Going Deeper into Neural Networks
Artificial Neural Networks have spurred remarkable recent progress in image classification and speech recognition. But even though these are very useful tools based on well-known mathematical methods, we actually understand surprisingly little of why certain models work and others don’t. via Pocket
aesthetics  art  creative  methodology  recognition  deep 
june 2015 by kintopp
Math and Art History find common ground in dictionary learning | Digital Humanities
Eric E. Monson is a Research Scientist at Duke University’s Visualization & Interactive Systems Group. Elizabeth Honig is an Associate Professor in UC Berkeley’s History of Art Department. Her project, janbrueghel. via Pocket
art  arthistory  recognition  search  images  ml  deep 
june 2015 by kintopp
Science and Culture: Charting the history of Western art with math
For more than a century, researchers have used statistics to study writing style in a sort of literary forensics technique called stylometry. In 1901, physicist T. C. via Pocket
art  arthistory  analysis  recognition  images 
june 2015 by kintopp
The British Library Machine Learning Experiment - Digital scholarship blog
The British Library Big Data Experiment is an ongoing collaboration between British Library Digital Research and UCL Department of Computer Science, facilitated by UCL Centre for Digital Humanities, that enables and engages students in computer science with humanities research and digital libraries via Pocket
recognition  images  ml 
june 2015 by kintopp
John Resig - Building an Art History Database Using Computer Vision
Since the fall of 2013 I’ve had the opportunity to collaborate with the Frick Art Reference Library Photoarchive, a venerable art history research institution here in New York City. via Pocket
art  arthistory  analysis  metadata  recognition  search  images 
june 2015 by kintopp
New Jorge Luis Borges-Inspired Project Will Test Whether Robots Can Appreciate Poetry | Open Culture
Jorge Luis Borges, as any reader of his stories knows, had a lot of ideas. Some of his ideas must have seemed pretty fantastical when he wrote stories around them from the 1920s to the 1950s. via Pocket
literature  poetry  recognition  search  text  images 
june 2015 by kintopp
John Resig - Italian Art Database Proposal
The proposed database will be a collection of images and artwork metadata from members of the IDPAC consortium. The database will work similarly to Ukiyo-e.org in some key ways, namely the addition of image searching and collection analysis using computer vision techniques. via Pocket
art  arthistory  italy  metadata  recognition  search 
may 2015 by kintopp
ukiyo-e.org
このページの英語版を表示している。 日本語版を表示します。 Early Ukiyo-e (Early-Mid 1700s) Okumura Masanobu (507) Hishikawa Moronobu (230) Torii Kiyomasu II (202) Nishikawa Sukenobu (189) Torii Kiyonobu II (179) Torii Kiyomasu I (177) Nishi via Pocket
art  arthistory  analysis  japan  recognition  search  images  ml 
may 2015 by kintopp
[1505.00855] Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature
Authors: Babak Saleh, Ahmed Elgammal Abstract: In the past few years, the number of fine-art collections that are digitized and publicly available has been growing rapidly. via Pocket
art  arthistory  recognition  ml 
may 2015 by kintopp
Dennis Yurichev: 13-May-2015: (Beginners level) Analyzing unknown binary files using information entropy.
For the sake of simplification, I would say, information entropy is a measure, how tightly some piece of data can be compressed. For example, it is usually not possible to compress already compressed archive file, so it has high entropy. via Pocket
compression  howto  recognition  mathematica 
may 2015 by kintopp
Getting started with TSX - Transcribe Bentham: Transcription Desk
This page is intended to provide users with a very brief introduction to using TSX, and is by no means a comprehensive guide to all of its features. Once you are familiar with navigating the site, you should consult: You can also visit a page containing examples of Bentham's handwriting. via Pocket
crowdsourcing  recognition  tools  writing 
may 2015 by kintopp
Microsoft Project Oxford Home
Microsoft Welcome to Microsoft Project Oxford An evolving portfolio of REST APIs and SDKs enabling developers to easily add intelligent services into their solutions to leverage the power of Microsoft's natural data understanding State-of-the-art face algorithms to detect and r via Pocket
api  faces  processing  language  recognition  speech  images 
may 2015 by kintopp
Images that fool computer vision raise security concerns | Cornell Chronicle
Computers are learning to recognize objects with near-human ability. But Cornell researchers have found that computers, like humans, can be fooled by optical illusions, which raises security concerns and opens new avenues for research in computer vision. via Pocket
recognition  images  deep 
march 2015 by kintopp
Sterling Crispin
I'm using state of the art face recognition and face detection algorithms to guide an evolving system toward the production of human-like faces. This exposes the way the machine and the surveillance state view human identity and makes aspects of these invisible power structures visible. via Pocket
3d  algorithm  art  faces  recognition 
february 2015 by kintopp
Using convolutional neural nets to detect facial keypoints tutorial — Daniel Nouri's Blog
This is a hands-on tutorial on deep learning. Step by step, we'll go about building a solution for the Facial Keypoint Detection Kaggle challenge. The tutorial introduces Lasagne, a new library for building neural networks with Python and Theano. via Pocket
faces  howto  recognition  ml 
february 2015 by kintopp
Caffe | Deep Learning Framework
Caffe is a deep learning framework developed with cleanliness, readability, and speed in mind. It was created by Yangqing Jia during his PhD at UC Berkeley, and is in active development by the Berkeley Vision and Learning Center (BVLC) and by community contributors. via Pocket
recognition  tools  images  ml 
february 2015 by kintopp
Source Code for Biology and Medicine | Full text | Wndchrm - an open source utility for biological image analysis
While the source code can be easily integrated into existing or new software products, the software tool described in this paper can be used in the form of a command line utility. This allows researchers who do not have programming skills to apply image analysis to their data. via Pocket
algorithm  recognition  tools  images 
february 2015 by kintopp
Inderscience Publishers: publishers of distinguished academic, scientific and professional journals
What makes a Pollock Pollock: a machine vision approach by Lior ShamirInternational Journal of Arts and Technology (IJART), Vol. 8, No. 1, 2015 Existing subscribers: Go to Inderscience Online Journals to access the Full Text of this article. via Pocket
art  recognition  images 
february 2015 by kintopp
NEH Grant details: FACES: Faces, Art, and Computerized Evaluation Systems
To support: A Level 1 project that will test the use of facial recognition software in the context of art history, with a long-term goal of assisting in the identification of human subjects in portraiture. via Pocket
art  arthistory  faces  recognition  images 
february 2015 by kintopp
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