User:Cristinac/Day2: Difference between revisions

Revision as of 11:46, 23 January 2015

Day four

Deconstructing Harry Guttorm Guttormsgaard Asger Jorn

metadata of information; data gallery; average colour of the image, timestamp, face recognition, average colour data, what could a photo gallery mean? Gaussian blur (many image treatments begin with) derivates

http://programmingcomputervision.com/

a gradient has a magnitude and a direction (like a vector)

Search By Image - Sebastian Schmieg

Control detection contours that are detected are not continuous, but they are fragments and there is an extra step that determines what kind of fragments got together and create an extra step

Volterra Kernel Training/Identification System

http://www.sciencedirect.com/science/article/pii/S0952197612002461 statistical, not logical model of the face behind the algorithm is a manual work that is done by people repeatedly every detail of the face is annotated labour conditions

Training data to feed the classifier : no image exists in isolation False positive : images that have been selected as containing a face when they don’t

https://en.wikipedia.org/wiki/Ghostwriter

cvdazzle.com - techniques to avoid face detection

the same algorithm can be fed with any kind of statistical data; ex: banana recognition sort by face

http://www.cise.ufl.edu/~arunava/papers/cvpr09.pdf

https://en.wikipedia.org/wiki/Volterra_series

CSV-no space for metadata, no authorship information https://okfn.org/

frictionless data http://centraldedados.pt/

adding “I think” at the end of every paragraph

iPython

@@ Line 1: / Line 1: @@
-Third Day
+Day four
-Critique on the annotators of sentiment analysis/ pedophilia classifier
+Deconstructing Harry
-subjectivity is at the root of things, however it seems to be taken as matter-of-fact
+Guttorm Guttormsgaard
-marketing value
+Asger Jorn
-making things visible
-there is no date of the assessment, no way of finding out more details about the annotation process.
-not a process of annotation, but a process of evaluation
-http://cs229.stanford.edu/proj2013/ReesmanMcCann-Vehicle%20Detection.pdf
-http://cs.stanford.edu/people/karpathy/deepimagesent/
-http://cs.stanford.edu/people/karpathy/deepimagesent/devisagen_arxiv.pdf
-http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf
-http://machinelearning.wustl.edu/mlpapers/paper_files/icml2010_JiXYY10.pdf
-feedback loops: analysis while typing
-Otlet’s text; progressive at the time it came out, colonialist, racist(??-writing from memory) now
--a book being referenced a lot without previous knowledge of it, how is it influencing the current discourse
-http://eipcp.net/transversal/0106/holmes/en
-http://rybn.org/
-http://www.antidatamining.net/
-http://bureaudetudes.org/
-http://theyrule.net/
-http://theyrule.net/drupal/topics/visualization
-http://www.nanex.net/
-https://en.wikipedia.org/wiki/Louis_Bachelier
-http://littlesis.org/
-http://www.wdgann.com/about-us
-http://www.wallstreetandtech.com/trading-technology/after-the-hash-crash-worrying-about-the-next-glitch/a/d-id/1268082
-https://en.wikipedia.org/wiki/Web_Bot
-http://predict-market.biz/
-http://asuperstitiousfund.com/
-http://robinhoodcoop.org/
-http://www.corp-lab.com/tradewar/
+metadata of information; data gallery; average colour of the image, timestamp, face recognition, average colour data, what could a photo gallery mean?
+Gaussian blur (many image treatments begin with)
+derivates
-W D Gann
+http://programmingcomputervision.com/
-Words for the annotator
--mild vs inflammatory
--oriented vs disoriented
+a gradient has a magnitude and a direction (like a vector)
+Search By Image - Sebastian Schmieg
+Control detection
+contours that are detected are not continuous, but they are fragments and there is an extra step that determines what kind of fragments got together and create an extra step
+Volterra Kernel Training/Identification System
+http://www.sciencedirect.com/science/article/pii/S0952197612002461
+statistical, not logical model of the face
+behind the algorithm is a manual work that is done by people repeatedly
+every detail of the face is annotated
+labour conditions
+Training data to feed the classifier : no image exists in isolation
+False positive : images that have been selected as containing a face when they don’t
+https://en.wikipedia.org/wiki/Ghostwriter
+cvdazzle.com - techniques to avoid face detection
+the same algorithm can be fed with any kind of statistical data; ex: banana recognition
+sort by face
+http://www.cise.ufl.edu/~arunava/papers/cvpr09.pdf
+https://en.wikipedia.org/wiki/Volterra_series
+CSV-no space for metadata, no authorship information
+https://okfn.org/
+frictionless data
+http://centraldedados.pt/
+ adding “I think” at the end of every paragraph
+iPython